Humans of Martech

Careers place a ton of demand on energy and attention way before results start to stabilize. Many operators discover that health and routine determine how long they can operate at a high level.I spoke with 50 people working in martech and operations about how they stay happy under pressure. This 3 part series – titled “50 Operators share the systems that keep them happy” explores each of these layers through the lived experience of operators who feel the same pressure you probably feel right now.Today we start with part 1: stability through routines, boundaries, and systems that protect the body and mind. We’ll hear from 15 people:(00:00) - Teaser (01:05) - Intro (01:30) - In This Episode (04:09) - Austin Hay: Building Non Negotiables (08:06) - Sundar Swaminathan: Systems That Prevent Stress (12:33) - Elena Hassan: Normalizing Stress (14:32) - Sandy Mangat: Managing Energy (16:31) - Constantine Yurevich: Designing Work That Matches Personal Energy (19:05) - Keith Jones: Intentional Work Rhythms (23:58) - Olga Andrienko: Daily Health Routines (26:06) - Sarah Krasnik Bedell: Outdoor Routines (27:21) - Zach Roberts: Physical Reset Rituals Outside Work (28:57) - Jane Menyo: Recovery Cycles (31:56) - Angela Vega: Chosen Challenges and Recovery Cycles (36:09) - Megan Kwon: Presence Built Into the Day (37:50) - Nadia Davis: Calendar Discipline (39:36) - Henk-jan ter Brugge: Planning the Week as a Constraint System (43:15) - Ankur Kothari: Personal Metrics (44:07) - Outro Austin Hay: Building Non NegotiablesOur first guest is Austin Hay, he’s a co-founder, a teacher, a martech advisor, but he’s also a husband, a dog dad, a student, water skiing fanatic, avid runner, a certified financial planner, and a bunch more stuff... Daily infrastructure shows up through repetition, discipline, and choices that protect energy before anything else competes for it. Austin grounds happiness in curiosity, but that curiosity only thrives when supported by sleep, movement, and time that belongs to no employer. Learning stays fun because it is not treated as another performance metric. It remains part of who he is rather than something squeezed into the margins of an already crowded day.Mental and physical health shape his schedule in visible ways. Austin treats them as operating requirements rather than aspirations. His days include a short list of behaviors that carry disproportionate impact:Regular sleep with a consistent bedtime.Exercise that creates physical fatigue and mental quiet.Relationships that exist entirely outside work.Hobbies and games that feel restorative rather than productive.These habits rarely earn praise, which explains why they erode first under pressure. In his twenties, Austin chased work, clients, and money with intensity. He told himself the rest would come later. That promise held eventually, but the gap years carried a cost. He remembers moments of looking in the mirror and feeling uneasy about the life he was assembling, despite checking every external box.Trade-offs now anchor his thinking. Austin frames decisions as equations involving time, energy, and outcomes. Goals demand inputs, and inputs consume limited resources. Avoiding that math leads to exhaustion and resentment. Facing it creates clarity. Many people resist this step because it forces hard choices into daylight. The industry rewards the appearance of doing everything, even when the math never works.“I view a lot of decisions and outcomes in life as trade-offs. At the end of the day, that’s what most things boil down to.”Sleep makes the equation tangible. Austin aims for bed around 9 or 9:30 each night because his mornings require focus, training, and sustained energy. He needs seven and a half hours of sleep to function well. That requirement dictates the rest of the day. Social plans adjust. Work compresses. Goals remain achievable because the system supports them.He defines what he wants to pursue.He calculates the energy required.He locks in non negotiables that keep the math honest.That structure removes constant negotiation with himself. The system holds even when motivation dips or distractions multiply.Key takeaway: Daily infrastructure depends on non negotiables that protect sleep, health, and energy. Clear priorities, visible trade-offs, and repeatable routines create careers that stay durable under pressure.Sundar Swaminathan: Systems That Prevent StressNext up is Sundar Swaminathan, Former Head of Marketing Science at Uber, Author & Host of the experiMENTAL Newsletter & Podcast. He’s also a husband, a father and a well traveled home chef, amateur chess master.Stress prevention sits at the center of Sundar’s daily system for staying happy and effective at work. A concentrated period of personal loss collapsed any illusion that stress deserved patience or tolerance. Three deaths in three weeks compressed time, sharpened perspective, and forced a reassessment of what stress actually costs. Stress drains energy first, then attention, then presence. A career cannot outrun that erosion for long.Control defines the structure of his days. Sundar organizes work and life decisions around what he can actively influence and treats everything else with intentional distance. That discipline reduces noise and preserves energy. The system stays practical because complexity invites self-deception.Work within