The AI podcast for product teams

Whether we admit it, like it, or believe it, we’re in a relationship with AI.That’s the first of many powerful reflections made by Sara Vienna, Metalab’s Chief Design Officer, in her must-read manifesto about how design and product must evolve. Unlike the design leaders who speculate about AI's impact, Sara and her world-class team are years ahead. They are designing disruptive AI product experiences and leveraging AI to elevate their workflows. Sara’s episode is one of the most important conversations we’ve had about the future of design and products.Listen on Spotify | Listen on Apple PodcastsShe believes that AI will change how we work and what we build. Those who embrace the potential of AI will succeed in the oncoming disruption. But most importantly, the future of product+AI will be in making five mindset shifts:They’re fundamentally principles for humanizing experiences. The hope is that AI will finally bridge the divide so products can deliver the value we’ve always wished was possible in the most humanized way possible. But there will be challenges in accomplishing this:* Most product orgs are built around the concept of delivery, not design excellence* Unlocking user data: Getting access to valuable data and knowing how to use it in a meaningful way are still more fantasy than reality* In every direction we turn, trust is being diluted* Design as we know it will need to be reborn to adapt to move from creating pixel-perfect interfaces to ones that adapt and spawn based on user interactionsAgain, I highly recommend listening to the entire episode.Thanks for reading Design of AI: Strategies & insights for product teams! This post is public so feel free to share it.Envisioning the future of design & productIf we extrapolate on Sara Vienna’s vision of how design should change, a couple of core reality checks come to mind:* Today, we can’t even conceptualize what products will be able to do tomorrow. Just like new AI tools are being released faster than we can read about them, more teams than ever are competing to deliver the use case & interaction model that will redefine a category. It’s a race to an undefined & moving finish line.* The underlying models may be the heartbeat of future products, but design will always be the brain. Products plug into whichever model suits them best at a particular moment, usually based on cost and accuracy. But just like each of our minds brings a different lived reality and way of using knowledge, the models are less important than the strategy that’s been designed into the product.* Fewer designers and product managers will yield immense power. AI automation platforms —like Make and Loveable— can effectively replicate more than half of products today. This percentage will grow until such a point that any product will soon be able to be cloned, undermining its competitive advantage. The designers and product managers working on the future of design will have the funding that enables them to compete in a global race that they’re likely to lose because they don’t know what competition they’re actually facing. The rest of us will be working to keep the lights on.Big question: How should we be using AI, today?Photoshop celebrated its 35th birthday today and is a perfect reminder of how disruptive platforms eventually become part of the boring vocabulary of the everyday.GenAI platforms, like ChatGPT, are in their infancy. Everything seems equal parts novel and confusing. We’re still unsure how to use this superintelligence, only that we should be using it. Photoshop’s rise was similar: a platform that opened up so many possibilities but whose ultimate impact wasn’t felt until it redefined the designer role many years later. What’s happening today is that employees are smuggling AI into work and this makes sense given the recent McKinsey report that finds that leaders are slow to adopt because of risks and a lack of vision.Our research finds the biggest barrier to scaling is not employees—who are ready—but leaders, who are not steering fast enough.Anthropic, the maker of Claude, published their Economic Index report and found that AI use is most prevalent in computer & mathematical occupations. Their AI model is mainly used for programming and administrative tasks.What the data also show is that design and creative tasks aren’t core use cases, yet. And rightfully so, large language models best serve requests about processing content and code, not pixels and ideas. A report about how generative AI is used in journalism showcases this by highlighting that even the creative tasks are largely operational ones, like resizing images and animating.This data highlights the divide in how leading organizations, like Metalab and Superside, leverage AI compared to the everyday user. While the average person uses Midjourney to generate stock art, leading designers automatically generate localized creative based on design systems and content guidelines.The reality is that product teams have three core workstreams:* Operations: Planning, organizing, editing, describing (e.g. Notion)* Creativity: Ideating, revising, collecting, analyzing (e.g. Cove)* Productivity: Deciding, planning, organizing, explaining (e.g. ChatGPT)Every designer, product