How product managers can create irresistible products Watch on YouTube TLDR Product psychology goes far beyond traditional product-market fit. When customers feel compelled to buy products, they move from rational comparison to emotional connection. Successful products trigger what Laurier Mandin calls “the flip” – transforming wants into psychological needs, making purchasing non-negotiable. Key topics: The psychology of “I need that” responses vs. traditional product-market fit How the “dog brain” makes purchase decisions 250x faster than rational thinking The “coveted condition” framework for emotional product connections Why products need to be 10x better to overcome status quo bias The CLIMB framework for identifying functional, emotional, transformative, and transcendent needs Integrating product development and marketing from the concept stage How craftsmanship and attention to detail create emotional value Introduction What makes a product not just desirable, but absolutely necessary in the minds of customers? In this discussion, we’re investigating the psychology of product development and marketing with Laurier Mandin. He is a product marketing strategist who has spent over three decades guiding hundreds of innovative products to market success. As founder of Graphos Product, he’s helped numerous startups and established brands through need-centric product development and compelling marketing strategies. With a deep understanding of consumer psychology and behavioral economics, he brings a unique perspective to product creation and marketing. He is also the author of  I Need That  and creator of the Product: Knowledge podcast. You’ll come away from this conversation with fresh insights and practical frameworks for creating products that customers don’t just want – but feel they absolutely need.  Beyond Product-Market Fit: Creating Products Customers Are Compelled to Buy Product managers often focus on achieving product-market fit – that sweet spot where a product satisfies a specific market need well enough to sustain itself and grow. I asked Laurier what the different is between a product that achieves product-market fit and a product that a customer is “compelled to buy.” While product-market fit focuses primarily on rational factors like features, pricing, and market size, being “compelled to buy” taps into something deeper – the psychological transformation that happens when a want becomes a need. The “Flip” From Want to Need Laurier described this transformation as “the flip” – the moment when our mind converts a desire into a psychological need. Beyond basic physiological needs, our perceived needs are mental constructs. When a product triggers this flip, owning it becomes entirely non-negotiable. Customers will overcome any friction or barrier to get it. Traditional Product-Market Fit 26284_d30497-78> Products Customers Are Compelled to Buy 26284_c0e4c2-a9> Focuses on rational factors (features, pricing) 26284_106a22-d2> Focuses on emotional triggers 26284_431383-49> Aims for customer satisfaction 26284_dc8c4a-14> Aims for “I need that” reactions 26284_a91c04-3a> Faces constant price pressure and competition 26284_4b4a0c-64> Breaks through resistance and friction 26284_e5fff0-b5> Customers compare features 26284_9bcbd5-3b> Customers imagine life with the product 26284_d7db31-47> Products that merely satisfy a need constantly battle price pressure and competition. In contrast, products that trigger an “I need that” response bypass these challenges because customers are no longer rationally comparing features – they’re emotionally invested in owning the product. This shift from satisfaction to compulsion represents a powerful strategic advantage for product teams who understand how to engineer it. The Psychology Behind Purchase Decisions Understanding how customers make buying decisions is crucial for creating products they feel compelled to purchase. Laurier explained that our brains have two primary decision-making systems: the “dog brain” (limbic system) and the rational brain (neocortex). The Dog Brain vs. The Rational Brain The dog brain is our emotional center, where intense responses and impulsive behaviors originate. It operates about 250 times faster than our rational brain. This explains why buying decisions often happen in milliseconds, driven by emotion rather than logic. Here’s what makes this understanding so powerful for product development: Purchase decisions start in the emotional center of the brain The rational brain only rationalizes decisions after they’re made emotionally Creating emotional connections happens before rational feature comparison Why Our Brains Prefer Emotional Decisions Our brain’s preference for emotional decision-making isn’t random – it’s about energy conservation. While the brain represents only about 2% of our body weight, it consumes approximately 20% of our energy. This creates a natural tendency to avoid energy-intensive rational thinking. The brain prefers activities like daydreaming, which require less energy than analytical thinking. This presents a major opportunity for product teams: If you can trigger your customer’s brain to daydream about your product, you’ve found a neurological shortcut to desire. The Coveted Condition Framework Building on this understanding of brain function, Laurier introduced the “coveted condition” framework – a tool for creating products that trigger emotional buying decisions. The framework focuses on what customers dream of becoming through using your product. It follows a simple structure: “I need [product] to become [coveted condition/desired future state].” The coveted condition isn’t about the product’s features – it’s about the better version of themselves that customers aspire to become. When you understand this aspirational state, you can design products that naturally trigger emotional desire. For example, truck commercials rarely show the everyday uses of pickup trucks. Instead, they show vehicles conquering rugged terrain, conveying power and freedom. The actual product experience might involve commuting and hauling supplies, but the coveted condition is about adventure and capability – emotional states that trigger the “I need that” response. By focusing on the coveted condition in your product development and marketing, you can bypass rational feature comparison and tap directly into your customers’ emotional decision-making system – making your product feel like a necessity rather than just an option. Integrating Product Development and Marketing Laurier described the value of integrating product development and marketing from the earliest stages of product development. In many organizations, these functions operate as distinct phases – engineers and product managers build the product, then throw it “over the wall” to marketing to make people want it. Breaking Down Functional Silos This separation creates a fundamental problem in product development. Engineers and product managers tend to excel at functional outcomes, while marketers are better at understanding emotional connections. When these teams work in isolation, the result is often a product that functions well but fails to create the emotional response needed for the “I need that” reaction. Laurier explained that successful companies introduce marketing thinking at the concept stage, having marketers involved in early product discussions. This approach ensures products are designed with emotional triggers in mind from the beginning. Learning from Apple’s Approach The interview highlighted Apple’s approach under Johnny Ive, who designed many of their most successful products starting with the colorful iMac. Ive often discussed how product development, design, and marketing were inextricably linked at Apple. This integration created feedback loops that ensured products weren’t just functional but emotionally compelling. Steps to Better Integration For product managers looking to implement this approach, consider these practical steps: Invite marketing team members to early concept discussions Focus early conversations on emotional reactions you want to trigger Create cross-functional feedback loops throughout development Prioritize features that spark emotion, not just solve problems Design onboarding experiences that deliver immediate gratification Build in shareable moments that encourage word-of-mouth marketing The 10X Better Rule for Product Success For a product to breakthrough the competition, it must be at least 10 times better than the existing product. Incremental improvements often fail to generate significant market traction, despite seeming like they should be sufficient. Why Incremental Improvements Don’t Break Through Many product managers assume that making a product twice as good as existing solutions should be enough to drive adoption. However, Laurier explained that a major psychological barrier stands in the way: the dramatic mismatch between how consumers and innovators perceive value. Research shows two critical factors at play: Consumers overvalue what they already have (the status quo) by a factor of 3 Innovators overvalue their new product by a factor of 3 This creates a 9:1 perception gap that must be overcome for a new product to break through. This means your product needs to be 10 times better than existing solutions to truly trigger the “I need that” response. Examples of 10X Better Products Laurier shared several examples of products that achieved this 10X improvement threshold: Tesla’s early electric vehicles: Not just electric, but better in nearly every measurable category – more powerful, more user-friendly, and featuring innovative details like pop-out door handles The original iPhone: Dramatically superior to stylus-based touchscreens on devices like the Palm Trio and Blackberry, making the interface vastly more intuitive A cycling computer: Compared to a basic speedometer, it tracked multiple metrics simultaneously, providing transformative data for serious cyclists Implementing the 10X Better Rule For product managers looking to apply this principle, consider these strategies: Identify the emotional metrics that matter most to your target customers Focus innovation on dimensions with high emotional impact Don’t spread improvements thinly across many features Concentrate resources on making a few aspects dramatically better Test whether your improvements actually trigger the “I need that” response The 10X Better Rule reminds us that breaking through consumer inertia and triggering psychological need requires dramatic improvement, not incremental change. This understanding helps explain why some innovative products succeed while others with seemingly good improvements fail to gain traction. The Role of Customer Research Understanding customers is fundamental to creating products they feel compelled to buy. Going Beyond Surface-Level Research Traditional customer research often focuses on functional needs and use cases. While this information is valuable, Laurier suggested that product teams need to dig deeper to uncover the emotional drivers that trigger the “I need that” response. Effective customer research for compelling products should: Identify what outcomes customers truly want to achieve Uncover the “coveted condition” customers dream about Determine what triggers genuine excitement Test emotional reactions, not just feature preferences Discover pain points that have emotional weight Finding the Emotional Connection Laurier explained that great products transcend being mere tools – they become pathways to who customers want to be. This perspective shifts customer research from focusing solely on what tasks customers need to accomplish to understanding how they want to feel when using your product. Testing for Emotional Response One advantage of modern digital marketing is the ability to test different emotional triggers quickly. Laurier described how nimble ad testing allows teams to: Try multiple emotional angles simultaneously Measure which messages create the strongest response Scale up approaches that trigger genuine excitement Quickly discard messaging that fails to connect emotionally By incorporating emotional testing into customer research, product teams can better understand what will transform their product from useful to necessary in the minds of their customers. This deeper approach to customer research provides the foundation for applying the frameworks Laurier discussed, ensuring that products aren’t just built to functional specifications but designed to trigger the psychological transformation that makes customers feel they need your product. The CLIMB Framework for Identifying Product Needs Laurier shared the CLIMB framework – an acronym for Customer Life Improving Mechanisms and Benefits. This tool helps product teams identify four levels of need that compelling products address. The Four Levels of Need The CLIMB framework breaks down customer needs into a hierarchy from basic functional benefits to transcendent impacts: Functional Needs: The practical benefits like saving time, reducing costs, and improving performance Emotional Needs: Feelings such as security, pride, belonging, enjoyment, and status Transformative Needs: Personal growth areas including health, sense of purpose, relationships, and confidence Transcendent Needs: Impact beyond the user, such as helping others, environmental benefits, and legacy building Laurier explained that while any good product addresses at least one level, the most compelling products typically address needs at multiple levels of the framework. Applying the CLIMB Framework For product managers, the CLIMB framework offers a structured approach to creating more compelling products: Identify your product’s position: Which levels of need does your product currently address? Find gaps and opportunities: Which higher-level needs could you incorporate? Prioritize applications: Determine which needs are most important for your target customers Guide marketing: Use insights to craft messaging that resonates emotionally Target audience selection: Identify customer segments most likely to value the needs your product addresses Using CLIMB for Persona Development Laurier described how the framework can enhance persona development by viewing needs through the eyes of specific customer types. For example, a family-oriented vehicle buyer might prioritize the transformative need of being a better parent (through safety features) while also valuing the emotional need of appearing successful and responsible. By systematically applying the CLIMB framework, product teams can move beyond feature-focused development to create products that connect with customers at deeper psychological levels – making them far more likely to trigger the coveted “I need that” response. The Role of Craftsmanship in Product Value Laurier explained that perceived craftsmanship creates emotional connections to products. This aspect of product development is often overlooked but can significantly influence whether customers feel compelled to purchase. How Craftsmanship Creates Emotional Value Laurier explained that when customers perceive craftsmanship in a product, they value it more highly – even for seemingly utilitarian items. This principle applies across product categories: Physical products: Attention to detail in design and construction signals value Digital products: Clean interfaces and thoughtful user experiences create emotional connection Services: Care and precision in delivery builds trust and loyalty The “Made with Love” Effect During our discussion, Laurier shared a personal example – telling his 10-year-old daughter that he made her sandwich “with love” genuinely enhances her enjoyment of it. This illustrates how the perception of care and intention transfers emotional value to the product experience. Similarly, when companies share behind-the-scenes glimpses of their development process, they tap into what Laurier called the “IKEA effect” – people value things more when they see or participate in their creation. Applying Craftsmanship Principles Product managers can leverage this insight by: Highlighting the care and attention in your development process Ensuring user interfaces reflect thoughtful design decisions Creating “evidence of craftsmanship” in product details Communicating the expertise behind your product Showing rather than telling about quality By incorporating craftsmanship into both your product development and marketing approaches, you can create deeper emotional connections that help trigger the “I need that” response from customers. Conclusion Creating products that customers feel compelled to buy isn’t about clever marketing tricks or feature overload—it’s about understanding the psychology that transforms wants into needs. By focusing on the emotional brain’s role in decision-making, integrating marketing and product development from the start, and applying frameworks like CLIMB, product managers can create offerings that trigger that coveted “I need that” response. As Laurier demonstrated throughout our conversation, successful products don’t just solve problems—they connect to customers’ aspirations and help them become who they want to be. The journey from product-market fit to creating products customers can’t resist requires a fundamental shift in approach. Rather than asking “What features should we build?” successful product teams ask “What will delight customers?” By aiming to be 10x better in ways that matter emotionally, showcasing craftsmanship in every detail, and focusing on innovation rather than imitation, product managers can create offerings that transcend rational comparison. When customers imagine their lives with your product and feel genuine excitement about owning it, you’ve created something truly compelling—a product they don’t just want, but absolutely need. Useful Links Visit Laurier’s website to get his book and sign up for emails with key takeaways Check out Laurier’s book, I Need That: Creating and Marketing Products People Are Compelled to Buy Connect with Laurier on LinkedIn Learn more about Graphos Product Search for Product: Knowledge on your podcast player Innovation Quote “Innovation distinguishes between a leader and a follower.”  – Steve Jobs Application Questions How could you identify the “coveted condition” that your product helps customers achieve? What specific aspirational state does your product enable, and how might you redesign your development and marketing approach to emphasize this transformation? Where in your current product development process could you integrate marketing thinking earlier? What specific changes to your team structure, meeting cadence, or decision-making process would help bridge the gap between engineering and marketing perspectives? Consider the CLIMB framework (functional, emotional, transformative, and transcendent needs). Which levels does your product currently address? How could your team elevate your product to address higher-level needs that might trigger stronger emotional connections? How could your team apply the 10X better rule to your current product roadmap? Which aspects of your product could realistically be made dramatically better in ways that matter emotionally to customers, rather than pursuing incremental improvements across many features? Where could you enhance the perception of craftsmanship in your product? What specific details could you improve to signal quality and care to customers, and how might you better showcase the expertise and intentionality behind your product’s development? Bio Laurier Mandin is a product marketing strategist and go-to-market expert who has guided hundreds of innovative products to market success. As founder and CEO of Graphos Product, he brings over three decades of expertise in helping product makers identify and penetrate resistant markets through visionary positioning and strategy.  Laurier developed the company’s proprietary CLIMB scoring system and Innovative Product Go-to-Market Roadmap process, which have become trusted frameworks for reducing launch risk and maximizing product success. His strategic insights have transformed struggling products into category leaders and helped numerous B2B and consumer innovations achieve breakthrough market performance.  A recognized thought leader in product marketing, Laurier is the author of “I Need That” and creator of the Product Knowledge podcast as well as an award-winning business columnist. When not working with clients to craft winning product strategies, he can be found cycling, cross-country skiing, hiking or enjoying paddle sports in his local community.  Thanks! Thank you for taking the journey to product mastery and learning with me from the successes and failures of product innovators, managers, and developers. If you enjoyed the discussion, help out a fellow product manager by sharing it using the social media buttons you see below. Source