control receives effort, follow-through, and care.Work outside control receives acknowledgment and release.Outcomes matter, but the quality of effort matters more.Emotional reactions get examined instead of amplified.That repetition builds resilience as a habit rather than a personality trait. Over time, the body learns that urgency does not improve outcomes, while steadiness often does.Long-term thinking provides ballast when short-term chaos shows up. Sundar frames happiness the way experienced investors frame capital. Daily decisions compound quietly. Some weeks produce visible setbacks. The trend still moves when investments stay consistent. He invests daily in relationships, energy, and directionally sound choices. Moving his family to Amsterdam followed that logic. The decision carried friction and uncertainty, yet it expanded daily happiness in ways that cautious planning rarely delivers.“If you keep investing in yourself and the relationships that matter every day, the long-term trend moves up.”Priorities reinforce the system. Sundar grew up with career dominance baked into identity. Family now anchors that identity with clarity. That hierarchy shapes calendars, boundaries, and energy allocation. Work performance benefits from this structure because focus sharpens when limits exist. Activities that drain energy lose priority quickly. Unhappiness spreads fast and contaminates every adjacent part of life.Environment completes the infrastructure. Daily systems matter as much as mindset. Living in a place where flexibility exists without negotiation removes friction before it forms. Parenting logistics do not create anxiety. Time away from work does not require justification. Many expat families notice similar relief because daily life carries less ambient pressure. When systems support people, stress loses room to grow.Key takeaway: Sustainable careers rely on daily infrastructure that prevents stress before it accumulates. Clear control boundaries, long-term thinking, and supportive environments create stability that protects energy and compounds over time.Elena Hassan: Normalizing Str...

Phyllis Fang, Head of Marketing at Transcend focused on data privacy and trust infrastructure, and former Uber safety marketer. She talks about permissioned data systems and how consent powers personalization. She explains auditing data touchpoints, building a marketing trust stack, and why consent can become a revenue lever. She also discusses designing teams for curiosity and skills that matter in the AI era.

In this discussion, Jordan Resnick, Senior Director of Marketing Operations at CHEQ, breaks down the complexities of identifying real versus fake traffic. He highlights the surge of AI-driven bots and explains how they mimic genuine user behavior. Jordan reveals strategies for detecting bot activity through behavioral patterns and offers practical solutions for go-to-market teams to mitigate lead pollution. He also discusses the importance of transparency in reporting and adapting marketing systems for the evolving landscape of machine customers.

What’s up everyone, today we have the honor of sitting down with Aleyda Solís, SEO and AI search consultant. (00:00) - Intro (01:17) - In This Episode (04:55) - Crawlability Requirements for AI Search Engines (12:21) - LLMs As A New Search Channel In A Multi Platform Discovery System (18:42) - AI Search Visibility Analysis for SEO Teams (29:17) - Creating Brand Led Informational Content for AI Search (35:51) - Choosing SEO Topics That Drive Brand-Aligned Demand (45:50) - How Topic Level Analysis Shapes AI Search Strategy (50:01) - LLM Search Console Reporting Expectations (52:09) - Why LLM Search Rewards Brands With Real Community Signals (55:12) - Prioritizing Work That Matches Personal Purpose Summary: AI search is rewriting how people find information, and Aleyda explains the shift with clear, practical detail. She has seen AI crawlers blocked without anyone noticing, JavaScript hiding full sections of sites, and brands interpreting results that were never based on complete data. She shows how users now move freely between Google, TikTok, Instagram, and LLMs, which pushes teams to treat discovery as a multi-platform system. She encourages you to verify your AI visibility, publish content rooted in real customer language, and use topic clusters to anchor strategy when prompts scatter. Her closing point is simple. Community chatter now shapes authority, and AI models pay close attention to it.About AleydaAleyda Solís is an international SEO and AI search optimization consultant, speaker, and author who leads Orainti, the boutique consultancy known for solving complex, multi-market SEO challenges. She’s worked with brands across ecommerce, SaaS, and global marketplaces, helping teams rebuild search foundations and scale sustainable organic growth.She also runs three of the industry’s most trusted newsletters; SEOFOMO, MarketingFOMO, and AI Marketers, where she filters the noise into the updates that genuinely matter. Her free roadmaps, LearningSEO.io and LearningAIsearch.com, give marketers a clear, reliable path to building real skills in both SEO and AI search.Crawlability Requirements for AI Search EnginesCrawlability shapes everything that follows in AI search. Aleyda talks about it with the tone of someone who has seen far too many sites fail the