manager, writer, producer, and researcher completes tasks in these three workstreams. And every one of you should be taking the time to break down your typical workflows into discrete activities so that you can explore what AI solutions can either augment or automate non-critical tasks.An example of this is how Kyle Soucy is using AI to streamline person and journey map creation. This type of knowledge work is considered sacred by traditionalists but as you can see in her article, she’s broken down her workflow to find effortful tasks to be augmented/automated. We can question AI’s accuracy all we want. We can challenge if models were trained ethically. And we can debate what percentage of your job may benefit from using AI. What will not change is the undeniable truth that the intelligence and capabilities of these models and tools will only improve. The sooner we embrace that truth, the better positioned we are to control our own fates. For example, a recent study evaluated AI vs. human-generated therapy responses from 13 expert therapists (clinical psychologists, counseling psychologists, marriage and family therapists, and a psychiatrist). The report found (questionable) data to indicate that users couldn’t tell if a human or an AI made the responses. The AI also outperformed human therapists on empathy, professionalism, and cultural competence.We’ll soon reach a point where generative AI can output designs that are indiscernibly human or automated. In this near-term reality, the role of designers must evolve or be replaced.A recommended action plan for how you should be using AI today:* Plan projects and workstreams using templates, resources, and added context* Communicate ideas and insights better by using AI to iterate and expand* Question the rationale, assumptions, and factors that impact the project goal* Compile inspiration, ideas, and information that will broaden your thinking* Analyze larger data sets and more sources than you could have before* Challenge your concepts by making variants and exploring new directions * Create more deliverables by automating localization, multiple formats, and generating content based on systemsIf you enjoy this content, please make sure to listen to the Design of AI podcast on Spotify and Apple. Make sure to follow us and rate the show if you like the show!Add me on LinkedIn if you want to ask any questions or discuss a project. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com

There’s little debate that AI will change the world. What we’re not so sure about is if AI’s expected disruptions to how we work will be outweighed by the benefits of accessing a super-intelligence.David Boyle thinks of LLMs as an electric bicycle for the mind, one that enables us to go farther than we ever imagined with much less effort. His opinion comes from being one of the first market researchers to experiment with LLMs and subsequently turn his learnings into the PROMPT series of books to help marketers, startups, researchers, musicians, and other creatives benefit from the emerging technology. He’s an audience research expert who has informed global strategies for many of the world’s biggest brands.In this episode we explore why David Boyle believes that AI can make strategy & research work faster, cheaper, AND better. Listen on Spotify | Listen on AppleThe conversation explains why any product manager, researcher, strategist, or creative should leverage AI. The greatest advantages are speed and quantity because GenAI overcomes research’s most time-intensive tasks: codifying and thematic analysis of large data sets.David admits that one of the biggest challenges is that AI are often confidently wrong and that experts must verify the results.This episode raises important questions:* If AI will make all tasks faster, what changes should we expect to our way of working? Consider how the internet is homogenizing the way we live globally.* If a human expert must verify results, how can we trust the results of AI tasks as soon as the velocity scales past the number of humans in-the-loop?* If executives are excited by AI reducing the cost of research, what will stop them from preferring synthetic or non-human verified data once the cost nears zero?Recommended articlesThe Future of Design: How AI Is Shifting Designers from Makers to Curators by Andy Budd“AI is transforming design, shifting designers from hands-on creators to curators focused on strategy” is the most common prediction about where design is headed. The author believes the design roles will evolve to where and how they can best deliver value and it will likely be in enhancing the quality of work delivered by AI. As optimistic as it sounds —hey everyone wants to be more strategic, yay!