Transform product team collaboration with Workshop Culture Watch on YouTube TLDR In my recent conversation with Alison Coward, author of Workshop Culture, we explored how product managers can transform collaboration and problem-solving through effective workshop facilitation. Alison shared that workshop culture isn’t about running constant workshops but about applying workshop principles to everyday collaboration. The key is a three-part approach: thorough preparation before, skilled facilitation during, and consistent follow-through after. By starting with the end in mind and focusing on creating the right environment for both introverted and extroverted team members, product managers can break down silos and foster innovation as a collective outcome. Key Topics Defining workshop culture and its importance for cross-functional collaboration The three-part workshop process (before, during, after) Starting with the end in mind when planning workshops Effective facilitation techniques that balance different communication styles The power of being the “ignorant” facilitator without domain expertise Creating the right physical environment for productive workshops Ensuring post-workshop implementation and follow-through Real-world example of transforming organizational collaboration Innovation as a collective outcome requiring effective team dynamics Introduction Today we’re exploring how product managers can improve their work with stakeholders, promote collaboration and trust, and apply a problem-solving approach. Our guest is Alison Coward, author of Workshop Culture: A Guide to Building Teams That Thrive. With over 20 years of experience leading creative teams, Alison has helped organizations worldwide boost team creativity, productivity, and collaboration. We’ll unpack what workshop culture means, learn practical steps for product managers to design impactful workshops, and hear real examples of how these techniques have helped teams overcome challenges. Understanding Workshop Culture Alison defined workshop culture as “ a team culture that uses the principles and practices of workshops and facilitations to achieve creativity and productivity and to build a more effective environment for team collaboration.” It doesn’t mean a team is running workshops all the time. Instead, it’s about applying the principles and practices of workshops and facilitation to create a more effective environment for team collaboration. Think about the last great workshop you attended. Remember that feeling at the end of the day – ideas flowing freely, people engaging meaningfully, and clear outcomes achieved. Often, this feeling disappears when you go back to work the next day. Workshop culture aims to capture this energy and extend it into everyday work. Despite recognizing the importance of collaboration in theory, many product teams struggle to make it work in practice. Meetings become status updates rather than problem-solving sessions. Stakeholders protect their territory rather than exploring possibilities. The implementation gap between idea and execution grows wider. Workshop culture bridges this gap by focusing on three key elements: Element 26213_cea0c7-a0> Description 26213_011b4a-e2> Principles 26213_a64bbd-27> The mindsets that drive effective collaboration (curiosity, active listening, synthesis) 26213_18b877-53> Practices 26213_1e6aed-53> The specific techniques that facilitate shared understanding and decision-making 26213_dcc48d-eb> Environment 26213_b743de-fb> The physical and psychological conditions that enable creative thinking 26213_023862-26> For product managers, implementing workshop culture means transforming how your team approaches problems. Rather than defaulting to the highest-paid person’s opinion or the loudest voice in the room, you create structured opportunities for all perspectives to contribute to solutions. The result? Teams that not only collaborate more effectively during dedicated sessions but carry that collaborative mindset into all aspects of product development. The Three-Part Workshop Process Effective workshops involve much more than just the session itself. Alison described a three-part process that many product managers overlook when planning collaborative activities: 1. Before: Preparation and Planning Preparation represents nearly 60% of what makes a workshop successful. This phase involves researching context, understanding team dynamics, clarifying objectives, and designing activities that will achieve your desired outcomes. 2. During: Facilitation and Activities This is what most people think of as “the workshop” – the actual time when participants gather to collaborate. While important, Alison noted that even the best-planned activities can go sideways if you’re not prepared to adapt to what emerges in the room. 3. After: Implementation and Follow-Up Perhaps the most neglected aspect of workshops is what happens afterward. Without deliberate follow-through, even the most energizing session can fail to create lasting impact. This phase transforms insights into action. When I work with product teams, I often see an overemphasis on the “during” phase – selecting cool activities or techniques – while neglecting thoughtful preparation and consistent follow-up. Alison’s framework provides a more balanced approach. For product managers, this three-part process applies to various collaborative scenarios: Customer journey mapping sessions Feature prioritization workshops Roadmap planning meetings Retrospectives and lessons-learned reviews Cross-functional problem-solving sessions By treating each phase with equal importance, you significantly increase the likelihood that your collaborative efforts will produce meaningful results rather than just generating ideas that never see implementation. Starting with the End in Mind When preparing your workshop, rather than starting with activities or exercises, Alison advised beginning with what happens after the workshop ends. Imagine yourself at the end of the workshop. What results did you get? What did you achieve? This approach resembles the “pre-mortem” technique I often use with product teams. While a post-mortem analyzes what went wrong after a project ends, a pre-mortem imagines potential failures before you begin. Alison’s method takes this concept further by envisioning success and working backward. Here’s how product managers can apply this approach: Envision successful implementation: Picture your team three months after the workshop. What specific changes have occurred? What decisions have been implemented? How has your product development process improved? Identify necessary outputs: Based on your vision of success, determine what tangible deliverables the workshop needs to produce. These might include prioritized feature lists, customer journey maps, or action plans with clear ownership. Research context thoroughly: Understanding team dynamics and project history represents about 50-60% of effective workshop planning. Connect with key stakeholders: Conduct one-on-one conversations with participants before the workshop to understand their perspectives, concerns, and desired outcomes. This approach transforms workshops from isolated events into strategic inflection points in your product development process. By starting with the end in mind, you ensure that every activity and discussion directly contributes to tangible progress rather than just generating ideas that never see implementation. Designing an Effective Workshop Once you’ve envisioned what success looks like after your workshop, Alison recommended a structured approach to designing the session itself. This process helps product managers move beyond generic brainstorming to create truly purposeful collaborative experiences. Alison outlined key steps that form the backbone of effective workshop design: 1. Identify Clear Purpose and Outcomes Begin with a concise statement of why you’re bringing people together and what you intend to achieve. For product managers, this might be “Align on our Q3 roadmap priorities” or “Identify the top customer pain points to address in our next release.” 2. Determine Tangible Outputs Define the specific deliverables you need the workshop to produce. These tangible outputs should directly support your post-workshop implementation plan. Examples include: Prioritized feature list with effort/impact estimates Refined user personas with key insights Journey maps highlighting critical moments Decision matrix for evaluating options 3. Brainstorm Essential Questions Instead of jumping to activities, Alison suggested brainstorming all the questions you need answered during the session. This question-based approach focuses on curiosity rather than predetermined outcomes. 4. Design Agenda Based on Questions Structure your agenda to address these questions in a logical flow. This ensures your workshop tackles the right problems in the right order. 5. Select Appropriate Activities Only now should you select specific exercises and techniques that will help answer your essential questions and produce your desired outputs. This methodical approach prevents a common pitfall I’ve observed in product teams—choosing workshop activities because they seem fun or trendy rather than because they serve your specific purpose. By following Alison’s process, you create workshops that deliver meaningful results rather than just generating temporary enthusiasm. Facilitation During the Workshop Even the best-planned workshop agenda must be held lightly. When facilitating product team sessions, you’ll need to adapt to the dynamics that emerge once everyone is in the room together. Setting the Tone Alison recommended starting workshops with an interactive exercise that signals “this isn’t a normal meeting.” These opening activities establish psychological safety and participation norms that carry through the entire session. Foundational Facilitation Techniques Alison shared several foundational techniques that she frequently includes in workshops. Breakout discussions: split into smaller groups for more intimate discussions. Individual reflection: Provide time for people to think about a question individually before discussing it. The Cycle of Facilitation Alison broke down effective facilitation into a repeating four-part cycle that product managers can master: Asking questions: Pose clear, thoughtful questions that drive toward your workshop goals. Active listening: Pay close attention to responses, watching for both verbal and non-verbal cues. Managing discomfort and chaos: Get comfortable with the messiness of divergent thinking. Synthesizing insights: Connect ideas and identify patterns to move the group forward. This cycle repeats throughout your workshop as you guide participants through different activities and discussions. For product managers, effective facilitation is about creating the conditions where your team’s collective intelligence can emerge. By mastering these techniques, you’ll transform unproductive debates into collaborative problem-solving that drives your product forward. The Facilitator’s Role The Advantage of Not Being the Expert As product managers, we often feel pressure to be the domain experts, but this can actually hinder effective workshop facilitation. Alison explained that she has successfully facilitated workshops in industries where she had no specific expertise—finance, pharmaceuticals, energy—precisely because her outsider perspective allowed her to: Ask basic questions that insiders might avoid Notice when discussions become too detailed or technical Pull the group back to higher-level objectives Help participants explain concepts in clearer terms While evolutionary improvements may benefit from deep domain knowledge, revolutionary innovation often requires fresh thinking unencumbered by the way things have always been done. Separating Facilitation from Content A subject-matter expert acting as a facilitator may have difficulty separating the needs of the group from their own preconceived ideas about what the workshop’s outcomes should be. For product managers, this means making a conscious choice before each workshop: Will you participate as a content contributor, or will you focus on facilitating the process? Trying to do both simultaneously often leads to suboptimal results in both roles. Facilitator as Process Guide As a facilitator, your primary responsibility is to keep your eyes on the process level: Is the team headed in the right direction? Are we getting into too much detail? Are we managing time effectively? Is everyone contributing? By focusing on these process questions rather than content details, you create space for your team’s collective expertise to emerge in ways that a single expert’s perspective never could. Physical Workshop Environment The environment of a workshop sends powerful signals about the type of interaction expected. Walking into a properly set up workshop space immediately communicates “this is different from a normal meeting.” Key elements can include: Post-it notes arranged on tables Flip charts positioned around the room Different seating arrangement from standard meetings Visible thinking tools and workshop materials These visual cues prime participants to engage differently from how they would in routine meetings. Dynamic Movement and Interaction Effective workshops should feel physically dynamic. Participants should: Get up and move around the room Interact with different team members Physically manipulate ideas (moving post-its, drawing on boards) Stand together while discussing concepts Research shows that standing meetings can be more productive because they engage participants physically as well as mentally. Choreographing the Workshop Space Alison thinks about the choreography of a workshop, viewing the room as a canvas for collaboration. She likes to get an image of what the workshop room looks like beforehand, so she can start planning the physical setup of the workshop. For product managers, this might mean planning specific areas for customer journey mapping, another for prioritization exercises, and separate spaces for small group discussions. This intentional use of space helps guide the energy and focus of your team throughout the workshop. Following Through Post-Workshop The excitement of a productive workshop often creates momentum that quickly dissipates once participants return to their daily responsibilities. Designing for Implementation Successful follow-through begins during the planning phase, not after the workshop ends: When designing your workshop, clearly define the specific outputs needed for implementation Create templates and frameworks that make post-workshop action straightforward Assign ownership for follow-up tasks before the workshop concludes Schedule check-in meetings to review progress on workshop outcomes The Reality of Behavior Change Workshops often aim to change behaviors and working relationships. Alison explained that behavior change does not happen overnight. Workshop culture involves the workshops themselves and the work afterward to make sure changes stick. For product managers, this means viewing workshops as catalysts within a longer transformation journey rather than one-time solutions. Persistent Follow-Up Effective post-workshop implementation requires: Regular check-ins with participants Revisiting key conversations and decisions Potentially scheduling follow-up workshops to continue progress Consistent reinforcement of new collaborative behaviors This persistent attention to implementation is what distinguishes workshops that create lasting impact from those that generate only temporary enthusiasm. For product teams, this might mean incorporating workshop outputs into sprint planning, creating visible artifacts that remind the team of decisions made, or establishing new rituals that reinforce workshop outcomes. Real-World Example: Creative Agency Transformation To illustrate how workshop culture transforms organizations, Alison shared a case study from her consulting work. She described a creative agency with ambitious growth goals that struggled with departmental silos despite producing high-quality work. Breaking Down Silos The agency’s challenge was familiar: Departments worked effectively in isolation but rarely collaborated across boundaries. Their meetings were primarily transactional, focusing on immediate deliverables rather than strategic thinking or innovation. Alison’s approach involved: Bringing teams together in regular workshop settings Facilitating conversations they wouldn’t normally have Moving beyond transactional interactions to forward-looking discussions Creating space for big-picture thinking and creative collaboration A Framework for Team Development Alison implemented a structured framework to guide the agency’s transformation: Framework Stage 26213_266d18-e9> Focus Area 26213_c16623-1d> Workshop Purpose 26213_2c6b76-a9> Alignment 26213_b970ce-f0> Shared vision 26213_bb153d-a9> Creating clarity on collective direction and success metrics 26213_943323-cf> Cohesion 26213_8972e6-ac> Role clarity 26213_7f2a18-34> Understanding how individual contributions connect to the bigger picture 26213_8662df-fd> This methodical approach helped team members see beyond their departmental boundaries and understand how their work contributed to the organization’s larger goals. Measuring Success The transformation became evident when team members began saying, “We need to run more of our meetings like these workshops.” This shift in mindset indicated they had recognized the value of structured collaboration not just in special sessions but in their everyday work. For product managers, this case study demonstrates how workshop culture can transform siloed product development into true cross-functional collaboration. By creating structured opportunities for alignment and cohesion, you can break down the barriers that often separate product, engineering, design, and business teams. Conclusion Workshop culture offers product managers a framework for transforming how teams collaborate and solve problems. As Alison explained, it’s not about running constant workshops but about applying workshop principles to everyday work: starting with the end in mind, creating space for diverse thinking styles, facilitating effectively without dominating, and following through persistently. These practices help break down the silos that often impede product innovation. If you’re looking to improve cross-functional collaboration and drive more effective product decisions, consider how you might implement these workshop principles in your next meeting or problem-solving session. Small changes in how you approach collaboration can yield significant improvements in both team dynamics and product outcomes. Useful Links Check out Alison’s book, Workshop Culture: A Guide to Building Teams That Thrive Connect with Alison on LinkedIn Learn more about Bracket Creative Innovation Quote “Leaders encourage and support the individuals in those groups because they are the source of ideas that constitute the raw material of innovation. Yet the ultimate innovation will almost always be a collective outcome, something devised through group interaction.” – Linda Hill, Collective Genius Application Questions How could you adapt your current product planning meetings to incorporate workshop culture principles? Which specific meeting would benefit most from being redesigned as a workshop? When facilitating cross-functional collaboration, how could you better balance the needs of both introverted and extroverted team members? What specific techniques might you implement in your next product discovery session? Think about your last product workshop or collaborative session that didn’t lead to effective implementation. How could you have designed it differently, starting with the end in mind? What specific post-workshop mechanisms could you put in place for your next session? How could your team apply the concept of “workshop choreography” to your physical or virtual workspace? What signals or environmental changes could help transition your regular meetings into more collaborative problem-solving sessions? Consider the tension between individual contributions and collective outcomes in your product development process. How could you structure your next feature prioritization or roadmap planning session to better harness both individual creativity and collective wisdom? Bio Alison Coward is the founder of Bracket, a consultancy that partners with ambitious, forward-thinking companies to help them build high-performing team cultures. She is a team culture coach, workshop facilitator, trainer, keynote speaker and author of “Workshop Culture: a guide to building teams that thrive” and “A Pocket Guide to Effective Workshops”. Clients include: Google, Meta, Wellcome and the V&A. With 20 years’ experience working in, leading and facilitating creative teams, Alison is passionate about finding the balance between creativity, productivity and collaboration so that teams can thrive and do their best work together.  Thanks! Thank you for taking the journey to product mastery and learning with me from the successes and failures of product innovators, managers, and developers. If you enjoyed the discussion, help out a fellow product manager by sharing it using the social media buttons you see below. Source