basics. AI crawlers behave differently from traditional search engines, and they hit roadblocks that most teams never think about. Hosting rules, CDN settings, and robots files often permit Googlebot but quietly block newer user agents. You can hear the frustration in her voice when she describes audit after audit where AI crawlers never reach critical sections of a site."You need to allow AI crawlers to access your content. The rules you set might need to be different depending on your context."AI crawlers also refuse to process JavaScript. They ingest raw markup and move on. Sites that lean heavily on client-side rendering lose entire menus, product details, pricing tables, and conversion paths. Aleyda describes this as a structural issue that forces marketers to confront their technical debt. Many teams have spent years building front-ends with layers of JavaScript because Google eventually figured out how to handle it. AI crawlers skip that entire pipeline. Simpler pages load faster, reveal hierarchy immediately, and give AI models a complete picture without extra processing.Search behavior adds new pressure. Aleyda points to OpenAI’s published research showing a rise in task-oriented queries. Users ask models to complete goals directly and skip the page-by-page exploration we grew up optimizing for. You need clarity about which tasks intersect with your offerings. You need to build content that satisfies those tasks without guessing blindly. Aleyda urges teams to validate this with real user understanding because generic keyword tools cannot describe these new behaviors accurately.Authority signals shift too. Mentions across credible communities carry weight inside AI summaries. Aleyda explains it as a natural extension of digital PR. Forums, newsletters, podcasts, social communities, and industry roundups form a reputation map that AI crawlers use as context. Backlinks still matter, but mentions create presence in a wider set of conversations. Strong SEO programs already invest in this work, but many teams still chase link volume while ignoring the broader network of references that shape brand perception.Measurement evolves alongside all of this. Aleyda encourages operators to treat AI search as both a performance channel and a visibility channel. You track presence inside responses. You track sentiment and frequency. You monitor competitors that appear beside you or ahead of you. You map how often your brand appears in summaries that influence purchase decisions. Rankings and click curves do not capture the full picture. A broader measurement model captures what these new systems actually distribute.Key takeaway: Build crawlability for AI search with intention. Confirm that AI crawlers can access your content, and remove JavaScript barriers that hide essential information. Map the task-driven behaviors that align with your products so you invest in content that meets real user goals. Strengthen your reputation footprint by earning mentions in communities that influence AI summaries. Expand your measurement model so you can track visibility, sentiment, and placement inside AI-generated results. That way you can compete in a search environment shaped by new rules and new signals.LLMs As A New Search Channel In A Multi Platform Discovery SystemSEO keeps getting declared dead every time Google ships a new interface, yet actual search behavior keeps spreading across more surfaces. Aleyda reacted to the “LLMs as a new channel” framing with immediate agreement because she sees teams wrestling with a bigger shift. They still treat Google as the only gatekeeper, even though users now ask questions, compare products, and verify credibility across several platforms at once. LLMs, TikTok, Instagram, and traditional search engines all function as parallel discovery layers, and the companies that hesitate to accept this trend end up confused about where SEO fits.Aleyda pointed to the industry’s long dependence on Google and described how that dependence shaped expectations. Many teams built an entire worldview around a single SERP format, a single set of ranking factors, and a single customer entry point. Interface changes feel existential because the discipline was defined too narrowly for too long. She sees this tension inside consulting projects when stakeholders ask whether SEO is dying instead of asking where their audience now searches for answers.Retail clients provided her clearest examples. They already treat TikTok and Instagram as core search environments. They ask for guidance on how to structure content so it gets discovered through platform specific signals. They ask for clarity on how product intent gets inferred through tags, comments, watch time, and creator interactions. Their questions treat search as a distributed system, and their behavior hints at what the wider market will adopt. Aleyda considers this a preview, because younger customers rarely begin their journey inside a traditional search engine.Her story from a conference in China made the point even sharper. She explained how Baidu no longer carries the gravitational pull many Western marketers assume. People gather information through Red Note, Douyin, and several specialized platforms, and they assemble answers through a blend of formats. That experience changed Aleyda’s expectations for Western markets. She believes...