— the truth is that in this future scenario, the concept of being a design completely changes with most being dedicated to managing AI tasks and the best assigned to bespoke design tasks that must be perfect. The End of Programming as We Know It by Tim O’reillyMakes a case that each fear cycle about software developers getting replaced actually led to an evolution of the craft. He admits that “Eventually much of what programmers do today may be as obsolete” but that it will be more akin to how the old skill of debugging was replaced with roles tackling more complex tasks. As knowledge workers we have to be concerned because our work can’t be quantified and automated in the same way as the production-line model of development.AI agents will replace SaaS software by Ayan MajumdarIn this analysis of the CEO of Microsoft’s statements that "AI agents will replace all software" he breaks down common SaaS use cases and whether AI can replace those use cases. He concludes that “The shift towards intelligent agents signifies a move away from manual software interactions towards more intuitive, AI-driven processes.” Overall this is further evidence AI agents could replace the SaaS layer which often only existed to give custom lenses to your own data.AI-Generated Slop Is Already In Your Public Library by Emanuel MaibergThe enshitifaction of knowledge is now hitting libraries. Libraries, once keepers and curators of the world’s most important knowledge now can’t guarantee the accuracy, provenance, and value of many works being submitted. “My library, like most, does not have the resources to be checking Hoopla on a weekly basis to weed out what we wouldn’t want there.”What being replaced by AI in 2025 looks likeWhere does knowledge work go from here?Here’s an example of the disruptions possible today where OpenAI’s new Deep Research was used in combination with Gamma to do big consultancy-level research into a market and publish a stunning report. All in 2 minutes.Agencies & consultants: Any business that doesn’t learn to adopt AI to augment and automate workflows will be at risk of losing niche projects to competitors who are optimized for price, speed, and/or scale. Legacy and large orgs tend to be overloading team members so much to remain profitable that they will be slow to adapt to challengers who will turn AI into a major advantage in a price-sensitive market.Researchers & designers: Orgs are hungry to cut costs and will jump at the opportunity to automate rote tasks. Worse yet the entire value of design and research is becoming so commodified that at least one of your leaders will have the misguided belief that everything you do can be automated. Find a culture that values you and become an expert in leveraging the tools to augment your imagination, planning, iteration, and delivery.Analysts & marketers: AI is giving you an ever-expanding superpower to access more data and analyze it more effectively. Your value only goes up if you challenge your own assumptions of what is possible. Being flexible with the tools, platforms, and methods you use will only lead to better outcomes. Unlike other knowledge workers you’re experts in how to deploy copilots and agents effectively because you know how to structure data and requests.Recap from Autonomous AI SummitThis week thousands of industry leaders and strategies attended the Board of Innovation’s online summit. Content was largely focused on shifting perspectives about the technology, the future, and use cases.Day 1 recap by Chisoko Luala SimbuleDay 2 recap by Chisoko Luala SimbuleThanks for reading Design of AI: Strategies & insights for product teams! This post is public so feel free to share it. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com

Up until recently Miro was the innovator’s defacto collaboration platform. In recent years a long list of apps added similar functionality to eat away at the online whiteboard segment. Our latest episode with Ioana Teleanu, Miro’s former Lead Product Designer for AI explores the challenges and opportunities of leveraging AI to enhance an existing product.Listen on Spotify | Listen on AppleKey takeaways:* When a product experience is already good, do we need to add AI?* AI makes it easier for more products to enter your category and add unexpected competition* Adding AI forces product teams to ship quickly to be able to learn, sometimes with uncertainty attached* You must consider if AI is the right solution to the problem you’re trying to solveIf you have any questions about these or other AI questions, reach out to us and we can help you upack what it means for your product.Thanks for reading Design of AI: Strategies & insights for product teams! This post is public so feel free to share it.Next week’s podcast episode features David Boyle who makes a case for why AI is transforming what we can learn about audiences and how those insights will improve our ability to strategize.Featured articlesAI Agents: How Businesses Must Adapt or Risk Obscurity (Arpy Dragffy)Ethan Mollick is right: #AIAgents are going to fundamentally change how websites, apps, and APIs are structured. But the implications go far deeper. We’re rapidly moving from a world where users seek out information to one where it is pushed to them by AI agents acting on their behalf. This shift has profound consequences for businesses of all sizes, and those that fail to adapt risk disappearing into the noise that these agents must sort through. (Read full article)The Wild Future of Commerce & The Rise of Conformative Software (Scott Belsky)This edition explores forecasts and implications around: (1) wild expectations for the future of commerce, (2) the era of “conformative software” that becomes more tailor-made as you use it, and (3) some surprises at the end, as always. (Read full article)25 Themes for 2025 (Bronwyn Williams)A cheat sheet of the 25 top things I'm watching unfold in 2025 (See presentation)Always remember… a good learner appreciates being proven wrongBiggest story of the week: Deepseek Ovetta Sampson is one of the must-follow voices in AI, bringing a rational perspective to an otherwise nonsensical chorus of voices. Read the full article here.Deepseek is the new Chinese model that is the biggest AI story of the year (so far). Yes the model is Chinese and may be compromised. But what’s most compelling about this story:* Despite the US places restrictions and pumping country-sized funding into AI, the model outperforms every model made in the USA.