AI in product management – perspectives from the legal industry, education, and entrepreneurship Watch on YouTube TLDR In my recent conversation with product executive and former colleague Matt Coatney, we explored how artificial intelligence is transforming product management and innovation. The technology has evolved dramatically in the past decade, from fragile, expensive systems to powerful tools that integrate seamlessly into workflows. Product managers can leverage AI for everything from customer research and brainstorming to prototyping and workflow automation. While organizations must balance specialized versus general AI tools and address concerns like hallucinations and data privacy, the benefits for productivity and innovation are substantial. The most successful implementations focus on solving real customer problems and seamlessly integrate into existing workflows. Key Topics Using AI as a brainstorming partner to overcome creativity blocks Accelerating product development with AI-powered prototyping tools Integrating AI into product management platforms and workflows Balancing specialized AI products versus general-purpose models Managing AI hallucinations and verification challenges Learning from AI adoption in risk-averse industries like legal Impact on mentorship and professional development Future trends: local AI models and data privacy Introduction In this episode, we had a free-form discussion. My guest doesn’t know what I’m going to ask him and I don’t know what he is going to ask me. Our goal is to make the discussion valuable for product managers, leaders, and innovators. Joining me is a former colleague, Matt Coatney. We worked together on an important product for LexisNexis. I went on to teach graduate courses in innovation and coach product managers and leaders in organizations, while Matt got more involved in Information Technology, leading professional services and consulting operations for a few organizations as well as serving as CIO for one of the large law firms in the US. His career started in AI systems some 25 years ago and today he continues learning about and applying AI and is also is a product executive. The Current State of AI in Product Management Matt asked about my observations of the effects of AI, from the perspective of a product manager, entrepreneur, and educator. Last year at the Product Development and Management Association (PDMA) conference, three separate sessions featured AI tools specifically designed for customer research. This wasn’t just theoretical discussion. These were practical applications already being implemented by forward-thinking product teams. At the PDMA conference, I participated in a workshop led by Mike Hyzy, where we completed what would normally be a 3-5 day Design Sprint in just three hours. Our team consisted of four humans and one AI companion, which functioned as a fifth team member. The AI was operated by someone skilled in prompt writing who understood the product space. What impressed me most was how the AI accelerated our work. When we brainstormed customer problems, the AI helped us explore details we hadn’t considered. It suggested unmet needs, offered additional perspectives, and helped us develop a comprehensive view in a fraction of the time it would have taken traditionally. By the end of those three hours, we had developed a solid marketing description for solving a customer problem and created a decent prototype—impressive results for such a compressed timeframe. Practical AI Applications for Product Professionals On a personal level, I’ve found AI tools like Claude to be valuable as brainstorming partners. Rather than trying to craft lengthy prompts with all my requirements upfront, I’ve shifted to a more conversational approach. Kicking ideas around with AI helps me overcome the inertia of starting a task. Matt shared similar experiences, noting that this brainstorming use case is often underappreciated. While many focus on productivity enhancements like email responses and content generation, the creative thinking support is particularly valuable for entrepreneurs and product innovators. Many professionals today lack the “water cooler culture” opportunities to casually discuss ideas with colleagues, especially with remote work becoming more common. AI tools help fill this gap, providing an always-available thinking partner. We also discussed the prototyping capabilities of AI. Matt mentioned tools like GitHub Copilot for assisting developers, and V0, which can build functional web applications directly from human prompts. These tools allow people with little or no coding knowledge to write code. For entrepreneurs and product managers, these prototyping tools address a common challenge: developing clear user workflows before engaging software developers. Taking time to create detailed prototypes helps clarify thinking and identify assumptions that might confuse users. AI accelerates this process, allowing teams to get clarity on user experiences sooner and save significant time and money during development. AI Application 26189_bb986f-32> Value for Product Professionals 26189_317750-59> Brainstorming partner 26189_7bf2d1-1c> Overcomes creative blocks and inertia 26189_72a4ee-82> Idea validation 26189_594cdb-10> Tests concepts quickly without scheduling meetings 26189_a9b617-9d> Prototyping assistance 26189_08e702-a9> Accelerates creation of user interfaces and workflows 26189_4f4fef-22> No-code development 26189_37a225-25> Allows faster proof-of-concept creation 26189_3ec935-e1> Design iteration 26189_089966-fa> Enables rapid exploration of alternative approaches 26189_0f07b2-92> AI-Enhanced Product Management Platforms The integration of AI into existing product management tools represents a significant opportunity for enhancing team effectiveness. ProdPad, one of the more popular platforms for managing product management work, and many competitors, have recently added AI capabilities—currently called Co-pilot—to their toolkit. What makes these integrated platforms valuable is their ability to serve as a central repository for product information. They help teams maintain alignment with overall strategy, track progress toward objectives, and understand user stories. With AI enhancement, these platforms can now help identify gaps in strategy alignment, surface unmet customer needs based on existing data, and answer questions from stakeholders outside the immediate product team. Matt and I discussed an important consideration when choosing AI solutions: whether to invest in specialized AI products or use general-purpose AI with custom prompts. Many specialized tools are essentially using the same large language models as ChatGPT but with carefully engineered prompts and workflows tailored to specific use cases. For organizations making these decisions, Matt shared insights from his experience co-leading AI initiatives at his law firm. They’ve taken a tiered approach, using one solid general-purpose language model for most applications while investing in legal-specific AI products for their revenue-generating lawyers. For highly specific tactical use cases, they evaluate additional specialized tools—but only when the return on investment justifies the significant cost. AI Tool Approach 26189_df0bd7-91> Pros 26189_1b6ce5-9f> Cons 26189_35f7e3-a5> General-purpose AI (e.g., ChatGPT, Claude) 26189_671b22-69> Lower cost, versatility, continuous updates 26189_b78775-e9> May require custom prompt engineering, less specialized 26189_534839-66> Industry-specific AI solutions 26189_26f06d-66> Optimized for domain-specific knowledge and workflows 26189_006306-e3> Higher cost, potential duplication of capabilities 26189_739c7b-d2> Integrated platform with AI features 26189_09f8da-3a> Seamless workflow integration, centralized data 26189_e91090-10> May have less advanced AI capabilities than specialized tools 26189_3a6b54-1d> Custom-built internal AI tools 26189_c3110f-c4> Precisely tailored to organization’s needs 26189_5b53ee-ab> Resource-intensive to develop and maintain 26189_235583-a4> Matt emphasized that the bar should be high for investing in specialized products when general tools can accomplish 90% of the required tasks. Organizations must consider whether the additional 10% improvement justifies spending five to six figures on multiple specialized tools, which could quickly add up to a million-dollar investment. Workflow integration is important for successful AI implementation. Matt provided an example: if an organization has employees manually uploading invoices to ChatGPT, extracting data, and re-entering it into systems, they’re missing efficiency opportunities. The real value comes from automating these workflows to minimize manual steps. Implementation Focus 26189_6ce0d0-41> Key Considerations 26189_47c3fa-69> Model Selection 26189_a0e861-84> Balance between general and specialized capabilities 26189_99570f-79> Integration Level 26189_95e9ea-54> How seamlessly AI fits into existing workflows 26189_873300-59> Data Strategy 26189_69e331-e2> What information AI can access to maximize value 26189_931d13-eb> ROI Analysis 26189_9bffc8-c3> Justification for specialized AI investments 26189_8a07aa-6a> User Adoption 26189_ce898e-29> Support for different user groups based on needs 26189_30c89b-66> Matt also reflected on the current state of AI capabilities. He noted that today’s models are becoming so robust that it’s increasingly difficult to find use cases they can’t handle, at least for English language applications. This growing general applicability raises the bar for specialized solutions to prove their value. We briefly touched on the debate around artificial general intelligence (AGI), acknowledging that while the term itself may be somewhat ambiguous, the general applicability of today’s AI tools is already impressive. This evolution has significant implications for how organizations approach their AI strategy, suggesting that for many use cases, the focus should be on integration and workflow rather than pursuing incrementally more powerful specialized models. Addressing AI Challenges and Concerns Implementing AI in product development isn’t without challenges. Matt and I explored several concerns that organizations must address to effectively leverage these tools. We discussed the potential impact on professional development, particularly in apprenticeship-model professions. If senior staff rely on AI instead of junior team members for certain tasks, how will those juniors develop expertise? Matt raised this concern for lawyers and software developers, and we discussed its relevance for product management as well. However, we identified a potential upside: AI could handle routine tasks that would previously occupy junior employees’ time, freeing them to engage in higher-value learning experiences. By eliminating basic tasks like fixing simple coding errors or catching obvious document mistakes, AI might actually create more meaningful mentorship opportunities focused on strategic thinking and core professional skills. Product managers should be aware of risks of AI hallucinations AI hallucinations—where models generate plausible but incorrect information—remain a persistent challenge despite recent improvements. I shared a personal experience using AI to analyze a detailed lease agreement. While the AI successfully identified unfavorable clauses and accurately referenced their location in the document, I was initially concerned it might fabricate issues. In other contexts, I’ve frequently encountered hallucinations where AI adds information not present in the source material. Both hallucinations and omissions pose serious risks, particularly in contexts like legal work. Matt’s firm strongly advises lawyers to verify all AI outputs, treating them as they would work from a junior associate. As Matt put it, the AI is like “a first-year associate that doesn’t sleep and is always there,” but still requires careful review. Type of AI Error 26189_76bd13-79> Risk 26189_403eaf-90> Mitigation Strategy 26189_91b91e-07> Hallucination 26189_f8260e-a8> Introducing incorrect information 26189_36b002-f0> Verify all outputs against source material 26189_1ee839-84> Omission 26189_37da4d-85> Missing critical information 26189_f5312b-05> Verify outputs and use multiple prompts to ensure comprehensive analysis 26189_2e4ae0-58> Misinterpretation 26189_9ad1be-a1> Drawing incorrect conclusions 26189_957aea-db> Apply domain expertise to evaluate outputs 26189_130d4a-b7> Over-confidence 26189_fda92a-94> Presenting speculation as fact 26189_780dc0-13> Require citation of sources for key claims 26189_45a9b8-67> For organizations implementing AI, establishing appropriate guardrails is essential. Matt described how his firm has developed policies that provide guidance without outright prohibiting most AI use cases. They focus on education about appropriate usage contexts, data confidentiality protections, and verification requirements, creating a balanced approach that manages risks while capturing benefits. AI Adoption in Risk-Averse Industries The legal profession offers insights into how AI transforms traditionally cautious industries. As a product management professional, I pay special attention to adoption patterns in risk-averse sectors—when they embrace new technology, it often signals well-established value and manageable risks. Matt shared a progression of attitudes toward AI within law firms over the past two years. Initially, many leaders experienced fear about AI’s potential to disrupt their profession. This concern was both practical and financial: AI tools represented a significant investment while potentially reducing billable hours by increasing efficiency—a challenging value proposition in an hourly billing model. Over time, with education and exposure, these perspectives evolved into more nuanced views. Today, many firms, including Matt’s, are bullish on AI’s capabilities within appropriate boundaries. Some see it as a competitive differentiator, while others pursue AI implementation to avoid falling behind competitors. The adoption curve among individual lawyers follows patterns familiar to any technology implementation. Matt observed that his organization has moved beyond early adopters and is now entering the early majority phase. While some users try AI briefly before abandoning it, those who integrate it into their workflow show steadily increasing usage over time. AI Use Case in Legal 26189_b45643-e3> Description 26189_81bddb-46> Value 26189_eb54e9-99> Content summarization 26189_e0ee99-d9> Condensing contracts, briefs, proceedings, and statutes 26189_3c154c-db> Saves time on document review 26189_a64ae4-a4> Synthesis 26189_fab9bf-d4> Combining information from multiple sources 26189_a03f6e-0e> Creates comprehensive understanding 26189_b6c5a2-7b> Drafting assistance 26189_973e14-cc> Generating initial document drafts 26189_668b21-42> Accelerates document preparation 26189_45dc85-37> Strategy brainstorming 26189_8f4f9b-08> Exploring alternative approaches and counterarguments 26189_0453c3-40> Enhances case preparation 26189_43aa64-3a> Provision analysis 26189_ba4752-d2> Identifying favorable/unfavorable contract terms 26189_d9f7a5-3a> Improves negotiation position 26189_c95f7c-46> What particularly impressed Matt after 25 years in the AI space was how dramatically the technology has evolved. The systems he worked with 15 years ago were fragile, expensive, rules-based, and easily broken when applied to adjacent use cases. Today’s models understand language nuance, adapt to specialized terminology, and apply reasoning to novel situations—capabilities previously thought to require years more development. For product managers serving risk-averse industries, this evolution suggests several insights: emphasize verification and human oversight in your AI implementation, focus on specific high-value use cases with clear ROI, and recognize that resistance often transforms into enthusiasm as users experience benefits firsthand. The legal industry’s journey provides a roadmap for introducing AI into other conservative sectors, from healthcare and finance to government and education. The Future of AI in Product Management The most successful AI implementations in product management will be those that seamlessly integrate into existing workflows. Matt and I agreed that transparent integration represents the next frontier for AI tools, moving beyond standalone applications to become embedded features within the systems product teams already use. This parallels our experience at LexisNexis, where we worked together on a product that integrated new capabilities without requiring users to change their behavior. I expect platforms like ProdPad to succeed by making AI assistance transparent and aligned with users’ natural work patterns. By contrast, Microsoft’s Copilot approach in Office applications often feels disconnected from the actual workflow. As I mentioned to Matt, I frequently close the Copilot prompt when opening Word because it feels like an extra step that interrupts my process rather than enhancing it. Our conversation also touched on recent developments in AI democratization. We discussed Deep Seek, which enables running sophisticated AI models on relatively inexpensive hardware—from Raspberry Pi devices to modest servers. This trend capability allows organizations concerned about data privacy and security to maintain complete control over their AI systems and data. Matt predicted this will lead to a bifurcated market: cutting-edge models will continue to require substantial computing resources, while slightly older generations will become commoditized and available for edge computing applications. He envisioned an “AIOT” (AI + Internet of Things) future where smart devices incorporate local AI processing. Future AI Trend 26189_c8b32d-ab> Impact on Product Management 26189_762500-52> Workflow integration 26189_33265e-24> Reduced friction in adoption and usage 26189_d56176-6a> Local AI models 26189_a4a5c9-43> Enhanced data privacy and security control 26189_955655-d6> Edge computing AI 26189_c1e643-b3> New product possibilities with embedded intelligence 26189_45254d-af> Democratized access 26189_882e3e-6b> More accessible AI for smaller teams and organizations 26189_0a7ff9-af> Specialized fine-tuning 26189_f9bccf-e5> Tailored models for specific product domains 26189_e7e62e-68> Beyond technical advancements, Matt expressed enthusiasm about AI’s potential social impact. He’s personally focused on applying AI to health and climate challenges. He noted that while large pharmaceutical companies are exploring AI applications, there’s tremendous untapped potential for nonprofits, NGOs, and small mission-driven organizations to leverage these tools. This social dimension presents an opportunity for product managers to apply their skills beyond traditional business contexts. As AI becomes more accessible, product professionals can help mission-driven organizations integrate these capabilities into their workflows, potentially creating outsized impact through enhanced efficiency and effectiveness in addressing critical social challenges. Key Insights for Product Managers Throughout our conversation, several actionable insights emerged that product managers can apply immediately to their work with AI. We discussed the need to reframe executive demands for adding AI into customer-focused questions. Matt and I both encountered situations where leadership teams push for AI integration without clarity about the specific value it will provide. Too often, senior executives make broad statements like “we need to add AI to what we’re doing” without understanding what that actually means for products or customers. As product professionals, our responsibility is to translate these directives into customer-oriented questions: How can we enhance our product’s value using AI to solve problems customers actually care about? Will AI help solve these problems faster, better, or more comprehensively? This reframing helps ensure AI serves genuine customer needs rather than becoming a superficial feature. Another opportunity Matt highlighted was AI’s ability to process unstructured data. He noted how frequently we encounter friction in our daily lives—retyping information into forms or manually extracting data from documents only to re-enter it elsewhere. These pain points represent prime opportunities for AI-enhanced products. Customer Pain Point 26189_cb85d5-5e> AI Application Opportunity 26189_41a141-4e> Form completion 26189_83a80d-d4> Auto-extraction of information from existing documents 26189_d2eae2-25> Data transcription 26189_5b4336-47> Converting formats without manual retyping 26189_17177c-9e> Information synthesis 26189_236f21-9c> Combining data from multiple sources automatically 26189_3c6c6e-cb> Content transformation 26189_a5c3d4-34> Converting between visual and text formats 26189_dec9e1-3f> Pattern recognition 26189_f75355-73> Identifying trends in unstructured information 26189_982c93-72> Matt shared personal examples of AI’s potential, including using it to solve scientific puzzles from Scientific American and helping his daughter understand idioms in her schoolwork. These seemingly simple applications demonstrate how AI can remove friction points that we’ve previously accepted as unavoidable. Conclusion The integration of AI into product management isn’t just a passing trend—it’s fundamentally transforming how we research customer needs, prototype solutions, and create value. AI has evolved from a specialized, experimental technology to an essential tool in the product manager’s toolkit. The question is no longer whether to incorporate AI into product development processes, but how to do so most effectively. As you incorporate AI into your product management practice, remember that the technology itself is just a tool. The true value comes from how you apply it to understand customer needs, solve meaningful problems, and create products that improve people’s lives. By focusing on these fundamentals while embracing AI’s capabilities, you’ll be well-positioned to thrive in this new era of product innovation. Useful Links Connect with Matt on LinkedIn Learn more about Matt’s mission-based organization, 10 Billion Ventures Innovation Quotes  “The best way to predict the future is to create it.” – attributed to Alan Kay and Peter Drucker  “The way to get started is to quit talking and begin doing.” – Walt Disney Application Questions How could you integrate AI as a brainstorming partner in your current product development process? What specific activities (customer research, feature ideation, user story creation) might benefit most from this partnership approach? In what ways could your team use AI to accelerate prototyping without sacrificing quality? How might this change your current approach to validating product concepts before full development? How could you transform executive requests to “add AI” into customer-focused initiatives? What specific customer problems in your product area could AI help solve more effectively? How could your organization balance investment in specialized AI tools versus leveraging general-purpose AI models with custom prompts? What ROI metrics would you use to make these decisions? How could you ensure AI integration enhances rather than disrupts your existing product workflows? What would seamless integration look like for your specific product and team? Bio Matt Coatney is a seasoned C-level AI and product executive with 25 years of diverse experience. His expertise includes: artificial intelligence, business growth, and product development. Matt has also supported a wide range of industries such as manufacturing, media, law, life sciences, government, and finance. His client list includes some of the largest, most well-known organizations in the world, including Microsoft, IBM, the Bill and Melinda Gates Foundation, Pfizer, Deloitte, HP, and the US government. Matt writes and speaks frequently on technology and product topics. In addition to a TED talk and keynotes, his work has been published by MIT, HarperCollins, and O’Reilly and has appeared in books, journals, and international conferences. Matt’s latest book is The Human Cloud: How Today’s Changemakers Use Artificial Intelligence and the Freelance Economy to Transform Work, with Matthew Mottola. Thanks! Thank you for taking the journey to product mastery and learning with me from the successes and failures of product innovators, managers, and developers. If you enjoyed the discussion, help out a fellow product manager by sharing it using the social media buttons you see below. Source

In a captivating discussion, Elizabeth Samara-Rubio, Chief Business Officer at SiMa.ai, shares her journey from product management to senior leadership, drawing from her experiences at HP and AWS. She emphasizes the importance of a clear vision and data-driven decision-making in leadership. Elizabeth discusses the potential of Edge AI, illustrating its benefits in privacy and cost savings. She also provides valuable insights for aspiring leaders on embracing calculated risks and fostering a culture of innovation to create lasting customer value.