What’s up everyone, today we have the honor of sitting down with the legendary Scott Brinker, a rare repeat guest, the Martech Landscape creator, the Author of Hacking Marketing, The Godfather of Martech himself.(00:00) - Intro (01:12) - In This Episode (05:09) - Scott Brinker’s Guidance For Marketers Rethinking Their Career Path (11:27) - If You Started Over in Martech, What Would You Learn First (16:47) - People Side (21:13) - Life Long Learning (26:20) - Habits to Stay Ahead (32:14) - Why Deep Specialization Protects Marketers From AI Confusion (37:27) - Why Technical Skills Decide the Future of Your Marketing Career (41:00) - Why Change Leadership Matters More Than Technical AI Skills (47:11) - How MCP Gives Marketers a Path Out of Integration Hell (52:49) - Why Heterogeneous Stacks are the Default for Modern Marketing Teams (54:51) - How To Build A Martech Messaging BS Detector (59:37) - Why Your Energy Grows Faster When You Invest in Other People Summary: Scott Brinker shares exactly where he would focus if he reset his career today, and his answer cuts through the noise. He’d build one deep specialty to judge AI’s confident mistakes, grow cross-functional range to bridge marketing and engineering, and lean into technical skills like SQL and APIs to turn ideas into working systems. He’d treat curiosity as a steady rhythm instead of a rigid routine, learn how influence actually moves inside companies, and guide teams through change with simple, human clarity. His take on composability, MCP, and vendor noise rounds out a clear roadmap for any marketer trying to stay sharp in a chaotic industry.About ScottScott has spent his career merging the world of marketing and technology and somehow making it look effortless. He co-founded ion interactive back when “interactive content” felt like a daring experiment, then opened the Chief Marketing Technologist blog in 2008 to spark a conversation the industry didn’t know it needed. He sketched the very first Martech Landscape when the ecosystem fit on a single page with about 150 vendors, and later brought the MarTech conference to life in 2014, where he still shapes the program. Most recently, he guided HubSpot’s platform ecosystem, helping the company stay connected to a martech universe that’s grown to more than 15,000 tools. Today, Scott continues to helm chiefmartec.com, the well the entire industry keeps returning to for clarity, curiosity, and direction.Scott Brinker’s Guidance For Marketers Rethinking Their Career PathMid career marketers keep asking themselves whether they should stick with the field or throw everything out and start fresh. Scott relates to that feeling, and he talks about it with a kind of grounded humor. He describes his own wandering thoughts about running a vineyard, feeling the soil under his shoes and imagining the quiet. Then he remembers the old saying about wineries, which is that the only guaranteed outcome is a smaller bank account. His story captures the emotional drift that comes with burnout. People are not always craving a new field. They are often craving a new relationship with their work.Scott moves quickly to the part that matters. He directs his attention to AI because it is reshaping the field faster than many teams can absorb. He explains that someone could spend every hour of the week experimenting and still only catch a fraction of what is happening. He sees that chaos as a signal. Overload creates opportunity, and the people who step toward it gain an advantage. He urges mid career operators to lean into the friction and build new muscle. He even calls out how many people will resist change and cling to familiar workflows. He views that resistance as a gift for the ones willing to explore.“People who lean into the change really have the opportunity to differentiate themselves and discover things.”Scott brings back a story from a napkin sketch. He drew two curves, one for the explosive pace of technological advancement and one for the slower rhythm of organizational change. The curves explain the tension everyone feels. Teams operate on slower timelines. Tools operate on faster ones. The gap between those curves is wide, and professionals who learn to navigate that space turn themselves into catalysts inside their companies. He sees mid career marketers as prime candidates for this role because they have enough lived experience to understand where teams stall and enough hunger to explore new territory.Scott encourages people to channel their curiosity into specific work. He suggests treating AI exploration like a practice and not like a trend. A steady rhythm of experiments helps someone grow their internal influence. Better experiments produce useful artifacts. These artifacts often become internal proof points that accelerate change. He believes the next wave of opportunity belongs to people who document what they try, translate what they learn, and help their companies adapt at a pace that competitors cannot easily match.Scott’s message carries emotional weight. He does not downplay the exhaustion in the field, but he reinforces that