* Just like China has done in countless other industries (e.g. Shein and Huawei), they create copy-cat products that deliver 80% of the value for significantly lower cost.* We went into 2025 thinking OpenAI won the AI model wars that we’d all be subjected to whatever pricing they forced upon us. WRONG. Deepseek and many more will now come along and chip away at that expectation. More analysis about China’s disruptive Deepseek model:* Why DeepSeek Prompted a $1 Trillion Tech Sell-Off (Business Insider)* 🐳 I just finished a deep dive into DeepSeek’s latest R1 model & paper. I am genuinely impressed by their approach. A few thoughts. (Reuven Cohen)* Running DeepSeek r1 32B locally is kind of depressing. (Ethan Mollick)* DeepSeek is out, the Stock Market crashed and Silicon Valley is in tears. Here's what happened in the last 48 hours and what it means for your business (Tobias Zwingmann)* The labour market over the next few years is going to be a complete 💩show thanks to super smart, super cheap open source Ai. Here’s why. (Reuven Cohen)* What if I told you the $500B Nvidia selloff is missing the point entirely? I believe AI Revolution Just Had Its "iPhone Moment" (Simon Taylor)New AI products worth trying outStorm Stanford’s new platform enables you to build a paper about nearly any topic with summary and references. Helpful to get a deep dive into big topics, from your desired perspective.RileyNew market research platform which guides users through questions about their product, competitors, and data available. As a proof of concept of where things may head it is compelling.Thanks for reading Design of AI: Strategies & insights for product teams! Subscribe for free to receive new posts and support my work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com

Healthcare is constantly highlighted as the industry that will benefit the most from AI. The prospective opportunities are endless: Improve access to services, improve quality of service, patient outcomes, and medical research. An analysis predicts that the healthcare could save up to $360B a year by implementing AI.That’s we invited an expert to discuss what other industries can learn from healthcare’s massive AI opportunity. Spencer Dorn, the Vice Chair and Professor of Medicine at the University of North Carolina. He is a contributor to Forbes and one of LinkedIn’s Top Voices speaking on Healthcare + Innovation.Listen on Spotify | Listen on Apple PodcastsKey takeaways from the episode:* AI has been impacting healthcare for years, especially to create Electronic Health Records (EHS) as a way of centralizing information* AI is being explored today as assistants to medical professionals (e.g. Virtual/digital scribes) and across a variety of diagnosis scenarios (video)* But the rollouts have been plagued by consistent issues related to adoption and poor comprehension of the actual problems* To get EHS implemented EHS it needed an Obama-era law and incentive plan* Many of the initiatives aiming to speed up access to healthcare and diagnosis are undermining the relationships across the journey of being a patient * Technology is rarely the solution because the problem is typically bureaucracy, culture, lack of incentives, and externalitiesLessons for you:* Beware complexity: Most of AI products being sold by major corps and consultancies are ones solving micro-problems and not designed to tackle complex problems* Worry about adoption: It doesn’t matter how brilliant your solution is, getting buy-in and adoption within enterprises will be the most pressing challenge* Think of problems as systems: JTBD and user stories have a tendency of over-simplifying problems and underrepresenting the range of factors, dependancies, and implications of a problem on the system as a whole* Ethnography is key: If you want to make a positive change to a problem space you need to leverage deep qualitative research techniques, like ethnography, to document and assess what matters and why* Monitor for unintended consequences: Even after dedicating lots of time to research and planning, we must be monitoring for unintended consequences that may create more work or more anxiety for those stakeholders within the system.Thanks for reading Design of AI: News & resources for product teams! This post is public so feel free to share it.Challenges building truly human-centred AI products and solutionsAI thought leaders love to push this message of getting to the future quickly. It creates this narrative that we’re all falling behind.But let’s slow down and recognize that there are