How AI captures customer needs that human product managers miss Watch on YouTube TLDR In my recent conversation with Carmel Dibner from Applied Marketing Science, we explored how artificial intelligence is transforming Voice of the Customer (VOC) research for product teams. The collaboration between AMS and MIT researchers has yielded impressive results, with AI tools not only matching human analysts in identifying customer needs but often exceeding them—especially for emotional needs that humans might overlook. Rather than replacing human researchers, AI serves as a copilot, helping product teams uncover twice as many unique needs while reducing analysis time and eliminating bias. This hybrid approach offers tremendous potential for innovation, particularly in the early stages of product development. Key Topics AI can now match or exceed human analysts in identifying customer needs from research data Large Language Models (LLMs) are surprisingly effective at capturing emotional needs that humans often miss AI finds twice as many unique customer needs compared to human analysts alone The most effective approach is using AI as a “copilot” alongside human researchers AI tools significantly speed up data analysis and can process multiple data sources simultaneously These tools can find niche needs that create innovation opportunities AI still has limitations in prioritizing needs and assessing the validity of different data sources Introduction Voice of the Customer research has been a cornerstone of product management for decades. But it is changing, with AI tools that are transforming how we uncover and analyze customer needs. While some fear AI might miss the human element of customer research, recent advancements show it can actually help us capture more nuanced emotional needs while eliminating human bias. Joining us is returning guest, Carmel Dibner, who is a principal and co-owner at Applied Marketing Science (AMS), where she has helped companies uncover critical customer insights to improve products, services, and customer experiences. Before moving to consulting she was in brand management at Unilever. More recently, she has collaborated with AI researchers at MIT to improve VOC outcomes. I regard Applied Marketing Science, Carmel’s company, as the thought leaders in VOC research, and it was the first organization to formalize the VOC interview process. In this discussion, we’ll explore how LLMs are revolutionizing Voice of the Customer analysis. Carmel will share results of experiments where AI not only matched human analysts in extracting customer insights but excelled at finding hidden needs – unmet needs that could unlock your next innovation opportunity and create competitive advantage. Whether you’re skeptical about AI in customer research or eager to embrace it, this discussion will challenge your assumptions about the future of Voice of the Customer analysis. The AI Revolution in Voice of the Customer Research Early AI Experiments (2017-2018) AMS began experimenting with artificial intelligence for customer research around 2017-2018. Their initial focus was on developing algorithms that could effectively analyze textual data and extract meaningful customer insights. However, these early efforts faced significant limitations. The AI could identify potentially useful information, but human analysts still needed to invest considerable time sifting through and making sense of what the AI had found. The process wasn’t yet efficient enough to deliver the time-saving benefits they hoped for. Recent Breakthroughs (2023) In 2023, AMS and MIT researchers tackled a more ambitious question: Could AI now effectively craft unmet customer needs statements that would be just as good as those created by experienced human analysts? To answer this question, they employed a technology called supervised fine-tuning. This approach involved “teaching” large language models how to craft clear, actionable customer needs statements based on transcript data, social media comments, and other text sources. Time PeriodResearch FocusAI ApproachLimitations2017-2018Extracting customer insights from textBasic AI algorithmsRequired significant manual effort to interpret results2023Crafting complete customer needs statementsSupervised fine-tuning of LLMsMuch improved but still best used alongside human analysis The supervised fine-tuning approach represented a significant advancement. Rather than simply flagging potentially relevant text, these newer AI models could produce fully formed needs statements that captured what customers truly wanted and why. This breakthrough laid the groundwork for the impressive results they observed in their comparative experiments between AI and human analysts. Validating AI Effectiveness in VOC Research The claim that AI could match or even exceed human performance in VOC research required solid evidence. During our conversation, Carmel described several experiments they conducted to validate the effectiveness of their AI approach. Experimental Approach One of their key experiments involved a blind testing methodology. They took authentic customer needs statements from previous VOC studies conducted by human analysts and mixed them with needs statements that the AI had generated. Then, they asked experienced human analysts to evaluate all the statements without knowing which were AI-generated and which were human-generated. The analysts evaluated each statement based on several criteria: Clarity – Was the need clearly articulated? Articulation quality – Was it well-expressed and understandable? Absence of hallucination – Was there any evidence of the AI inserting details that weren’t actually present in the customer data? Authenticity – How true was the need to what customers actually said? Surprising Results In these blind tests, the AI-generated needs statements performed just as well as—and in some cases better than—those crafted by human analysts. Key Benefits of AI-Powered Analysis Through these experiments, Carmel and her team identified several significant advantages of using AI for VOC research: BenefitDescriptionImpact on Product TeamsSpeedSignificantly accelerates the rate of gathering customer needsMore rapid product discovery and development cyclesVolume capacityNo practical limit on the amount of data that can be analyzedMore comprehensive understanding of customer needsMultiple data sourcesCan simultaneously analyze interviews, social media, forums, call center dataRicher, more diverse insights from various customer touchpointsReduced fatigueAI doesn’t experience the mental fatigue that affects human analystsConsistent quality throughout large datasetsReduced biasLess likely to have preconceived notions about what should be foundMore objective insights, potentially uncovering unexpected needs This last point about reduced bias is particularly important. As product managers, we sometimes unconsciously look for evidence that confirms our existing assumptions about customer needs or product direction. An AI system, properly implemented, doesn’t have these same motivations—it simply reports what it finds in the data. These benefits combine to create a more robust database of customer needs, which serves as the foundation for effective product innovation. The faster a product team can build this comprehensive understanding of customer needs, the more quickly they can move into solution development with confidence. AI’s Ability to Capture Emotional Needs One of the most surprising findings from AMS’s research was how effectively AI could identify emotion-infused customer needs. This discovery challenged a common assumption that machines would struggle with the emotional aspects of customer research due to their lack of human empathy. Why AI Excels at Finding Emotional Needs Humans conducting customer research are often unconsciously biased toward functional needs. As product professionals, we’re trained to identify problems and create solutions. We get rewarded professionally for finding practical issues that can be addressed with concrete features or improvements. This solution-oriented mindset can cause us to quickly move from emotional needs to functional needs to potential solutions. It’s simply how our professional brains are wired. AI, however, doesn’t have this bias. It gives equal weight to functional and emotional needs in customer data because it isn’t influenced by the pressure to jump to solutions. This creates a unique advantage in identifying the full spectrum of customer needs. Case Study: Wood Stains Category In analyzing customer feedback about staining furniture and wood products, the AI identified an emotional need that human analysts completely overlooked: Customers wanted “a manufacturer that values my feedback, will respond to my emails, and will address my concerns.” Human analysts had dismissed this as a generic desire that everyone would have, not recognizing it as a core need specific to this category. However, this need is particularly important for wood staining projects because customers often encounter problems and need responsive manufacturer support. The AI, without bias toward “important” functional needs, recognized this emotional need as significant based purely on the data. The Competitive Value of Emotional Needs There are three dimensions to any customer job: Functional – The obvious practical outcome the customer wants to achieve Emotional – How they want to feel (or avoid feeling) during and after the job Social – How they want to be perceived by others While functional needs are often the easiest to identify and address, emotional needs frequently drive purchasing decisions and brand loyalty. A product that connects strongly with customers’ emotional needs will typically outperform one that only addresses functional requirements. Even in highly functional categories like home heating and cooling systems, emotional needs like “feeling like a responsible homeowner” or “not feeling like I’m throwing money down the drain” are important to customer satisfaction. By leveraging AI to help identify these often-overlooked emotional needs, product teams can develop more holistic solutions that connect with customers on multiple levels, creating stronger competitive advantages. The Human-AI Partnership in VOC Research While the results from AI-powered analysis are impressive, Carmel emphasized that the most effective approach is using AI as a copilot rather than a complete replacement for human researchers. This partnership model leverages the strengths of both human expertise and AI capabilities to produce superior results. AI as a Copilot During our conversation, Carmel described how she views the relationship between human analysts and AI tools. The AI will find many things that humans overlook, but humans will also identify aspects that AI might miss. It’s similar to having two different analysts review the same data—each will notice different things. This copilot perspective emphasizes collaboration rather than replacement. Quantifiable Advantages of the Hybrid Approach The partnership between human researchers and AI creates measurable benefits for product teams. In their experiments, AMS examined the overlap between needs identified by humans and those identified by AI, creating a Venn diagram of findings. The results were eye-opening: When examining just the unique needs (those found by only one method), AI identified twice as many unique needs as human analysts AI didn’t suffer from the “I already heard that” fatigue that affects humans reviewing large datasets AI excelled at finding that “one new piece of data in a mountain of data” that might otherwise be missed These niche needs—ones that might be mentioned infrequently in customer feedback—are opportunities for innovation. As Carmel pointed out, the frequency with which a need is mentioned doesn’t necessarily correlate with its importance. Sometimes, these rarely mentioned needs represent the greatest opportunities for competitive differentiation. Case Study: Finding New Insights in Mature Industries To illustrate how the human-AI partnership can unlock unexpected value, Carmel shared an example from the snowplow industry. She worked with a client who had been in the snowplow and snow equipment business for 20 years—a veteran who deeply understood the industry. Initially, she wondered what new insights they could possibly discover for someone with such extensive experience. However, through their research process, they uncovered insights about visibility issues—specifically, that snowplows often have the worst visibility precisely when they need it most. These insights weren’t obvious even to industry experts, but they represented significant innovation opportunities. The client had never thought to ask certain questions, but the AI-augmented research process helped uncover these hidden needs. Breaking Through Subject Matter Expert Limitations AI-powered research can help overcome the limitations that come with deep domain expertise. When we’re very familiar with an industry, product, or customer base, we often make assumptions that limit our perspective. We might believe we’re in a commodity space or that we already understand all customer needs. In these situations, an outside perspective is valuable. Traditionally, this might come from new team members who aren’t constrained by industry conventions. Now, AI tools can provide a similar fresh perspective, almost like bringing in a consultant who specializes in finding customer needs across many different categories and can apply that expertise to your specific domain. Limitations of AI in VOC Research Despite the impressive capabilities of AI in VOC research, it’s important for product teams to understand its current limitations. During our conversation, Carmel highlighted several areas where human judgment and expertise remain essential. Equal Weighting of Data Sources One significant limitation is that AI algorithms like large language models tend to treat all data sources with equal weight. As Carmel explained, these tools currently can’t make sophisticated judgments about the relative validity or reliability of different data sources they analyze. This means that human expertise is still necessary for: Determining which data sources are worthy of the AI’s attention Evaluating the quality of inputs the AI is analyzing Deciding how much weight to give various pieces of feedback The AI is only as good as the data provided to it, so human selection and curation of input data is critical. The Continued Value of Direct Customer Conversations There’s still no substitute for real, live conversations with customers. While AI can extract tremendous value from interview transcripts and written feedback, direct customer interactions provide benefits that go beyond data collection: Benefit of Direct Customer InteractionWhy AI Can’t Replace ThisBuilding genuine customer empathyEmotional understanding comes from person-to-person connectionCapturing non-verbal cuesCurrent AI tools don’t analyze body language or vocal toneAsking follow-up questions in real-timeAI can’t yet dynamically probe based on subtle conversational cuesDeveloping organizational compassionTeams need direct exposure to customer challenges These human-to-human interactions develop deeper understanding within product teams that purely AI-mediated research might miss. The ideal approach combines direct customer conversations with AI-powered analysis of the resulting data. Prioritization Challenges Carmel also noted that AI currently can’t prioritize needs for innovation effectively. While AI excels at identifying the full spectrum of customer needs, it doesn’t yet have the capability to determine which of those needs represent the most valuable opportunities. The AI can identify all the needs that should go into a prioritization survey (what AMS calls “secondary needs”), but product teams still need to: Conduct surveys or other customer research to prioritize these needs Identify which needs are most important to customers Determine which needs are currently unmet or poorly met Decide which needs represent the best innovation opportunities This final step of prioritizing where to focus innovation efforts remains a human-driven process that requires business judgment, market understanding, and strategic thinking. Balancing AI Capabilities with Human Expertise Understanding these limitations helps product teams use AI tools more effectively. Rather than seeing AI as a complete replacement for traditional customer research methods, the most successful approach treats AI as one powerful tool in the product manager’s toolkit. By being realistic about what AI can and can’t currently do, product teams can design research processes that leverage the strengths of both AI analysis and human expertise, creating more comprehensive customer insights than either could achieve alone. Integrating AI into the Product Innovation Process With an understanding of both the capabilities and limitations of AI in VOC research, the next question becomes how product teams can effectively incorporate these tools into their innovation processes. Carmel shared several practical insights on this topic during our conversation. AI Throughout the Innovation Funnel Carmel described AI as providing product teams with “different lenses” to view customer needs. Unlike traditional research projects that often require extensive planning and formal structure, AI-powered approaches offer more agility and flexibility. Product teams can leverage AI at multiple stages of the innovation funnel: Innovation StageAI ApplicationBenefitsEarly discoveryBroadly identify customer needs across multiple data sourcesComprehensive understanding of the problem spaceFocus area explorationDig deeper into specific need areas identified as prioritiesRicher understanding of core underlying needsConcept testingAnalyze feedback on early conceptsRapid iteration based on customer responsesLater-stage validationVerify that solutions address original needsEnsuring alignment between solutions and customer needs Most Useful Application: Beginning of Innovation While AI can provide value throughout the innovation process, Carmel emphasized that its most useful application is at the very beginning—the discovery phase where teams are trying to understand the landscape of customer needs before diving into solutions. This aligns with best practices in product management, where thorough understanding of customer problems should precede solution development. AI can help product teams build this foundation more quickly and comprehensively than traditional methods alone. Filling Gaps in the Process AI can fill gaps when the ideal innovation process hasn’t been followed. In practice, product development doesn’t always follow a perfect linear path: Sometimes teams jump directly to solutions without thorough needs identification Sometimes they develop concepts first, then try to determine the right messaging Sometimes they’re already far along in development when they realize they need to verify customer needs In these situations, AI can quickly analyze customer data to ensure teams haven’t missed anything foundational before proceeding to later stages. The speed and efficiency of AI analysis makes it a highly agile tool for course correction. Broadening Innovation Horizons Another benefit of integrating AI into the product innovation process is how it can help teams break out of established patterns of thinking. By identifying needs that might be overlooked in conventional analysis, AI can point product teams toward unexpected innovation opportunities. This is particularly valuable in mature product categories or for teams working with products they’ve managed for a long time. The AI can help challenge assumptions and reveal new possibilities that might not have been considered. Practical Implementation of AI-Powered VOC Understanding the potential of AI in VOC research is one thing, but knowing how to practically implement these tools is another challenge entirely. During our conversation, Carmel provided insights into how product teams can begin leveraging these capabilities today. Current State: Client Service Model Currently, AMS is offering AI-powered VOC as a service to their clients. This approach allows product teams to benefit from advanced AI analysis without needing to develop the expertise or tools internally. Carmel explained that their goal is to help clients get insights faster and more efficiently than ever before. This service model makes sense given the specialized nature of the AI tools being used. The large language models employed by AMS have been fine-tuned with supervised learning based on 1,500 carefully selected customer needs from multiple case studies. This specialized training creates AI systems that are specifically optimized for VOC research rather than general-purpose AI. Benefits for Product Teams Implementing AI-powered VOC research, whether through a service provider like AMS or eventually through internal capabilities, offers several practical benefits for product teams: BenefitReal-World ImpactAccelerated insightsReducing research timelines from weeks to daysMore comprehensive analysisIdentifying needs that would be missed in traditional analysisGreater agilityAbility to quickly adapt research focus as project needs evolveBetter resource allocationFreeing up human analysts for higher-value strategic workCross-source integrationCombining insights from interviews, social media, support tickets, etc. Tailoring Implementation to Product Complexity Carmel noted that the level of detail needed from AI-powered analysis varies based on the complexity of the product and its development stage. Not all products require the same depth of customer needs exploration: Higher complexity products may need comprehensive needs identification Products already in development might need targeted analysis to verify assumptions Products nearing launch might need focused research on messaging alignment with needs The flexibility of AI-powered approaches allows teams to adjust the scope and depth of analysis to match their specific situation, making this a highly adaptable tool for diverse product development contexts. Conclusion The integration of AI into Voice of the Customer research represents a significant advancement for product teams seeking to better understand and address customer needs. These tools aren’t replacing human researchers but rather enhancing their capabilities, helping teams discover more customer needs—particularly emotional ones—faster and more comprehensively than traditional methods alone. The ability to process diverse data sources without fatigue or bias opens new possibilities for innovation, especially in mature product categories where fresh insights can be challenging to find. By leveraging AI to help us better understand customer needs—both functional and emotional—product teams can create solutions that connect more deeply with customers, driving both satisfaction and competitive advantage. Useful Links Register for the Supercharge Your VOC Webinar on March 5th, 2025, with Carmel Dibner and Artem Timoshenko Stay up-to-date on upcoming AMS workshops Connect with AMS on Instagram Innovation Quote “Simplicity is the ultimate sophistication.” – attributed to Leonardo da Vinci Application Questions How could you use AI as a “copilot” in your current customer research process? Consider specific points where AI analysis might complement your team’s human analysis and help identify needs that might otherwise be overlooked. What emotional needs might your customers have that your team hasn’t fully explored? How could you use AI tools to help uncover these less obvious but potentially valuable insights? How might your team’s subject matter expertise be creating blind spots in your understanding of customer needs? What “fresh perspective” could AI bring to challenge your established assumptions? Where in your product innovation funnel could AI-powered customer research create the most value? Consider both early-stage discovery and later validation activities. What data sources about your customers do you already have that could be analyzed more comprehensively with AI tools? Think beyond interviews to include support tickets, reviews, social media comments, and other text-based feedback. Bio Carmel Dibner is a principal in the Insights for Innovation practice at Applied Marketing Science (AMS) where she is responsible for client relationships, client service delivery, and business development. She has worked closely with researchers at the MIT Sloan School of Management to experiment with new AI techniques and has successfully applied machine learning to answer her clients’ most difficult research questions. These techniques became the basis for a research study for Boston Children’s Hospital that was awarded a 2023 Quirk’s Marketing Research and Insight Excellence Award in the Health Care/Pharmaceutical Research Project category. More recently, she’s collaborated with researchers from MIT and Northwestern University’s Kellogg School of Management on next generation AI. She regularly presents at leading industry conferences such as the Front End of Innovation Continued and The Market Research Event Continued. Carmel is passionate about the intersection of psychology and business. She holds a Bachelor of Arts in Psychology and Sociology with a Business and Organizations concentration from Cornell University. She also holds an Masters in Business Administration in Marketing and Management from The Wharton School of the University of Pennsylvania, where she was named a Palmer Scholar. Thanks! Thank you for taking the journey to product mastery and learning with me from the successes and failures of product innovators, managers, and developers. If you enjoyed the discussion, help out a fellow product manager by sharing it using the social media buttons you see below. Source