reinvention often happens inside the work, not outside of it. People who move toward new capabilities build careers that feel less fragile and more future proof.Key takeaway: Mid career marketers build real leverage by running small AI experiments inside their current roles, documenting the results, and using those learnings to influence how their companies adapt. Start with narrow tests that affect your daily work, share clear outcomes with your team, and repeat the cycle. That way you can build rare credibility and position yourself as the person who accelerates organizational change.If You Started Over in Martech, What Would You Learn FirstCross functional fluency shapes careers in a way that shiny frameworks never will, and Scott calls this out with blunt honesty. He shares how his early career lived in two worlds, writing brittle code on one side and trying to understand marketers on the other. He laughs about being a “very mediocre software engineer” who built things that probably should not have survived contact with production. That imperfect background still gave him an edge, because technical fluency mixed with genuine curiosity about marketing created a role no one else was filling. He could explain system behavior in a language marketers understood, and he could explain marketer behavior in a language engineers tolerated. That unusual pairing delivered force inside teams that usually worked in isolation.Scott makes the case that readers can build similar momentum by leaning into roles where disciplines collide. He argues that the most useful skills often come from pairing two domains and learning how they influence each other. He highlights combinations like:Marketing and IT for people who enjoy systems.Marketing and finance for people drawn to modeling and forecasting.Marketing and sales for people who want to connect customer signals with revenue conversations.He believes these intersections are crowded with opportunity because organizations rarely invest enough in communication across teams. You can create real leverage when you speak multiple operational languages with confidence.“The ability to serve as a bridge of cross pollinating between multiple disciplines has a lot of opportunity.”Scott also shares the part he would invest in first if he were twenty two again. He spent years focusing almost entirely on what systems could do. He cared deeply about architecture diagrams and technical possibility, and he assumed people would adopt anything that worked. He later realized that adoption follows trust,...

What’s up everyone, today we have the pleasure of sitting down with Matthew Castino, Marketing Measurement Science Lead @ Canva.(00:00) - Intro (01:10) - In This Episode (03:50) - Canva’s Prioritization System for Marketing Experiments (11:26) - What Happened When Canva Turned Off Branded Search (18:48) - Structuring Global Measurement Teams for Local Decision Making (24:32) - How Canva Integrates Marketing Measurement Into Company Forecasting (31:58) - Using MMM Scenario Tools To Align Finance And Marketing (37:05) - Why Multi Touch Attribution Still Matters at Canva (42:42) - How Canva Builds Feedback Loops Between MMM and Experiments (46:44) - Canva’s AI Workflow Automation for Geo Experiments (51:31) - Why Strong Coworker Relationships Improve Career Satisfaction Summary: Canva operates at a scale where every marketing decision carries huge weight, and Matt leads the measurement function that keeps those decisions grounded in science. He leans on experiments to challenge assumptions that models inflate. As the company grew, he reshaped measurement so centralized models stayed steady while embedded data scientists guided decisions locally, and he built one forecasting engine that finance and marketing can trust together. He keeps multi touch attribution in play because user behavior exposes patterns MMM misses, and he treats disagreements between methods as signals worth examining. AI removes the bottlenecks around geo tests, data questions, and creative tagging, giving his team space to focus on evidence instead of logistics. About MatthewMatthew Castino blends psychology, statistics, and marketing intuition in a way that feels almost unfair. With a PhD in Psychology and a career spent building measurement systems that actually work, he’s now the Marketing Measurement Science Lead at Canva, where he turns sprawling datasets and ambitious growth questions into evidence that teams can trust.His path winds through academia, health research, and the high-tempo world of sports trading. At UNSW, Matt taught psychology and statistics while contributing to research at CHETRE. At Tabcorp, he moved through roles in customer profiling, risk systems, and US/domestic sports trading; spaces where every model, every assumption, and every decision meets real consequences fast. Those years sharpened his sense for what signal looks like in a messy environment.Matt lives in Australia and remains endlessly curious about how people think, how markets behave, and why measurement keeps getting harder, and more fun.Canva’s