countless of questions to be addressed before throwing everything out in favour or the shiny new system. This paper from Microsoft explored the many questions that users are posing about using AI agents. And these are very important questions that every team should be able to answer clearly to their users before deploying any solution.This poll from Google’s former Chief Decision Scientist highlights that the technical part of implementing AI is no longer the biggest barrier, understanding humans is. If the organizations polled —ones who have successfully implemented AI— are struggling to identify good opportunities and to convince people to use it, then imagine what struggles an everyday org will have.And also worth considering that AI adoption is still much lower than we’d expect given all the hype. The implementation of aI —especially across large orgs— may takes a decade or more because we’re fundamentally asking teams to change the way they work. Moreso, those in regulated industries need the permission to change how they operate before they can even consider implementing AI products.In the background many workers are using AI without their employers’ knowledge, leading to an endless range of potential risks.Thanks for reading Design of AI: News & resources for product teams! This post is public so feel free to share it.Mindset shifts to help implement AI In the podcast Spencer kept highlighting that we need to go into problem spaces with humility and without the expectation that problems are easy to solve. Other guests have suggests other types of mindset shifts:* Jess Holbrook stated we need to be specific when talk about AI: Too many projects are built off of expectations, not specifications of what AI should do and how* Kristie J. Fisher believes we need to measure time well spent using AI: The best solution to adoption problems is making sure that the AI product delivers value AND time well spent* Josh Clark advocated for embracing the weirdness of AI: The imperfectness of AI outputs should be viewed as a creative and innovative feature to help you explore new directions* Phillip Maggs challenges us to imagine new possibilities with AI: This is your time to spread your capabilities into areas you always wished were possible * Alexandra Holness expects that designers need to be less emotional precious with AI: This is a time of uncertainty and what worked before may not work in the future, so especially designers will need to go into problem spaces with additional humblenessMetalab is probably the premier design shop in North America. They’ve designed many of the most popular AI products in market today. Sara Vienna, their VP Design published a great manifesto about mindset shifts that’s worth a read. And she’ll be a guest on the podcast soon!And for those wanting more of a blueprint: Tertiary Education Quality and Standards Agency of Australia put together a guide that has lots of helpful detail into some of these. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com

In our latest episode, Lisa Weaver-Lambert dispels the belief that is incapable of delivering impact in her book "The AI Value Playbook." She also lays out principles for succeeding in your implementation of AI:1. Your tech stack determines winners: Orgs that already were built to process and leverage data as part of core decision making are at a huge advantage. Especially those that are focused on leveraging insights to learn and iterate.2. Leadership and strategy matter: The vision, guiding principles, and culture matter. They will dictate the strategy or lack of a cohesive strategy.3. AI shouldn’t be added on top: AI should be viewed as the pathway ro removing layers, friction, and complexity.4. Getting from proof of concept to value is harder: AI reduces the barrier to creating proof of concepts while also layering in a lot more uncertainty about how to make it production-ready.5. Centralize AI strategy & decentralize implementation: Orgs should have a cohesive strategy owned by a centralized team. But the workflows and use cases defined by the teams that are seeking to gain specific value.Listen on Spotify | Listen on Apple | Watch on YoutubePlease rate the podcastIf you’ve listened to the podcast, please help us by giving us a rating. It helps us get in front of more people and know that what we’re publishing is delivering value.Rate us on Spotify | Rate us on Apple PodcastsAnd if you have comments, questions, or suggestions: info@designof.ai New report showing use of Anthropic (Claude) doubled, while OpenAI lost 1/3Menlo Ventures published their 2024 report: The State of Generative AI in the Enterprise. It shows the continued maturation of the AI market and clear use cases where the tech is being leveraged. Not surprising, task-level use cases that can be directly evaluated/audited are coming out on top. Also, the layers of AI stack are becoming more distinct with some products starting to create their own moats. As we move into 2025 expect the Data layer to split as more orgs realize that they need a semantic layer to structure and make sense of first-party data.Thanks for reading Design of AI: News & resources for product teams! This post is public so feel free to share it.The LLM market share data makes OpenAI look like the big loser. But I suggest throwing out the 2022 and 2023 data since adoption was so low and leveraging the tech for experimentation rather than impact. 