How an AI-powered fashion startup achieved product-market fit Watch on YouTube TLDR In this episode, we’re joined by Anya Cheng, former product leader at Meta, eBay, McDonald’s, and Target, and current founder of the AI-powered fashion startup Taelor. Her journey from corporate product management to successful startup founder offers valuable lessons for product managers and innovators. The key message: Focus on solving one problem exceptionally rather than competing on multiple features. Key Topics Why successful products often start with a single powerful feature How to discover and validate product-market fit through deep customer research The importance of balancing customer feedback with your own product vision Ways to create value on both customer and supplier sides of your business Lessons from Target’s iPad app success through radical feature minimization The value of breaking out of your industry bubble for innovation insights Introduction We all face numerous challenges creating products customers love—understanding the customer and their unmet needs, achieving product-market fit, working with stakeholders, scaling the product in the marketplace, and more. Today we’ll learn how to overcome some of those challenges from a product leader with experience at Target, McDonalds, eBay, and Meta, and now as Founder and CEO of Taelor. Our guest, Anya Cheng, founded Taelor, combining her leadership experience at B2Cs and her knowledge of tech product management, to make it easy for men to wear stylish clothes for any occasion. Anya also is mentor at 500 Startups and a teacher of product management for Northwestern University.  A Fresh Perspective on Product Development Anya challenged common assumptions about product development strategy. Instead of advocating for feature-rich products or complex innovation frameworks, she emphasized the power of solving one problem exceptionally. This approach has informed her success across different industries and roles, from retail to technology. Core Product Development Challenges: The Power of Single-Feature Focus Anya highlighted a mistake many startups and product teams make: trying to compete with established companies by matching or exceeding their feature lists. The Feature Competition Trap During her work mentoring product managers and startups, Anya noticed a recurring pattern. Teams often pitch their products by comparing them to industry giants: “It’s like Uber, but with these extra features” or “It’s Amazon, plus these additional capabilities.” The problem? This approach fundamentally misunderstands how successful products actually emerge and grow. Large companies can quickly replicate individual features, making it nearly impossible for smaller players to compete on feature quantity. Instead, a startup should focus on solving one problem better than anyone else. Historical Examples of Successful Single-Feature Products To illustrate the power of focused problem-solving, Anya shared three examples: Company Initial Core Feature Outcome Google Simple search bar Outperformed Yahoo’s comprehensive portal YouTube Video upload and sharing Acquired by Google Instagram Photo filters Acquired by Meta The Key to Standing Out The lesson Anya learned from both her corporate experience and startup journey is clear: Success comes from doing one thing exceptionally rather than doing many things adequately. She shared wisdom from her mentors, including founders of Rotten Tomatoes and YouTube, who emphasized that startups should focus on having “one giant check mark” instead of many small ones. This approach requires: Identifying a specific, valuable problem to solve Developing a focused solution that addresses that problem better than any alternative Resisting the temptation to add features just because competitors have them Maintaining focus on core value proposition during growth Finding Product-Market Fit: The Taelor Case Study The journey to product-market fit often begins with a personal pain point, but successful products emerge when founders look beyond their own experiences. Anya’s development of Taelor offers valuable lessons in how to validate and expand upon initial product insights. From Personal Problem to Market Opportunity Anya found herself wanting to look more professional but discovered existing clothing services weren’t designed for busy professionals who prioritized efficiency over fashion. This led her to explore whether others faced similar challenges. Through market research, she discovered her ideal customers weren’t whom she initially expected. While her original problem centered on professional women’s clothing needs, her research revealed a more acute pain point among busy professional men who: Need to look good for various occasions Don’t enjoy shopping Want to save time on clothing decisions Prefer efficiency over fashion exploration Developing the Value Proposition Based on this customer understanding, Taelor evolved into a comprehensive solution combining several key elements: Service Component Customer Benefit AI-Powered Recommendations Personalized outfit selections based on schedule and preferences Monthly Subscription Regular rotation of 10 clothing items Professional Styling Expert guidance without requiring fashion knowledge Laundry Service Elimination of cleaning and maintenance hassles Validating the Model Anya explained that although Taelor sells clothes, their real value proposition is helping customers save time while getting ready for the week.  The service attracted a surprisingly diverse customer base, ranging from 16 to 85 years old, including professionals across various industries – from sales executives to pastors. This broad appeal confirmed that the core problem (wanting to look good without spending time on fashion) resonated across demographics. Taelor’s customers pay a monthly fee and receive ten clothing items per month. They answer some questions about their current favorite clothes and clothing issues. They can upload a picture, get a 15-minute free consultation with a human stylist, and provide information about special events they’re dressing for each month. An AI uses this information to pick five clothing items that are mix-and-matched into two to three outfits, which are shipped to the customer. They can wear these clothes for two weeks, mail the dirty clothes back, and keep any pieces they want. The business model created multiple value streams: Monthly subscription fees for the core service Purchase options for items customers particularly like Significant discounts (30-70% off) on pre-owned items Thorough customer research and iterative development can transform a personal insight into a viable product with broad market appeal. The key was moving beyond initial assumptions to discover and validate deeper customer needs. Customer Research and Product Evolution: Uncovering Hidden Opportunities Anya emphasized how continuous customer research revealed unexpected opportunities and shaped Taelor’s product evolution. Her approach to gathering and acting on customer insights offers valuable lessons for product managers at any stage. Multi-Channel Research Approach Rather than relying on traditional surveys alone, Taelor implemented a comprehensive research strategy that combined multiple touchpoints: Research Channel Insights Gained Impact on Product Style Quiz Initial fit preferences and style comfort zones Improved AI recommendations Stylist Consultations Detailed customer concerns and aspirations Enhanced personalization features Direct Customer Communication Real-time feedback and emerging needs New service opportunities identified Post-Use Feedback Product quality and fit issues Refined clothing selection process Emerging Customer Needs This deep customer engagement revealed several unexpected opportunities for service expansion: Gift recommendations for special occasions Style guidance beyond clothing (haircuts, accessories) Corporate partnership opportunities for employee benefits Educational institution collaborations for student interview preparation The Human Touch in AI-Driven Products Taelor decided to maintain human stylists alongside AI capabilities. While they could have automated all customer communications, they discovered that human interaction provided invaluable product development insights. This human element helped them: Understand nuanced customer needs that might not appear in data Identify patterns in customer requests before they become trends Build stronger relationships with customers Gather qualitative feedback that improved their AI systems Strategic Partnerships The research also led to strategic partnership opportunities that expanded the product’s value proposition. These included: Collaborations with women’s rental companies for gift services Partnerships with dating apps for style preparation Corporate programs for employee benefits Educational institution partnerships for student career preparation This systematic approach to customer research and product evolution demonstrates how careful attention to customer feedback can reveal new opportunities while strengthening the core value proposition. Supply Side Innovation: Solving Industry-Wide Challenges Anya revealed how Taelor’s innovation extends beyond customer service to address significant industry challenges. This dual-sided approach to product development offers lessons for product managers about finding opportunities in industry-wide problems. Addressing Fashion Industry Waste The fashion industry faces two challenges that Taelor’s  business model helps address: Industry Challenge Scale of Impact Taelor’s Solution Direct-to-Landfill Waste 30% of new clothes Rental model extending clothing lifecycle Carbon Emissions 10% from fast fashion Sustainable clothing rotation system Creating Value for Suppliers What makes Taelor’s approach particularly innovative is how it transforms these industry challenges into opportunities for brands. The platform serves as: A real-world testing ground for new designs A direct feedback channel from customers A solution for inventory management A market research platform for future collections Market Testing Innovation Anya shared how Taelor has become a valuable testing platform for clothing brands in several ways: Testing Aspect Brand Benefit Pre-Release Testing Real customer feedback before full production Durability Assessment Quality testing through multiple wears and washes Fit Verification Customer feedback on specific design elements Market Validation Early indicators of design popularity Breaking Traditional Distribution Models The platform also solves a significant challenge for new brands: finding their initial customer base. Anya explained how traditional distribution channels often involve multiple intermediaries, resulting in: Delayed feedback from end customers Limited understanding of customer preferences Reduced ability to iterate on designs Increased inventory risk By providing direct access to customers and immediate feedback, Taelor helps brands make more informed decisions about their products while reducing waste and improving sustainability. This demonstrates how innovative product development can create value for multiple stakeholders while addressing larger industry challenges. Key Product Management Lessons: Trust, Testing, and Breaking Bubbles Anya shared several pivotal lessons from her product management journey that challenge conventional wisdom. These insights, drawn from both successes and failures, offer valuable guidance for product managers at any stage of their careers. Trust Your Product Knowledge Anya shared a story about her early pitching experiences that illustrated a common product manager challenge. In preparing for startup competitions, she initially focused on memorizing answers to potential questions, consulting 42 former judges. However, this over-preparation actually hindered her ability to respond authentically and confidently. Key lessons she learned about product leadership: Value your deep product knowledge as the product’s “parent” Trust your understanding developed through daily engagement Balance external input with your own expertise Respond authentically rather than reciting prepared answers Think Beyond Utility One of Anya’s most significant realizations came after transitioning from corporate to startup environments. She discovered that successful product development requires thinking beyond pure utility: Move beyond the corporate “usefulness” mindset Embrace curiosity about different perspectives Seek insights from unexpected sources Break out of industry and professional bubbles Value of Diverse Perspectives Anya emphasized how her most valuable insights often came from unexpected sources: Successful entrepreneurs outside tech Professionals from different industries Customers with unique use cases Team members with diverse backgrounds Successful product management isn’t just about following established frameworks – it’s about developing the judgment to know when to trust your instincts, question assumptions, and seek diverse perspectives. Conclusion Throughout my conversation with Anya Cheng, one theme consistently emerged: Successful product development isn’t about matching competitors feature-for-feature or trying to solve every customer problem. Instead, it’s about identifying a significant problem, solving it exceptionally, and having the discipline to maintain that focus even as your product grows. Her journey from leading product teams at major tech companies to founding Taelor demonstrates how this principle applies across different scales and industries. For product managers and innovators, the lessons from Anya’s experience offer a refreshing perspective on product development. Whether you’re working in a large corporation or leading a startup, success comes from deeply understanding your customers, questioning your assumptions, embracing diverse perspectives, and having the courage to maintain a singular focus on solving one problem exceptionally. Useful Links Connect with Anya on LinkedIn Check out Taelor and use code global30 to get 30% off your first month of subscription or globalgitft10 to get 10% off a gift card Innovation Quote “A great product solves a real problem with a singular, powerful value proposition.” – Anya Cheng Application Questions How could you simplify your current product down to one “giant check mark” – a single feature or capability that solves a crucial problem better than any alternative? What would you need to remove or change to achieve this focus? How could your team expand your customer research methods beyond traditional approaches? Consider Anya’s example of having customer service representatives purposefully engage with users to gather product development insights. What new channels could you create for deeper customer understanding? Looking at your product’s recent feature additions, how many were driven by competitor comparison versus genuine customer needs? How could you reframe your feature prioritization process to focus more on solving specific customer problems exceptionally? How could you break out of your industry bubble to gain fresh perspectives on your product challenges? What unexpected sources or diverse viewpoints could you engage with to inspire innovation in your product development process? Consider the Target iPad app example: How could your team apply radical feature minimization to your product? What would happen if you reduced your product to just two core capabilities that align perfectly with your key value proposition? Bio Anya Cheng is the Founder and CEO of Taelor, a leading men’s clothing subscription service that provides personal styling and curated rentals, powered by expert stylists and AI. A Girls in Tech 40 Under 40 honoree, she previously led eCommerce and digital innovation teams at Meta, eBay, Target, and McDonald’s. Experience effortless style with rental and personal styling services: Taelor: Get 25% OFF your first month of men’s clothing subscription. Use code: PODCAST25 Sign up at: https://taelor.style/pages/membership Armoire: Get 50% OFF your first month of women’s clothing subscription. Use code: ArmoirexTaelor Sign up at: https://www.armoire.style/refer/ArmoirexTaelor Give the gift of time, convenience, and effortless style: Taelor Gift Cards: Get 10% OFF Use code: PODCASTGIFT Purchase at: https://taelor.style/products/menswear-rental-gift-card   Thanks! Thank you for taking the journey to product mastery and learning with me from the successes and failures of product innovators, managers, and developers. If you enjoyed the discussion, help out a fellow product manager by sharing it using the social media buttons you see below. Source