Prioritization System for Marketing ExperimentsCanva’s marketing experiments run in conditions that rarely resemble the clean, product controlled environment that most tech companies love to romanticize. Matthew works in markets filled with messy signals, country level quirks, channel specific behaviors, and creative that behaves differently depending on the audience. Canva built a world class experimentation platform for product, but none of that machinery helps when teams need to run geo tests or channel experiments across markets that function on completely different rhythms. Marketing had to build its own tooling, and Matthew treats that reality with a mix of respect and practicality.His team relies on a prioritization system grounded in two concrete variables.SpendUncertaintyLarge budgets demand measurement rigor because wasted dollars compound across millions of impressions. Matthew cares about placing the most reliable experiments behind the markets and channels with the biggest financial commitments. He pairs that with a very sober evaluation of uncertainty. His team pulls signals from MMM models, platform lift tests, creative engagement, and confidence intervals. They pay special attention to MMM intervals that expand beyond comfortable ranges, especially when historical spend has not varied enough for the model to learn. He reads weak creative engagement as a warning sign because poor engagement usually drags efficiency down even before the attribution questions show up.“We try to figure out where the most money is spent in the most uncertain way.”The next challenge sits in the structure of the team. Matthew ran experimentation globally from a centralized group for years, and that model made sense when the company footprint was narrower. Canva now operates in regions where creative norms differ sharply, and local teams want more authority to respond to market dynamics in real time. Matthew sees that centralization slows everything once the company reaches global scale. He pushes for embedded data scientists who sit inside each region, work directly with marketers, and build market specific experimentation roadmaps that reflect local context. That way experimentation becomes a partner to strategy instead of a bottleneck.Matthew avoids building a tower of approvals because heavy process often suffocates marketing momentum. He prefers a model where teams follow shared principles, run experiments responsibly, and adjust budgets quickly. He wants measurement to operate in the background while marketers focus on creative and channel strategies with confidence that the numbers can keep up with the pace of execution.Key takeaway: Run experiments where they matter most by combining the biggest budgets with the widest uncertainty. Use triangulated signals like MMM bounds, lift tests, and creative engagement to identify channels that deserve deeper testing. Give regional teams embedded data scientists so they can respond to real conditions without waiting for central approval queues. Build light guardrails, not heavy process, so experimentation strengthens day to day marketing decisions with speed and confidence.What Happened When Canva Turned Off Branded SearchGeographic holdout tests gave Matt a practical way to challenge long-standing spend patterns at Canva without turning measurement into a philosophical debate. He described how many new team members arrived from environments shaped by attribution dashboards, and he needed something concrete that demonstrated why experiments belong in the measurement toolkit. Experiments produced clearer decisions because they created evidence that anyone could understand, which helped the organization expand its comfort with more advanced measurement methods.The turning point started with a direct question from Canva’s CEO. She wanted to understand why the company kept investing heavily in bidding on the keyword “Canva,” even though the brand was already dominant in organic search. The company had global awareness, strong default rankings, and a product that people searched for by name. Attribution platforms treated branded search as a powerhouse channel because those clicks converted at extremely high rates. Matt knew attribution would reinforce the spend by design, so he recommended a controlled experiment that tested actual incrementality."We just turned it off or down in a couple of regions and watched what happened."The team created several regional holdouts across the United States. They reduced bids in those regions, monitored downstream behavior, and let natural demand play out. The performance barely moved. Growth held steady and revenue held steady. The spend did not create additional value at the level the dashboards suggested. High intent users continued converting, which showed how easily attribution can exaggerate impact when a channel serves people who already made their decision.The outcome saved Canva millions of dollars, and the savings were immediately reallocated to areas with better leverage. The win carried emotional weight inside the company because it replaced speculati...