2024 is the year when AI became the workhorse for the first time powering countless products. Nonetheless, it is compelling to see Anthropic and Claude shoot up. Their focus on UX seems to be paying dividends, that or OpenAI’s dilution of trust is.Of no surprise, prompt engineering is falling off a cliff. It was a bandaid approach for a tech that had no standards yet. For reference a business that built their product through prompts often had to rebuild all those prompts whenever a model was updated. Thanks for reading Design of AI: News & resources for product teams! This post is public so feel free to share it.AI use & impact assessment surveyPlease share your experiences and point of view in our year-end AI research study.Your lessons and opinions will shape a critically important assessment of how & if AI is positively impacting individuals and teams.Less than 5-minutes of your time will help us a lot.Perplexity is one-upping Google by introducing AI-powered shopping journeysPerplexity, the upstart GenAI search form is firing shots at Google by taking a refreshing look at shopping. Rather than focusing on someone searching for a product (e.g. Patio furniture), they are taking a very human-centred approach by focusing on what a user is trying to accomplish (e.g. renovate my outdoor living space). The platform then provides ideas, support, and instructions. Plus, recommends products to buy.While this is immensely helpful, it brings up the ever-present concern that AI will pick winners and losers for us. Where Google served up dozens or hundreds of results and encouraged us to make our own decisions, AI only shows a handful of options. This is the beginning of the platform as expert and it could change how we interact with the world in a huge way. It could lead to small merchants being shut out or even grow distrust of options that aren’t recommended by a platform.Alarming data showing that achieving AGI could destroy market wagesEconomics at the International Monetary Fund have modeled data that shows that if Sam Altman & crew succeed at bringing AGI to the world faster than expected, it could set into motion a total destruction of market wages (aka devalue everything).Their model also showed that on the expected timeline of AGI, wages will continue to rise as humans continue to do the thinking for the machines.Read the reportThanks for reading Design of AI: News & resources for product teams! Subscribe for free to receive new posts and support my work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com

The last two years have been extremely stressful for anyone working in tech. There’s been a consistent sense that we all need to do more with less. That our jobs are on the line. And now AI is being touted as the cheat code that will unlock productivity and profit gains.In our latest podcast, Peter Merholz (add him on LinkedIn) doesn’t see AI helping much in the short-term because teams are too over-tasked to believe they have the time to try new models of working. He also believes that most organizations don’t have cultures and leadership that promote experimentation and reward learning. Listen on Spotify | Listen on Apple | Watch on YoutubeWhat makes matters worse is that simply “using AI” won’t get you the results you need. Simply using ChatGPT or Claude will not give you and your business a significant boost because data is at the heart of AI. The more of your first-party data that you train models on and the more that you craft agents around specific workflows, the closer you’ll get to what AI acolytes are selling. Accenture calls this AI maturity: Advancing from practice to performance. And this is where Peter Merholz believes that most orgs will be blocked. His experience working in mega-corps has found that most aren’t learning cultures. Introducing new tools, mental models, and ways of working aren’t well-received. AI use & impact assessment surveyPlease share your experiences and point of view in our year-end AI research study. Your lessons and opinions will shape a critically important assessment of how & if AI is positively impacting individuals and teams. Less than 5-minutes of your time will help us a lot.Valuable lessons 💡 Nearly half of workers are uncomfortable admitting to their manager that they used AI for common workplace tasks💡 Evaluations —or “Evals”— are the backbone for creating production-ready GenAI applications. 💡 Ten lessons that separate impactful training from mere AI showcases💡 Even teams actively working with AI are wrestling with fundamental knowledge structuring challenges. The tools are advancing faster than our practicesThanks for reading Design of AI: News & resources for product teams! This post is public so feel free to share it.Exciting AI jobs👉 USA | Anthropic | Strategic Product Management👉 USA | World Economic Forum | Head of Data and AI Innovation👉 USA | Google DeepMind | Group Product Manager, Generative AI Tools for Music Creators 👉 USA | Amazon Web Services | Generative AI Strategist, Generative AI Innovation Center👉 Australia | Canva | Creative Technologist (Gen AI)👉 Canada | Autodesk | AI Research 3D Dataset Creation & Annotation Manager 👉 Canada | Robinhood | Staff Product Designer, AI Investing👉 Canada | McAfee | Sr Product Manager, GenAI This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com