The product manager’s path to influence Watch on YouTube TLDR In my recent conversation with Francesca Cortesi, CPO, we explored why the popular phrase “CEO of the product” can be misleading for product managers. Francesca explained that while this concept aims to emphasize ownership and decision-making authority, successful product management actually requires a different approach. Instead of acting as a sole decision-maker, today’s product managers need to excel at facilitation, stakeholder collaboration, and building trust across teams. She shared insights from her experience leading product teams at various organizational scales and helping companies transform their product vision into measurable business growth. Key Topics The evolution from the traditional “decision-maker” model to a modern “facilitator” approach in product management How product management roles and responsibilities vary across different organization sizes and contexts Essential skills for product management success: trust building, stakeholder management, and collaborative leadership Setting clear role boundaries and expectations in product management positions Practical strategies for navigating product management challenges while maintaining customer focus Career development considerations for product managers, including different growth paths and role transitions Introduction Ever heard someone call product managers “CEO of the product”? It’s a catchy phrase that has attracted some people to product management, but it certainly doesn’t tell the whole story. In today’s discussion we’ll break down where the CEO comparison holds up, where it falls short, and most importantly what makes product management a uniquely challenging and rewarding role in its own right. Along the way we’ll explore how the role varies across different organizations and discuss the critical skill of defining and managing the boundaries of that product management role. If you’re considering how to grow your product management career and your influence, this episode will give you some clarity about what success really looks like in this field. Our guest is Francesca Cortesi, CPO and consultant for some of Europe’s multi-billion dollar brands and fastest growing businesses. Recently, at Hemnet, Sweden’s beloved property platform, she led product development that drove a 130% increase in top line revenues, making it the growth engine of the business. She now runs her own consultancy, helping CEOs scale their companies by transforming product vision into measurable business growth. The Reality Behind “CEO of the Product”: Redefining the Product Manager’s Role The “CEO of the product” concept emerged when product management literature was scarce, and professionals often had to figure out their roles with limited guidance. Early in her career, Francesca encountered this phrase alongside the common description of product management as sitting at the intersection of business, user experience, and technology. While this description aimed to emphasize ownership and agency in decision-making, it created some misconceptions about the role. Traditional View Modern Reality Product manager as primary decision maker Product manager as skilled facilitator Information flows through PM as central point PM enables direct cross-functional collaboration Focus on authority and control Focus on influence and alignment Today’s product management landscape has evolved significantly. While the role still requires strong leadership skills, the most successful product managers approach their work differently than what the “CEO of the product” phrase might suggest. Instead of focusing on authority and control, they excel at facilitation, stakeholder collaboration, and building trust across teams. This shift reflects a deeper understanding of how successful products are built in modern organizations. As we’ll explore in this article, effective product management requires a unique blend of skills that goes beyond traditional leadership models. Whether you’re an aspiring product manager, a seasoned professional, or a leader developing your product team, understanding these nuances is necessary for success in today’s product landscape. The Evolution of Product Management Understanding Francesca shared how her understanding of product management transformed over her career, moving from a traditional decision-maker model to a more nuanced facilitator approach. This evolution offers valuable insights for product managers at all career stages. The Traditional Decision-Maker Model: A Limited View Early in her career, Francesca interpreted the “CEO of the product” concept literally, believing her primary role was to make decisions. She positioned herself at the intersection of different functions, collecting input from business stakeholders to define problems, then transmitting requirements to development teams to create solutions. This approach, while common, created several challenges: Information bottlenecks formed when all communication flowed through the product manager Stakeholders became disconnected from understanding product development complexities Development teams lost direct insight into business context and customer needs The product manager’s personal biases could inadvertently filter or alter important information The Modern Facilitator Model: A Better Approach As Francesca’s experience grew, she discovered that effective product management requires a different mindset. The role isn’t about being the sole decision-maker, but rather about: Key Responsibility Implementation Approach Facilitating Discussions Creating spaces for direct dialogue between stakeholders Identifying Decision Makers Understanding who is best positioned to make specific decisions Driving Progress Keeping initiatives moving forward through collaboration Building Understanding Helping teams grasp complex business and technical contexts This evolved understanding acknowledges that no single person, even a CEO, can be an expert in all areas. Instead, successful product managers excel at bringing together diverse perspectives and expertise to create better outcomes. This shift isn’t about abdicating responsibility. Rather, it’s about recognizing that the most effective product decisions emerge from collaborative processes where all stakeholders can contribute their expertise directly. This approach leads to better solutions and stronger buy-in from teams responsible for building and supporting the product. The facilitator model also addresses a common challenge in product management: the need to drive progress without direct authority over many of the people involved. By focusing on facilitation rather than control, product managers can maintain momentum while building the trust and relationships necessary for long-term success. This evolution in understanding reflects broader changes in how modern organizations approach product development. As products become more complex and teams more specialized, the ability to facilitate effective collaboration becomes increasingly valuable. Key Motivations and Misconceptions in Product Management Throughout my years of teaching and practicing product management, I’ve asked hundreds, if not thousands, of product managers why they chose this career path. During my conversation with Francesca, we explored these common motivations and the misconceptions that often accompany them. Why People Choose Product Management Three primary motivations consistently emerge when people discuss their attraction to product management: Motivation Reality Challenge Creating Customer Value Direct impact on solving customer problems Balancing customer needs with business constraints Organizational Influence Ability to shape product direction Learning to influence without authority Strategic Overview Understanding the bigger picture Managing competing priorities and perspectives Common Misunderstandings About the Role Francesca highlighted several misconceptions she encountered when working with and mentoring product managers: Many believe becoming a product manager automatically grants decision-making authority Some think their job is to have all the answers rather than facilitate finding solutions Product managers often struggle with undefined boundaries of their role These misconceptions can lead to frustration when new product managers encounter the reality of the role. Francesca shared that she often heard product managers say, “We can just solve this because I’m the CEO of the product,” not realizing that the product is much bigger than any individual’s authority. Bridging the Gap Successfully navigating these misconceptions requires understanding that: 1. Influence and authority are different skills 2. Product success depends on collaborative decision-making 3. Role boundaries vary significantly by organization 4. Leadership doesn’t always mean management Many professionals enter product management from technical backgrounds, such as engineering or development. While these backgrounds provide valuable technical knowledge, they don’t always prepare individuals for the people-focused aspects of product management. This transition often requires developing new skills and sometimes discovering whether you actually enjoy the highly collaborative nature of the role. This understanding leads to an important question that Francesca posed: “How do we know that this is what we like?” Not everyone who excels at technical work will enjoy or excel at the collaborative, facilitative aspects of product management. Recognizing this early can help professionals make better career choices and find roles that align with their strengths and preferences. Essential Skills for Product Management Success My discussion with Francesca revealed that success in product management hinges on two key competencies: building trust capital and mastering stakeholder management. These skills form the foundation for effective product leadership, regardless of organization size or industry context. Building Trust Capital A key element for product management success is trust capital—how much you’re able to make people around you trust you. Francesca explained that trust capital comes from two primary sources: Trust Component How to Develop It Why It Matters Deep Field Understanding Immerse in industry knowledge, market dynamics, and technical aspects Enables credible participation in strategic discussions People Skills Focus on empathy, communication, and relationship building Creates foundation for effective collaboration A significant insight Francesca shared was her evolution from trying to convince others of her ideas to truly empathizing with their perspectives. Early in her career, she focused on building compelling arguments to win support for her decisions. However, she discovered that success comes from understanding where others see opportunities and why they prioritize certain approaches over others. Mastering Stakeholder Management Effective stakeholder management requires a proactive approach. Francesca outlined several key strategies: Early Involvement: Engage stakeholders at the beginning of initiatives rather than presenting finished solutions Collaborative Ownership: Help stakeholders feel the solution is theirs by incorporating their input meaningfully Cross-functional Alignment: Ensure marketing, sales, and customer support believe in the product direction This approach doesn’t mean everyone needs input on every decision. Instead, it means identifying key stakeholders and involving them at appropriate points in the process. From Convincing to Collaborating The shift from convincing to collaborating represents a fundamental change in how product managers approach their role. Instead of preparing perfect presentations for the boardroom, successful product managers: 1. Hold preliminary discussions to understand concerns and perspectives 2. Build consensus through ongoing dialogue 3. Incorporate diverse viewpoints into product strategy 4. Create shared ownership of solutions This collaborative approach yields several benefits: Benefit Impact Stronger Solutions Multiple perspectives lead to more robust product decisions Better Buy-in Early involvement creates natural advocates for the product Faster Implementation Aligned teams move more quickly and effectively Sustainable Success Collaborative wins create foundation for future cooperation Francesca noted that these skills become increasingly important as product managers advance in their careers. Whether moving toward senior individual contributor roles or people management positions, the ability to build trust and manage stakeholders effectively remains crucial for success. The Varying Nature of Product Management Roles Product manager occupy various roles across different organization, so it is important for them to understand their specific context and adapt their approach accordingly. Impact of Organization Size The size of an organization significantly influences the scope and nature of product management roles: Company Stage Role Characteristics Key Challenges Startup Broad responsibilities, including customer support Wearing multiple hats while maintaining focus Scale-up (100-200 people) More defined role with clearer boundaries Establishing processes while maintaining agility Enterprise Specialized focus on specific features or products Navigating complex organizational structures Context-Specific Variations Francesca highlighted two distinct contexts that shape product management roles: 1. Product-as-Company Environment Digital-first businesses where the product is central Clear connection between product metrics and business success Direct influence on company revenue and growth 2. Product-as-Channel Environment Traditional businesses with digital components Product serves as one of multiple channels Need to align with broader business strategies For example, Francesca contrasted a digital marketplace with a luxury retailer like Gucci. While the marketplace’s product team directly drives business success, Gucci’s digital product team supports a broader retail strategy where physical products generate most revenue. Organizational Considerations The role’s scope can vary dramatically based on organizational structure: Scope Type Description Example Full Product Ownership of entire product or product line Complete marketplace platform Feature Focus Responsibility for specific functionality Search feature in office software Channel Management Digital presence for traditional business E-commerce platform for retailer Francesca noted that these differences often relate more to company stage than industry type. As organizations grow, product management roles typically become more specialized and focused on smaller components of the overall product strategy. Managing Internal Competition An important challenge Francesca highlighted was managing internal competition between products or channels. Product managers must navigate situations where: 1. Different products target overlapping market segments 2. New digital channels might cannibalize traditional sales 3. Multiple teams compete for the same resources 4. Various stakeholders have conflicting objectives Success in these situations requires strong stakeholder management skills and regular engagement with key decision-makers to understand their objectives and help them achieve their goals while maintaining product focus. Setting Clear Role Boundaries Francesca emphasized how important it is to establish clear boundaries and expectations in product management roles, especially as organizations scale and evolve. Establishing Expectations Francesca recommended conducting expectation workshops, particularly during key organizational transitions. These workshops should address: Expectation Area Key Questions to Address Why It Matters Role Definition What specific responsibilities fall under the PM role? Prevents scope creep and role confusion Success Metrics How will performance be measured? Aligns efforts with business goals Support Needs What resources are needed for success? Ensures proper enablement Stakeholder Engagement Who are the key stakeholders and how often to engage? Sets communication standards Managing Role Evolution As organizations grow, product management roles often shift dramatically. Francesca highlighted several key considerations: Different roles require different skills (individual contributor vs. management) Career progression isn’t always linear The higher you go, the less direct product work you do Not everyone wants or needs to become a people manager Creating Clear Guidelines Francesca recommended being explicit about expectations in several key areas: 1. Customer Interaction Required frequency of customer contact Expected depth of market understanding Methods for gathering customer feedback 2. Business Understanding Knowledge of revenue models Understanding of key metrics Grasp of market dynamics 3. Stakeholder Management Required meeting cadence Communication expectations Decision-making processes Adapting to Change Organizations need to revisit role boundaries regularly, especially during: Transition Point Required Actions Company Growth Phases Redefine roles and responsibilities Leadership Changes Align on new expectations Strategy Shifts Update success metrics Team Expansion Clarify reporting structures What works in one company or context might not work in another. Product managers often join new organizations with expectations based on previous experiences, which might not align with their new environment. Regular expectation alignment helps prevent confusion and ensures everyone understands their role in driving product success. Call out unreasonable expectations, such as excessive stakeholder meetings that prevent meaningful customer interaction. The key is approaching these discussions with solutions rather than complaints, showing how adjustments could improve overall impact. Practical Advice for Product Managers Francesca shared valuable insights for product managers navigating their roles, drawing from her experience both as a product leader and consultant to major European brands. Her advice focused on practical approaches to common challenges and sustainable professional development. Navigating Role Challenges When facing common product management challenges, Francesca recommended several key strategies: Challenge Solution Approach Expected Outcome Too Many Stakeholder Meetings Analyze impact of activities and propose alternatives More time for high-value work Unclear Decision Authority Define decision-making frameworks with leadership Faster progress on initiatives Limited Customer Interaction Make customer contact non-negotiable Better product decisions Execution Pressure Validate assumptions before full implementation Reduced risk of failure Avoiding Common Pitfalls Francesca highlighted several modern product management pitfalls to watch for: Following industry best practices without considering context Implementing frameworks without understanding their purpose Prioritizing execution speed over proper validation Losing touch with customer needs due to internal focus Professional Development Strategies For ongoing growth, Francesca recommended focusing on: 1. Context-Aware Learning Study industry best practices but adapt them to your environment Understand why certain approaches work in specific contexts Learn from both successes and failures 2. Expectation Management Regularly align with managers on priorities Document and track role expectations Proactively address misalignments 3. Impact Measurement Area Measurement Approach Value Creation Track key product metrics and customer outcomes Team Effectiveness Monitor collaboration quality and decision speed Stakeholder Satisfaction Regular feedback and alignment checks Customer Understanding Depth and frequency of customer insights Maintaining Focus on What Matters Product managers should: 1. Prioritize customer insights over internal politics 2. Focus on facilitating good decisions rather than making all decisions 3. Build collaborative relationships across the organization 4. Keep learning and adapting their approach as contexts change While books and frameworks provide valuable guidance, success comes from understanding your specific context and adapting best practices accordingly. Maintain a learning mindset while staying focused on creating value for customers and the business. Conclusion As product management continues to mature as a discipline, we’re seeing a shift away from the oversimplified “CEO of the product” concept toward a more nuanced understanding of the role. Success comes not from authority or control, but from the ability to facilitate collaboration, build trust, and maintain focus on creating value for customers. The most effective product managers embrace this evolution, recognizing that their impact comes not from making every decision, but from enabling better decisions through collaboration and shared understanding. By focusing on building trust capital, managing stakeholder relationships effectively, and adapting their approach to specific organizational contexts, product managers can drive success while maintaining enthusiasm through the inevitable challenges along the way. Useful Links Check out Francesca’s website to learn about her mentorship service and to get free resources Connect with Francesca on LinkedIn Innovation Quote “Success is being able to go from failure to failure without losing your enthusiasm.” – Winston Churchill Application Questions Which decisions about your product are you currently acting as a bottleneck for? How well do your current role boundaries and expectations align with your organization’s needs? How could your team benefit from improved stakeholder management? How could you build stronger trust capital in your organization? What internal activities could you eliminate or delegate to create more space for customer understanding? Bio With over a decade of product leadership experience, Francesca Cortesi knows what it takes to turn big ambitions into real, scalable outcomes. She specializes in helping growing companies go beyond market fit and scale sustainably, focusing on clear strategies, practical frameworks, and fostering strong collaboration across teams. Drawing from her experience as Chief Product Officer and Head of Product, Francesca enables businesses to drive results that matter—for both the business and its customers. A passionate advocate for human-centered leadership, she shares insights through speaking and thought leadership, helping founders and teams navigate the exciting (and messy!) journey of scaling. Thanks! Thank you for taking the journey to product mastery and learning with me from the successes and failures of product innovators, managers, and developers. If you enjoyed the discussion, help out a fellow product manager by sharing it using the social media buttons you see below. 