What’s up everyone, today we have the pleasure of sitting down with Anna Aubuchon, VP of Operations at Civic Technologies.(00:00) - Intro (01:15) - In This Episode (04:15) - How AI Flipped the Build Versus Buy Decision (07:13) - Redrawing What “Complex” Means (12:20) - Why In House AI Provides Better Economics And Control (15:33) - How to Treat AI as an Insourcing Engine (21:02) - Moving BI Workloads Out of Dashboards and Into LLMs (31:37) - Guardrails That Keep AI Querying Accurate (38:18) - Using Role Based AI Guardrails Across MCP Servers (44:43) - Ops People are Creators of Systems Rather Than Maintainers of Them (48:12) - Why Natural Language AI Lowers the Barrier for First-Time Builders (52:31) - Technical Literacy Requirements for Next Generation Operators (56:46) - Why Creative Practice Strengthens Operational Leadership Summary: AI has reshaped how operators work, and Anna lays out that shift with the clarity of someone who has rebuilt real systems under pressure. She breaks down how old build versus buy habits hold teams back, how yearly AI contracts quietly drain momentum, and how modern integrations let operators assemble powerful workflows without engineering bottlenecks. She contrasts scattered one-off AI tools with the speed that comes from shared patterns that spread across teams. Her biggest story lands hard. Civic replaced slow dashboards and long queues with orchestration that pulls every system into one conversational layer, letting people get answers in minutes instead of mornings. That speed created nerves around sensitive identity data, but tight guardrails kept the team safe without slowing anything down. Anna ends by pushing operators to think like system designers, not tool babysitters, and to build with the same clarity her daughter uses when she describes exactly what she wants and watches the system take shape.About AnnaAnna Aubuchon is an operations executive with 15+ years building and scaling teams across fintech, blockchain, and AI. As VP of Operations at Civic Technologies, she oversees support, sales, business operations, product operations, and analytics, anchoring the company’s growth and performance systems.She has led blockchain operations since 2014 and built cross-functional programs that moved companies from early-stage complexity into stable, scalable execution. Her earlier roles at Gyft and Thomson Reuters focused on commercial operations, enterprise migrations, and global team leadership, supporting revenue retention and major process modernization efforts.How AI Flipped the Build Versus Buy DecisionAI tooling has shifted so quickly that many teams are still making decisions with a playbook written for a different era. Anna explains that the build versus buy framework people lean on carries assumptions that no longer match the tool landscape. She sees operators buying AI products out of habit, even when internal builds have become faster, cheaper, and easier to maintain. She connects that hesitation to outdated mental models rather than actual technical blockers.AI platforms keep rolling out features that shrink the amount of engineering needed to assemble sophisticated workflows. Anna names the layers that changed this dynamic. System integrations through MCP act as glue for data movement. Tools like n8n and Lindy give ops teams workflow automation without needing to file tickets. Then ChatGPT Agents and Cloud Skills launched with prebuilt capabilities that behave like Lego pieces for internal systems. Direct LLM access removed the fear around infrastructure that used to intimidate nontechnical teams. She describes the overall effect as a compression of technical overhead that once justified buying expensive tools.She uses Civic’s analytics stack to illustrate how she thinks about the decision. Analytics drives the company’s ability to answer questions quickly, and modern integrations kept the build path light. Her team built the system because it reinforced a core competency. She compares that with an AI support bot that would need to handle very different audiences with changing expectations across multiple channels. She describes that work as high domain complexity that demands constant tuning, and the build cost would outweigh the value. Her team bought that piece. She grounds everything in two filters that guide her decisions: core competency and domain complexity.Anna also calls out a cultural pattern that slows AI adoption. Teams buy AI tools individually and create isolated pockets of automation. She wants teams to treat AI workflows as shared assets. She sees momentum building when one group experiments with a workflow and others borrow, extend, or remix it. She believes this turns AI adoption into a group habit rather than scattered personal experiments. She highlights the value of shared patterns because they create a repeatable way for teams to test ideas without rebuilding from scratch.She closes by urging operators to update their decision cycle. Tooling is evolving at a pace that makes six month old assumptions feel stale. She wants teams to revisit build versus buy questions frequently and to treat modern tools as a prompt to redraw boundaries rather than defend old ones. She frames it as an ongoing practice rather than a one time decision.Key takeaway: Reassess your build versus buy decisions every quarter by measuring two factors. First, identify whether the workflow strengthens a core competency that deserves internal ownership. Second, gauge the domain complexity and decide whether the function needs constant tuning or specialized expertise. Use modern integration layers, workflow builders, and direct LLM access to assemble internal systems quickly. Build the pieces that reinforce your strengths, buy the pieces that demand specialized depth, and share internal workflows so other teams can expand your progress.Why In House AI Provides Better Economics And ControlAI tooling has grown into a marketplace crowded with vendors who promise intelligence, automation, and instant transformation. Anna watches teams fall into these patterns with surprising ease. Many of the tools on the market run the same public models under new branding, yet buyers often assume they are purchasing deeply specialized systems trained on inaccessible data. She laughs about driving down the 101 and seeing AI billboards every few minutes, each one selling a glossy shortcut to operational excellence. The overcrowding makes teams feel like they should buy something simply because everyone else is buying something, and that instinct shifts AI procurement from a strategic decision into a reflex."A one year agreement might as well be a decade in AI right now."Anna has seen how annual vendor contracts slow companies down. The moment a team commits to a year long agreement, the urgency to evaluate alternatives vanishes. They adopt a “set it and forget it” mindset because the tool is already purchased, the budget is already allocated, and the contract already sits in legal. AI development moves fast. Contract cycles do not. That mismatch creates friction that becomes expensive, especially when new models launch every few weeks and outperform the ones you purchased only months earlier. Teams do not always notice the cost of stagnation because it creeps in quietly.Anna lays out a practical build versus buy framework. Teams should inspect whether the capability touches their core competency, their customer experience, or their strategic distinctiveness. If it does, then in house AI provides more long term value. It lets the company shape the model around real customer patterns. It keeps experimentation in motion instead...