Phillip Maggs, who leads Generative AI Excellence at Superside and has collaborated with giants like Netflix and Google, shares invaluable insights on design and AI. He emphasizes that merely consuming AI content isn't enough; experimentation is key to career advancement. Maggs also highlights the shift in design roles due to GenAI, advocating for the automation of routine tasks while preserving the artistry in brand-defining designs. Additionally, he discusses the importance of codifying brand rules to unlock AI's potential in creative processes.

GenAI’s promise is that digital experiences will become more intelligent. Big Medium Founder Josh Clark and his daughter, Veronika Kindred, are the authors of the upcoming book “Sentient Design” and the latest guests on the podcast. They see products that are radically adaptive to our situational needs and collaborate with users in ways that seemed insane a few years ago. Listen on Spotify | Listen on Apple PodcastsBut what struck me the most were three things:* Veronika, a GenZer who figuratively grew up inside of tech because of her father’s work, sees the role of AI much differently than what us older folk would expect. There’s an awkward comfort with the centralization of power within these systems and the expectation that we, the users, will decide whether it is used for good or bad.* Not building towards personalization. Josh knows that it requires far too much data for a system to understand us and what we truly need. So they’re better suited to inferring where we are in our journey, making assumptions about what might have changed about us, and adapting to meet us where we are.* Josh is a champion for embracing the weirdness of AI. Rather than be intimidated and worried about hallucinations, use the not-so-perfect technology in ways that provide unexpected results. The counter-point to intelligent products continues to be how much intelligence a user wants and how much personal information they are willing to give up for it. There’s nothing more uncomfortable than a salesperson who doesn’t get your signals.Adobe’s Project Concept is the start of something hugeEmbracing the weirdness is exactly what Adobe’s new product, Project Concept does. Better you watch the video than me try and explain. It will be interesting to see how agencies respond to the further commoditization of their expertise.Always remember, GenAI is great at the boring stuffAmazon, in its quest for greater efficiency, has developed new systems to shave seconds off each package delivery and to help customers make faster buying choices, even for new product types that they may know little about. The company announced Wednesday it has created spotlights within its trucks to guide delivery people to packages for each stop along a route."When we speed up deliveries, customers shop more," said Doug Herrington, CEO of Amazon worldwide stores in remarks at the event. "Once a customer experiences fast delivery, they will come back sooner and shop more."Interestingly, this also highlights the tech’s ability to imagine solutions to problems that humans may not be able to see otherwise. You could call that embracing the weirdness again. We’ll go into this conversation in detail when we interview Lisa Weaver-Lambert, the author of The AI Value Playbook. In the book she interviewed business leaders to document exactly where and how AI has been delivering value.Multi-modal AI: 8 ways computer vision will change our livesWhile GenAI has been monopolizing the headlines, Apple, Meta, and Snap continue to invest in augmented reality headsets. Apple's Vision Pro landed with a thud —largely due to the price and home-bound use cases— but the others stirred buzz because they focused on lightweight and fashionable eyewear (courtesy of their partnership with Ray-Ban).We've been here before though. Google Glass famously failed. And no one remembers Snap's previous eyewear.But now is different.AI researchers have made huge advancements related to computer vision. If AI enables computers to think, computer vision enables them to see, observe and understand.Continue reading the article on LinkedIn…jmlg1PjBPcyf8mwPJYsfWant to join as a contributor?Contact us info@designof.ai to help us collect the best resources about how AI is shaping the world around us.Thanks for reading Design of AI: News & resources for product teams! Subscribe for free to receive new posts and support my work. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com

Join Kristie J. Fisher, Sr. Director of Global User Research at PlayStation Studios, as she shares insights from her impressive journey in the gaming industry. Discover the unique challenges of designing GenAI products, where user control meets the leap of faith in technology. Learn about evolving research methodologies that adapt to digital trends, the importance of crafting joy-focused experiences, and redefining success through new user-centered metrics. Kristie's expertise bridges gaming, psychology, and product strategy to enhance user engagement.