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Serial innovators see dead people Watch on YouTube TLDR The transformation of Olay from a declining “Oil of Old Lady” brand into a market-leading skincare innovator offers valuable lessons for product managers and innovation leaders. Through deep consumer research, strategic pricing, and holistic product development, P&G’s Nancy Dawes led a team that created an entirely new market category of “mass-prestige” skincare products. The success of this transformation hinged on understanding consumer psychology, developing innovative technology, and carefully positioning the product between mass market and luxury price points. Key Topics Characteristics and strategies of successful serial innovators in product development Comprehensive approach to consumer research and insight gathering Strategic product positioning and pricing in the mass-prestige market Integration of technical innovation with consumer psychology Organizational navigation techniques for innovative product managers Holistic approach to product development and brand transformation Market validation and testing strategies for premium products Cross-functional expertise development for product innovation Introduction Remember when Pringles was just another potato chip, or when Olay was losing its shine in the cosmetics aisle? If you’ve ever wondered how struggling brands transform into market leaders, you’re about to get a masterclass in product innovation and consumer insight. Today, we’re joined by Nancy Dawes, a legendary force in product transformation who tripled Pringles sales and breathed life into the Olay brand by creating new product lines. She was Proctor & Gamble’s first female engineer to be honored as a Victor Mills Society Research Fellow. Nancy has also been recognized as a Serial Innovator—featured in the book Serial Innovators: How Individuals Create and Deliver Breakthrough Innovations in Mature Firms. She spent 38 years at P&G mastering the art of understanding what customers want before they know they want it. After retiring from P&G, Nancy continues to guide founders and entrepreneurs in creating products customers love and also volunteers with Ohio State College of Engineering and Girl Scouts of Western Ohio. Whether you’re leading a product team at a Fortune 500 or founding a startup, Nancy’s proven approach for uncovering consumer insights and driving breakthrough innovation could be the difference between your product’s decline and its dramatic comeback. Serial Innovation in Product Development Nancy characterized serial innovators as those who: conceive new ideas for products that solve problems develop those ideas into breakthrough new products and services invent technologies as needed along the way guide those products into the market for commercial success Serial innovators solve important consumer problems, and often figuring out the right problem is just as important as fixing it. They invent new technologies to support their solutions and follow their products into the marketplace rather than handing them to someone else. The Olay Transformation: A Product Innovation Case Study Nancy told the story of how Olay transformed from a struggling brand, called “Oil of Old Lady” by some customers, to a market leader through strategic product innovation. The story begins in 1985 when P&G acquired Olay, which was then known as Oil of Olay. By 1995, when Nancy joined the project, the brand had declined by approximately 50% in value. Market Analysis and Opportunity Recognition Nancy identified four factors that created the perfect environment for transformation: Demographic Opportunity: 75 million Baby Boomer women were entering their prime skincare years, ready to reinvent aging Technology Evolution: The emergence of alpha hydroxy acids moving from professional to consumer products Corporate Support: Strong leadership commitment to winning in the skincare category Expertise Alignment: Right combination of technical knowledge and consumer understanding Initial Assignment vs. Strategic Vision Nancy’s original assignment was simply to create a superior facial moisturizer, but Nancy recognized that just having a better product wasn’t enough for success and it wasn’t really what the women who were buying skincare and starting to age really wanted. She came to this conclusion by using what she calls “kitchen logic”—understanding both what women wanted and how women believed anti-aging skin care products worked. Customers believed products need to penetrate the skin to work. They wanted to develop a product that is efficacious and that women intuitively feel is working. Connecting the Dots To create a product that delights customers, Nancy and her team had to “collect and connect” many dots—considering many areas that were important to customers. Their innovations included: Design Element Strategic Purpose Short, squat jar Communicates cream efficacy Pump mechanism Suggests absorption and precise dosing Large window carton Creates shelf visibility Simple graphics Encourage counter display Light-reflecting particles Reduce appearance of fine lines and wrinkles in the short-term Innovative combination of ingredients Reduces signs of aging in the long-term Consumer Research and Insight Development: Understanding the Skincare Market Nancy’s approach to consumer research demonstrated how product managers can gain deeper insights by going beyond traditional market research methods. Her commitment to understanding consumer behavior firsthand led to breakthrough insights that shaped Olay’s transformation. Research Methodology Nancy’s comprehensive research approach included: Personal conversations with over 1,000 women about their skincare routines and preferences In-home visits to observe real product usage patterns Shopping with customers to learn their packaging preferences Blind product testing without branding to understand true value perception Analysis of the consumer decision-making timeline in skincare Understanding the Consumer Journey By observing customers do their skincare routines, Nancy learned that after a customer first uses the product, she thinks about how it makes her feels. Over the next few days, she checks whether her fine lines and wrinkles are disappearing. After 2-3 weeks, she decides whether to keep using the product, but the bioactive ingredients take a few months to work. This knowledge led to Olay adding light-reflecting particles to reduce the appearance of fine lines and wrinkles, encouraging customers to keep using the product long enough for the bioactive ingredients to start working. Market Positioning and Pricing Strategy: Creating the Mass-Prestige Category Nancy learned from customer focus groups that customers perceived Olay’s product as a department store product that would cost $30-40. Olay created the “mass-prestige” skincare category, launching their Total Effects skincare as a $20 prestige product but in the mass channel. To validate the premium positioning, the team conducted extensive testing, including blind tests in which Olay outperformed leading department store brands in improving seven signs of aging. Developing a Different Product Nancy realized early-on that it wouldn’t be enough to develop a better product. Olay needed a different product. Rather than just staying comfortable as a product developer, Nancy acted as a serial innovator and took the risk of launching an entirely new brand. Nancy identified a significant market gap between cheap mass market skincare and expensive department store skincare. Their customers shopped at both places. They tested different price points and found that $20 was inexpensive enough for mass market shoppers and expensive enough to be a high-quality department store product. Navigating Organizational Challenges One of the most valuable insights Nancy shared was about managing the challenges serial innovators face within large organizations. She acknowledged that innovators often feel like “square pegs in round holes” and offered practical strategies for success: Make the “implicit explicit” by creating visual models and clear documentation of your thinking process Build a network of mentors and allies who understand and support your approach to innovation Identify and execute critical experiments that can validate your ideas to stakeholders Learn to communicate complex, interconnected ideas to linear thinkers in your organization Nancy’s experience showed that while holistic innovation might look simple once completed, the process of getting there can appear chaotic to others in the organization. The key is helping others understand your thought process and building support for your approach through clear evidence and results. The Spider Web Nancy compared the work of a serial innovator to a spider in its web: Intimacy with the Problem: Just as a spider is intimately connected to its web, successful innovators maintain close contact with the problems they’re solving. This means getting personally involved in consumer research and product testing. Multi-Domain Mastery: Like a spider’s expertise in web building, vibration analysis, and food capture, innovators need deep knowledge across multiple domains. Nancy called this becoming an “M-shaped innovator” rather than just having depth in a single area. Integration of Knowledge: Serial innovators must connect insights across different areas, similar to how a spider interprets various web vibrations to make decisions. Environmental Awareness: Understanding when and where to build the web is as crucial as knowing how to build it – timing and organizational context matter significantly. Conclusion The transformation of Olay from a declining brand into a market leader offers valuable lessons for today’s product managers and innovation leaders. Through Nancy’s systematic approach to consumer research, strategic product development, and market positioning, we see how breakthrough innovation happens when technical expertise meets deep consumer understanding. Her story demonstrates that successful product transformation requires more than just creating better products – it demands a holistic approach that considers every aspect of the consumer experience. For product managers looking to drive innovation in their organizations, the key takeaway is the importance of becoming what Nancy calls an “M-shaped innovator” – someone who can master multiple domains while connecting insights across disciplines. Whether you’re working to transform an existing product or create an entirely new category, success depends on your ability to combine consumer insights, technical innovation, and strategic thinking while building the organizational support needed to bring transformative ideas to market. The Olay case study shows that with the right approach and persistence, even the most challenging product transformations are possible. Useful Links Watch Nancy Dawes and Bruce Vojak’s webinar, “Serial Innovators” Check out Nancy and Bruce’s PDMA session, “Are You Firing the Wrong People?” Connect with Nancy on LinkedIn Innovation Quote “I see dead people.” – Nancy Dawes, based on The Sixth Sense Application Questions What research methods could you use to better understand your users’ unstated beliefs and assumptions about your product category? What evidence would you need to gather to validate a new price positioning for your product? Looking at your current product development process, how could you better integrate immediate user satisfaction with long-term benefits? What early indicators could you provide to users that would encourage them to stick with your product long enough to see its full benefits? How could you become more of an “M-shaped innovator” in your organization? What additional domains of expertise would help you better connect insights across different areas of your product development process? Think about a current challenge in your product line: How could you use small-scale experiments to validate your hypotheses before requesting major organizational investments? What would be your equivalent of Nancy’s early package testing? Bio Nancy is a recognized Serial Innovator from Procter & Gamble for her transformative work on Pringles, Olay, & Head & Shoulders.  She was instrumental in creating the anti-wrinkle, masstige skin care movement inside the $135 billion global skin care category via her pioneering work which turned a declining Olay brand into a $2.5 billion powerhouse.  She led global teams that demonstrated improvement in skin health and appearance, strategized proprietary materials, innovated packaging and product characteristics with uniquely strong consumer appeal and enabled pricing that led to holistic business wins for P&G.  Bookending this program, she employed similar methods and achieved similar results in P&G’s global Head & Shoulders and Pringles’ brands.  Nancy’s string of achievements caused P&G to elevate her to the level of its most elite scientists and engineers (Vic Mills society) and the Ohio State College of Engineering to award her the 2021 Benjamin Lamme Medal for Meritorious Achievement in Engineering.   After 38 years at P&G, Nancy leverages her innovation experience providing training to help companies/people improve their innovation capability.  She is an active volunteer for Girl Scouts and the College of Engineering at Ohio State. Thanks! Thank you for taking the journey to product mastery and learning with me from the successes and failures of product innovators, managers, and developers. If you enjoyed the discussion, help out a fellow product manager by sharing it using the social media buttons you see below. Source

How product managers use Jobs-To-Be-Done to create products customers love Watch on YouTube TLDR In this episode, I explain the Jobs-To-Be-Done (JTBD) framework, a powerful approach to understanding customer needs and developing successful products. Real-world examples like McDonald’s morning milkshakes, Snickers vs. Milky Way marketing strategy, and Bosch’s entry into the circular saw market demonstrate how understanding what customers are trying to accomplish (their “job-to-be-done”) leads to better product decisions and innovation. The episode contrasts Clayton Christensen’s consumer demand approach with Tony Ulwick’s job analysis perspective, while providing practical guidance for conducting customer interviews and prioritizing product improvements. Key Topics: The classic McDonald’s milkshake study and what it reveals about customer behavior How Mars used Jobs-To-Be-Done to differentiate Snickers and Milky Way Bosch’s successful entry into the North American circular saw market through customer observation Two main approaches to Jobs-To-Be-Done: consumer demand vs. job analysis Four key areas to explore in Jobs-To-Be-Done interviews How to prioritize unmet needs through customer surveys The importance of ethnographic research in understanding customer needs Tony Ulwick’s IBM PCjr experience and its influence on Jobs-To-Be-Done Different jobs-to-be-done for various podcast listener segments The fundamental role of curiosity in effective product management Introduction Last week, I met with my podcast production team to discuss the job-to-be-done that our listeners have. I got a few blank looks and one person said, “Yeah, the milkshake story.” Since we don’t all know the milkshake story, I want to share this Jobs-To-Be-Done (JTBD) story with you too. Jobs-To-Be-Done is a great tool, concept, and language that helps us understand the customer’s problem, what they need solved, and what might prevent them from buying our product. I’ve found the JTBD language very helpful, and through examples and applications, I hope you’ll learn how to make better use of it yourself. The Classic Milkshake Story McDonald’s wanted to sell more milkshakes. They had tried reformulating them, making them thicker, and offering flavor-of-the-month options, but sales hadn’t improved significantly. What caught their attention was that they were selling many milkshakes around 9-10 o’clock in the morning through the drive-thru. They hired Clayton Christensen and his colleagues to examine this phenomenon. As Clayton tells the story on YouTube, they stationed themselves at the end of the drive-thru lane. When customers ordered a morning milkshake, they would ask, “What did you hire that milkshake to do for you?” The responses were revealing. Many customers had a long, boring commute ahead and wanted: Something that would take time to consume Was easy to drink while driving Would fill them up Help with the monotony of the drive When asked what else they had tried for breakfast, customers mentioned alternatives like donuts, which are messy and distracting while driving. The milkshake worked well because it satisfied multiple needs: It was filling, took time to consume, and was neat and easy to manage while driving. Analyzing the Milkshake Case Study The milkshake story illustrates how JTBD helps us understand existing products. We’re examining what consumers are doing, their demand for the product, and any friction in the process. This understanding provides insights into how we can improve the product to better meet customer needs and make our marketing more effective to attract the right customers – in this case, those looking for a breakfast solution during their morning commute. Interestingly, I’ve never heard Clayton Christensen or others discuss what McDonald’s actually did with these insights. As an occasional McDonald’s customer, I’m not sure if they made any changes. It seems they could have developed a morning smoothie – a breakfast-appropriate option that might appeal to health-conscious customers with the same need. A smoothie might sound healthier than a milkshake, which can feel like an indulgence or too sweet for breakfast. The Snickers vs. Milky Way Case Study Another interesting JTBD example comes from Chris Spiek, who shared it in episode 057 of this podcast. The story involves two candy bars: Snickers and Milky Way. Chris’s boss, Bob Moesta at the Rewire Group, was hired by the candy company to help them decide which product to remove from the market. The company believed focusing their energy on one brand would help them compete more effectively. Bob began his research at an airport when he noticed someone selecting a Snickers bar. This led to a broader study where researchers would observe customers making purchases and ask why they chose one candy bar over another. They found: Snickers was chosen primarily as a filling snack, almost like a meal replacement Milky Way was selected as a reward or indulgence, appreciated for its rich, creamy experience Based on this research, the company realized these products served different market segments for different reasons. They decided to keep both brands and reframe their marketing. They even enhanced the Snickers formula by adding more peanuts and increasing the nougat to make it more filling and satisfying. This is an example of how JTBD can give companies valuable information they can use to enhance the product or more effectively market it. The Empty Nester Condominium Story Bob Moesta shared another example in episode 335, involving condominiums designed for retirees downsizing from their homes. The builder was having trouble converting interest into sales for their 55-plus community condominiums. Through interviews with potential buyers, Bob discovered two main barriers to purchase: The overwhelming task of downsizing possessions Emotional attachment to furniture, particularly dining room tables that held years of family memories The builder responded with two innovative solutions: Redesigned floor plans with smaller kitchens but larger dining areas to accommodate family dining tables Free first-year storage in the building for items people weren’t ready to part with This allowed potential buyers to: Bring their cherished dining room tables Avoid rushed decisions about possessions Retrieve stored items if needed Gradually decide what to keep or discard Two Approaches to Jobs-To-Be-Done The Consumer Demand Perspective The examples shared so far – the milkshake, candy bars, and condominiums – represent what we might call a Consumer Demand approach to JTBD. This approach focuses on: Understanding why consumers choose or don’t choose a product Identifying barriers to purchase Finding ways to make the product more appealing Questions for Jobs-To-Be-Done Interviews Dave Duncan, who worked with Clayton Christensen, outlines four key areas to explore in JTBD interviews, from the Consumer Demand perspective: Customer circumstances (current context) Jobs-to-be-done (what they’re trying to accomplish or avoid) Current solutions (what they’re doing today) Quality evaluation (how they measure success) The Consumer Demand perspective works well for existing products, and its language helps us understand what customers want. The Unit of Analysis Perspective Tony Ulwick offers a different perspective, focusing on the “unit of analysis,” which is the job itself, rather than consumer demand. The IBM PCjr Story Tony Ulwick’s perspective on Jobs-To-Be-Done emerged from his experience at IBM during the PCjr project. The PCjr was designed to revolutionize home computing at a time when personal computers were primarily used in business settings. There was growing interest in home computers, with options like the VIC-20, Commodore 64, and Tandy 8080 from Radio Shack already in the market. The project development took about a year, with significant marketing buildup creating anticipation for the product. However, the launch was disastrous – within two hours of release, the Wall Street Journal declared the IBM PCjr “dead on arrival.” For Tony and the team, this was a gut-wrenching experience. After spending a year developing what they thought would be an exciting product, they discovered that consumers weren’t interested. From a consumer perspective, the PCjr was both overpriced and underperforming compared to other options available, including self-built computers. This failure led Tony to deeply investigate how products could go so wrong despite extensive development efforts. Later, after Clayton Christensen had published The Innovator’s Dilemma about disruption in business, he and Tony had a conversation at Harvard. Clayton was intrigued by Tony’s framework, which had been published in Harvard Business Review. They discussed how Tony’s approach might provide solutions to the innovation dilemma Clayton had identified. During this conversation, Clayton suggested they needed a name for the concept, and “Jobs-To-Be-Done” was born. However, their approaches remained distinct: Clayton’s approach focused on consumer demand and choice Tony’s approach focused on the job itself as the unit of analysis This difference in perspective makes Tony’s framework particularly valuable for identifying opportunities in white space markets and creating entirely new products, while Clayton’s approach excels at understanding and improving existing products. The Bosch Circular Saw Example This case study demonstrates how focusing on the job itself can lead to innovation in what seems like a commodity market. When Bosch was trying to enter the North American market, they wanted to stand out rather than be in direct competition with other circular saws. Tony used ethnographic research (observing customers) to identify 14 unmet needs in the circular saw market, including: Prevent the cord from being cut (solution: removable power cord) Tool placement between cuts (solution: built-in hook for hanging) Line visibility while cutting (solution: improved sawdust blower) The result was an award-winning product that quickly captured market share. The primary job of a circular saw is straightforward: cut a straight line. But there are other elements of value that are important to customers. Ethnographic research allows us to identify those other unmet needs. Prioritizing Unmet Needs After conducting ethnographic research like in the Bosch case study, where they identified 14 unmet needs, the next question is: Do we design solutions for all of them? The answer is typically no. Even though all identified needs are unmet, they exist in a hierarchy of importance. Some needs provide significantly more value when addressed, while others might be merely minor annoyances that customers can live with. To determine which needs to address, follow these steps: Take the insights gained from observations Convert them into a list of unmet needs Create a survey for other users Ask them to rank the importance of each need This process provides real evidence to guide development decisions. Often, addressing the top 30% of needs can result in 80% more value for customers. This data gives designers clear direction on where to focus their efforts. The Results for Bosch This approach paid off significantly for Bosch: The resulting circular saw won product innovation awards It quickly captured market share in the North American market Contractors recognized the added value and were willing to pay slightly more for a significantly better tool This success demonstrates the power of not just identifying unmet needs through ethnographic research, but also properly prioritizing which ones to address based on customer input. Understanding Our Podcast Listeners’ Jobs-To-Be-Done When I was with my team that produces this podcast, we talked about why customers listen to this podcast. What is the job-to-be-done for them? We identified several key audience groups and their specific needs: Product VPs and Chief Product Officers These senior leaders listen to: Gain insights from expert interviews Find ways to coach and mentor their product managers Build and organize effective product management teams For these leaders, I offer the Rapid Product Mastery (RPM) Experience, a facilitated training program designed to help them nurture their product managers’ development. Individual Contributors This group spans several experience levels: Aspiring Product Managers Want to understand what product management looks like Learn how product managers think and what they prioritize Explore if product management is right for them Current Product Managers Many have 5+ years of experience Looking to deepen their skills Want to fill knowledge gaps from on-the-job learning Seeking comprehensive understanding of the discipline For these professionals, I offer the self-study version of the RPM Experience, based on the Product Development and Management Association’s (PDMA) seven knowledge areas. The program works best for those with at least two years of experience who want to: Solidify their existing knowledge Connect disconnected pieces of learning Fill in blind spots Gain a comprehensive view of professional product management Product VPs/CPOs Improving the performance of their product managers Needing to refocus on customers to drive product improvements Seeking improved collaboration and trust among product managers Wanting to provide training to aid in nurturing product managers For Product VPs and CPOs, I provide a facilitated version of the RPM Experience for groups of up to 14 product managers (and related product professionals). We meet virtually 75 minutes a week for 9 weeks. Participants also have access to all the materials provided in the self-study version of the RPM Experience. Innovation Leaders For those preparing to: Help their organizations improve innovation processes Move into formal innovation leadership roles I offer the Certified Innovation Leader (CIL) Program, aligned with the Association of International Product Marketers and Managers (AIPMM). This program provides training and certification for those who want to lead innovation initiatives within their organizations. Senior Leaders These listeners include executives who: May not be directly involved in product management Recognize product’s importance to their organization Need to understand how to foster innovation For these leaders, we’ve created the Unleashing Innovation Program, which focuses on: Moving beyond operational leadership Encouraging innovation throughout the organization Effectively responding to employee ideas Leveraging frontline employees’ insights Creating new revenue opportunities Reducing costs through innovation The common thread among all these listeners is their desire to enhance their product management knowledge and move toward product mastery. Whether they’re looking to advance their careers, build better products, or transform their organizations, they’re all seeking practical insights and actionable knowledge. Conclusion The Jobs-To-Be-Done framework, whether approached from Clayton Christensen’s consumer demand perspective or Tony Ulwick’s job analysis perspective, provides valuable tools for understanding customer needs and creating successful products. But at its core, effective product management comes down to one fundamental trait: curiosity. From understanding why people buy milkshakes for breakfast to designing better circular saws or creating the right living spaces for retirees, by maintaining genuine curiosity about customer needs and problems, product managers can uncover the true jobs-to-be-done and create solutions that customers love. Useful links: Check out my product management training Watch Clayton Christensen tell the milkshake story 057: Applying the Jobs-to-be-Done Framework – with Chris Spiek 335: JTBD tips from a veteran practitioner – with Bob Moesta 345: How to use Jobs-to-be-Done to be a market detective – with Dave Duncan, PhD 106: Jobs to be done – with Tony Ulwick Stopping the confusion of Jobs to be Done (JTBD)- with Tony Ulwick Innovation Quote “Many product managers are nervous about talking with customers, yet that is a primary responsibility of product management. You can make talking with customers easier by simply being genuinely curious about them, about their problem, and about what they want to achieve. Just be curious.“ – Chad McAllister, PhD Application Questions 1. How could you apply the Jobs-To-Be-Done interview structure (circumstances, jobs-to-be-done, current solutions, quality evaluation) to better understand your customers? Consider a specific product in your portfolio and outline what questions you would ask to uncover the true job your customers are hiring that product to do. 2. How could you use ethnographic research to better understand why customers choose or don’t choose your solution? 3. Think about the last time your team identified multiple potential product improvements. How could you adapt Bosch’s approach of surveying customers to rank unmet needs? How might this change your current prioritization process? 4. What barriers might prevent customers from choosing your solution? 5. How could you use Jobs-To-Be-Done insights to better align your marketing messages with customer needs? Like the Snickers/Milky Way example, are there ways you could better differentiate your product by focusing on the specific job it does for customers? Bio Chad McAllister, PhD, is a product management professor, practitioner, trainer, and host of the Product Mastery Now podcast. He has 30+ years of professional experience in product and leadership roles across large and small organizations and dynamic startups, and now devotes his time to teaching and helping others improve. He co-authored “Product Development and Management Body of Knowledge: A Guide Book for Product Innovation Training and Certification.” The book distills five decades of industry research and current practice into actionable wisdom, empowering product professionals to innovate and excel. Chad also teaches the next generation of product leaders through advanced graduate courses at institutions including Boston University and Colorado State University and notably re-engineered the Innovation MBA program at the University of Fredericton, significantly broadening its impact. Further, he provides online training for product managers and leaders to prepare for their next career step — see https://productmasterynow.com/. Thanks! Thank you for taking the journey to product mastery and learning with me from the successes and failures of product innovators, managers, and developers. If you enjoyed the discussion, help out a fellow product manager by sharing it using the social media buttons you see below. Source

How serial innovators transform product management Watch on YouTube TLDR Navigating innovation in mature organizations requires a unique approach that goes beyond traditional business strategies. During my conversation with Bruce Vojak, PhD, a leading expert in breakthrough innovation, we explored the challenges and opportunities for product managers and business leaders seeking to drive meaningful organizational change. The key is understanding how serial innovators can transform business potential and overcome deeply entrenched operational mindsets. Understand the concept of serial innovators and their unique value to organizations Learn strategies for overcoming innovation barriers in mature businesses Discover practical tools for fostering a culture of continuous innovation Explore real-world examples of successful innovation in different industries Develop a mindset that embraces creative problem-solving and organizational renewal Introduction Innovating is tough for businesses. Companies find something that works and gives them a competitive advantage and then tend to stick with it, limiting meaningful innovation over time. Since you are an innovator, you already know where your organization struggles with innovation. I have had the pleasure of coaching some of the best organizations in their industry and I can tell you every company can improve how it innovates. Let’s get some help and learn how to talk about the importance of innovation with senior leaders and the tools that can help organizations be better at innovation. Joining us is Dr. Bruce Vojak, founder of Breakthrough Innovation Advisors. He helps companies survive and thrive in a volatile, complex, and increasingly ambiguous world. Bruce has a unique and powerful mix of expertise in product innovation, including as a Director at Motorola, and in academia and research, serving at the Grainger College of Engineering at the University of Illinois at Urbana-Champaign and previously as a researcher at MIT Lincoln Laboratory. His research in innovation has been published in several places, including his recent book No-Excuses Innovation and his practice-changing book Serial Innovators. The Challenge of Innovation in Today’s Business Landscape Mature businesses typically have three strategic options when facing innovation challenges: Strategy Description Extend Current Model Optimize existing processes and incrementally improve current offerings Lean Optimization Focus on reducing costs and improving operational efficiency Pursue Innovation Develop breakthrough solutions and explore new market opportunities The most successful organizations recognize that innovation is not a one-time event but a continuous process. It requires a unique approach that goes beyond traditional management techniques. Product managers and business leaders must create an environment that nurtures creative thinking, supports risk-taking, and values the unique perspectives of serial innovators within their organizations. By understanding these challenges and adopting a proactive approach to innovation, businesses can transform potential obstacles into opportunities for growth and renewal. Understanding Serial Innovators: The Breakthrough Difference Serial innovators are a unique breed of professionals who consistently drive breakthrough innovations within organizations. These individuals possess a remarkable ability to see opportunities where others see roadblocks. Bruce’s research, highlighted in the groundbreaking book Serial Innovators revealed fascinating insights into these exceptional team members. Unlike traditional employees who often work within established frameworks, serial innovators approach challenges with a fundamentally different mindset. They’re not just thinking outside the box – they’re reimagining the box entirely. Characteristics of Serial Innovators What sets these individuals apart? Bruce’s research identified several key traits: Exceptional ability to understand unarticulated customer needs Willingness to challenge existing organizational assumptions Capability to reframe complex problems in unique ways Intrinsic motivation to drive meaningful change The Value They Bring to Organizations Serial innovators are not just creative thinkers – they’re strategic assets. Many of the most significant breakthroughs in business come from these individuals who: Innovation Capability Organizational Impact See Patterns Others Miss Identify new market opportunities before competitors Navigate Organizational Challenges Build bridges between departments and break down silos Drive Continuous Improvement Create sustainable paths for business renewal One insight from Bruce’s research was the diverse backgrounds of these innovators. Interestingly, most serial innovators he studied did not have traditional business degrees. Instead, they learned business “on the street” – through direct experience, observation, and an innate ability to solve real-world problems. The key for organizations is not just identifying these individuals, but creating an environment that nurtures and supports their unique approach to innovation. This means moving beyond rigid processes and embracing a more flexible, human-centered approach to product development and organizational strategy. By recognizing and empowering serial innovators, companies can transform their innovation potential and create sustainable paths for growth in an increasingly competitive business landscape. Overcoming Innovation Barriers in Mature Organizations Innovation doesn’t happen by accident. Organizations often struggle to break free from the gravitational pull of their existing business models. Mature companies, in particular, face significant challenges when attempting to drive meaningful innovation. Most organizations inadvertently create barriers that prevent breakthrough thinking. These barriers can be deeply ingrained in company culture, organizational structure, and management approaches. Functional departments often become siloed, with each team focused narrowly on their specific performance metrics, creating natural resistance to cross-functional innovation efforts. Common Innovation Roadblocks Comfort with existing successful business models Reward systems that discourage risk-taking Rigid organizational hierarchies Fear of failure and short-term performance pressures Tools and Frameworks for Innovation Success Having the right tools can make the difference between innovation success and failure. While processes alone don’t guarantee breakthrough innovations, they provide essential frameworks for structured thinking and exploration. Bruce shared valuable insights about combining traditional innovation approaches with more unconventional methods. The key is understanding that these tools should enable rather than constrain creative thinking. Essential Innovation Tools Tool Type Purpose Key Benefits Design Thinking Customer-focused problem solving Ensures solutions address real needs Lean Innovation Rapid experimentation Reduces waste and speeds learning Phase-Gate Process Risk management Provides structured decision points Open Innovation External collaboration Accesses diverse perspectives Strategies for Breaking Through Bruce emphasized that successful serial innovators often practice what he calls “understanding unarticulated assumptions.” This means looking beyond surface-level problems to identify deeper patterns and opportunities. The most effective innovation tools support this kind of deep exploration while providing practical frameworks for moving ideas forward. The key is remembering that these tools should serve as enablers rather than constraints. Whether you’re using design thinking, lean methodologies, or another framework, the goal is to support and amplify innovative thinking, not replace it with rigid processes. Leading Innovation: Strategies for Success Navigating organizational resistance to innovation requires a delicate balance of leadership approaches. Bruce shared several powerful strategies that successful innovation leaders use to drive meaningful change in their organizations. Three Key Approaches to Innovation Leadership Strategy Description When to Use Direct Advocacy Making compelling arguments for innovation When leadership is receptive to new ideas Strategic Maneuvering Building coalitions and influence When facing organizational resistance Quiet Momentum Working behind the scenes until projects become “too big to fail” When direct approaches might face early rejection The “quiet momentum” approach proved particularly interesting. Bruce shared examples of successful innovation leaders who chose to work quietly on breakthrough projects until they became too significant to ignore. This strategy often works well in organizations where traditional innovation processes might stifle creativity early on. Building Innovation Support Effective innovation leaders focus on: Creating psychological safety for teams to experiment and take risks Developing cross-functional relationships to navigate organizational barriers Identifying and nurturing potential serial innovators within the organization Building credibility through small wins before pursuing larger innovations One insight from our discussion was the importance of understanding organizational context. What works in one company might fail in another. The most successful innovation leaders adapt their approach based on their organization’s culture, structure, and readiness for change. Bruce emphasized that innovation leadership isn’t just about managing processes – it’s about creating environments where breakthrough thinking can flourish. This often means protecting innovative teams from bureaucratic constraints while still maintaining enough structure to deliver results. The key is finding the right balance between structure and freedom, between direct advocacy and behind-the-scenes work, and between short-term results and long-term innovation potential. Success often comes from knowing when to push forward and when to build quiet momentum for change. Real-World Innovation Success Stories In my discussion with Bruce, we explored several compelling examples of successful innovation in mature industries. These case studies demonstrate how organizations can achieve breakthrough results even in traditionally conservative markets. Innovation in Action: Key Examples Company Innovation Key Insight Slice Reimagined Box Cutter Finding innovation opportunities in commodity products West Tech Automation Electric Vehicle Manufacturing Breaking traditional supplier-client relationships Midtronics Battery Management Systems 40-year culture of continuous renewal The Slice box cutter case particularly stands out. In a market where box cutters were seen as pure commodities, the company identified opportunities for meaningful innovation. They introduced features like: Ergonomic handle design for better grip and protection Ceramic blade technology for longer life Enhanced safety features Improved user experience West Tech Automation’s story demonstrates how companies can innovate in their approach to customer relationships. Rather than following traditional supplier specifications, they embraced a collaborative approach to solving complex manufacturing challenges in the electric vehicle industry. This required a fundamental shift in how they engaged with clients and managed projects. The Moneyball Effect Bruce highlighted the movie Moneyball as a metaphor for innovation in traditional industries. The story illustrates several key innovation principles: Challenging deeply held industry assumptions Using data to drive decision-making Navigating organizational resistance to change Creating sustainable competitive advantages These examples show that meaningful innovation is possible in any industry, regardless of how mature or traditional it might be. The key is finding ways to challenge assumptions, identify unmet needs, and execute effectively on new ideas. Success often comes from combining deep industry knowledge with fresh perspectives on longstanding challenges. Conclusion The landscape of product innovation is continuously evolving, and the insights Bruce shared during our conversation reveal both challenges and opportunities for today’s business leaders. Organizations that want to thrive, rather than just survive, must embrace innovation as a core capability rather than treating it as a peripheral activity. For organizations seeking to enhance their innovation capabilities: Start by recognizing and supporting the serial innovators already in your midst Create space for experimentation and learning from both successes and failures Build cross-functional relationships that enable innovation to flourish Invest in tools and frameworks that support rather than restrict creative thinking The message is clear: in today’s rapidly changing business environment, innovation isn’t just an option – it’s a necessity for long-term survival and growth. Whether you’re leading a small enterprise or a large corporation, the ability to innovate consistently and meaningfully will increasingly determine your organization’s success. Remember, there are no excuses for avoiding innovation. Every organization, regardless of size or industry, has the potential to create breakthrough value. The key is combining the right mindset, tools, and leadership approaches to unlock that potential. Useful links: Check out Bruce’s website, Breakthrough Innovation Advisors Connect with Bruce on LinkedIn Innovation Quote “We have a guy like that; his name is Kevin.” – Steve McShane, founder and CEO of Midtronics, Inc., in response to Bruce sharing insights about Serial Innovators “I see dead people.” – Nancy Dawes, retired Vic Mills Fellow at P&G and Serial Innovator, in response to Bruce’s question, “How do you know what to do?” describing her ability to see patterns and opportunities that others missed Application Questions How could you identify potential serial innovators within your organization? What characteristics would you look for, and how could you create opportunities for them to demonstrate their innovative capabilities? Think about a recent innovation initiative that faced resistance in your organization. How could you apply the “quiet momentum” strategy to build support for similar initiatives in the future? What specific steps would you take to grow the project until it becomes “too big to fail”? How could your team adapt Adobe’s Kickbox program concept to fit your organization’s needs and constraints? What resources and support would you need to include to make it successful in your specific context? Looking at your current product portfolio, where could you find opportunities for breakthrough innovation in seemingly “commodity” products, similar to the Slice box cutter example? How could you and your team challenge basic assumptions about these products? How could you better balance your organization’s need for structured processes with the flexibility required for breakthrough innovation? What specific changes to your current development process would help achieve this balance? Bio A leading authority on innovation, Bruce Vojak helps mature companies survive and thrive in a volatile, complex, and increasingly ambiguous world. Co-author of No-Excuses Innovation: Strategies for Small- and Medium-Sized Mature Enterprises (Stanford, CA: Stanford University Press, 2022) and Serial Innovators: How Individuals Create and Deliver Breakthrough Innovations in Mature Firms (Stanford, CA: Stanford University Press, 2012), Bruce is a Senior Fellow with The Conference Board, he has served on the boards of JVA Partners, Micron Industries Corporation, and Midtronics, Inc. He regularly presents to, leads workshops for, and advises various other companies, having founded Breakthrough Innovation Advisors, LLC following and building on a career as an innovation practitioner and executive at MIT Lincoln Laboratory, Amoco Corporation, and Motorola, and as an innovation researcher in the top-ranked Grainger College of Engineering at the University of Illinois at Urbana‐Champaign. Thanks! Thank you for taking the journey to product mastery and learning with me from the successes and failures of product innovators, managers, and developers. If you enjoyed the discussion, help out a fellow product manager by sharing it using the social media buttons you see below. Source