Episode 17. Our guest is Glenn MacDonald who was Spotify’s Data Alchemist, building it into an algorithmic powerhouse.We’re critically evaluating algorithms' effectiveness and why GenAI probably isn’t the best technology for many problems.Some key insights:#1. As Spotify's former data alchemist, I expected huge advocacy for hashtag#ML & hashtag#AI as a predictive technology. Instead, we must not play god with algos. They should be assistive tool to get people to where they're headed. Prediction leads to errors.#2. You must be able to evaluate algorithms. Too often we're deploying fancy tech with no way to know it is performing better than an alternative. hashtag#GenAI has a huge risk of this because the assumption is that it solved everything. But the cost of deploying it is also very high."I think the main thing I've learned Is actually not to think about it as prediction, I think the thing that happens to you when you start thinking about things as prediction, and I think this applies to thinking about LLM, LLM outputs as predicting text. It also applies to A& R and music as like predicting hit artists. The moment you start thinking about it as prediction, you've sort of internalized sort of ugly idea that the future is kind of determined and you're just attempting to guess what it's going to be and thus profit by anticipation. And I think it's a lot more productive to not think about the future as something you're predicting, but it's something you're making. ""I think a lot of the time we evaluate new tech against really Poor baselines, like against randomness or against the most popular things, or like you said, against just like our intuitive guesses. And in those contexts, sometimes the fancy tools seem like, Oh, they're clearly better. But then when you compare them against, Oh, what if we just did some math and you realize. Oh, the math's even better. It's a lot simpler. "The episode is hosted by:Arpy Dragffy Guerrero (Founder & Head of product strategy, PH1 Research) https://www.linkedin.com/in/adragffy/Brittany Hobbs (VP Insights, Huge) https://www.linkedin.com/in/brittanyhobbs/Glenn McDonald is a music evangelist, algorithm designer, software engineer and technology strategist. He created the music-exploration website Every Noise at Once, and for 12 years was the Data Alchemist at the Echo Nest and Spotify. He has written about music online since before "blog" was a word, and his first offline book, You Have Not Yet Heard Your Favourite Song: How Streaming Changes Music, is available now from Canbury Press.00:24 Meet Glenn MacDonald: Spotify's Data Alchemist01:50 The Evolution of Music Discovery08:39 The Role of AI in Music and Beyond13:29 Challenges and Future of AI in Music29:14 Navigating AI in the Workplace31:25 Designing User-Friendly Algorithms34:59 Challenges with Algorithmic Recommendations39:42 Evaluating AI and User Testing47:41 The Future of Music and AIThank you for listening to the Design of AI podcast. We interview leaders and practitioners at the forefront of AI. If you like this episode please remember to leave a rating and to follow us on your favorite podcast app.Take part in the conversations about AI https://www.linkedin.com/company/designofai/And subscribe to our newsletter for additional resources https://designofai.substack.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit designofai.substack.com