What’s up everyone, today we have the pleasure of sitting down with Pam Boiros, Fractional CMO and Marketing advisor, and Co-Founder Women Applying AI.(00:00) - Intro (01:13) - In This Episode (03:49) - How To Audit Data Fingerprints For AI Bias In Marketing (07:39) - Why Emotional Intelligence Improves AI Prompting Quality (10:14) - Why So Many Women Hesitate (15:40) - Why Collaborative AI Practice Builds Confidence In Marketing Ops Teams (18:31) - How to Go From AI Curious to AI Confident (24:32) - Joining The 'Women Applying AI' Community (27:18) - Other Ways to Support Women in AI (28:06) - Role Models and Visibility (32:55) - Leadership’s Role in Inclusion (35:57) - Mentorship for the AI Era (38:15) - Why Story Driven Communities Strengthen AI Adoption for Women (42:17) - AI’s Role in Women’s Worklife Harmony (45:22) - Why Personal History Strengthens Creative Leadership Summary: Pam delivers a clear, grounded look at how women learn and lead with AI, moving from biased datasets to late-night practice sessions inside Women Applying AI. She brings sharp examples from real teams, highlights the quiet builders shaping change, and roots her perspective in the resilience she learned from the women in her own family. If you want a straightforward view of what practical, human-centered AI adoption actually looks like, this episode is worth your time.About PamPam Boiros is a consultant who helps marketing teams find direction and build plans that feel doable. She leads Marketing AI Jump Start and works as a fractional CMO for clients like Reclaim Health, giving teams practical ways to bring AI into their day-to-day work. She’s also a founding member of Women Applying AI, a new community that launched in Sep 2025 that creates a supportive space for women to learn AI together and grow their confidence in the field.Earlier in her career, Pam spent 12 years at a fast-growing startup that Skillsoft later acquired, then stepped into senior marketing and product leadership there for another three and a half years. That blend of startup pace and enterprise structure shapes how she guides her clients today.How To Audit Data Fingerprints For AI Bias In MarketingAI bias spreads quietly in marketing systems, and Pam treats it as a pattern problem rather than a mistake problem. She explains that models repeat whatever they have inherited from the data, and that repetition creates signals that look normal on the surface. Many teams read those signals as truth because the outputs feel familiar. Pam has watched marketing groups make confident decisions on top of datasets they never examined, and she believes this is how invisible bias gains momentum long before anyone sees the consequences.Pam describes every dataset as carrying a fingerprint. She studies that fingerprint by zooming into the structure, the gaps, and the repetition. She looks for missing groups, inflated representation, and subtle distortions baked into the source. She builds this into her workflow because she has seen how quickly a model amplifies the same dominant voices that shaped the data. She brings up real scenarios from her own career where women were labeled as edge cases in models even though they represented half the customer base. These patterns shape everything from product recommendations to retention scores, and she believes many teams never notice because the numbers look clean and objective."Every dataset has a fingerprint. You cannot see it at first glance, but it becomes obvious once you look for who is overrepresented, who is underrepresented, or who is misrepresented."Pam organizes her process into three cycles that marketers can use immediately.The habit works because it forces scrutiny at every stage, not just at kickoff.Before building, trace the data source, the people represented, and the people missing.While building, stress test the system across groups that usually sit at the margins.After launch, monitor outputs with the same rhythm you use for performance analysis.She treats these cycles as an operational discipline. She compares the scale of bias to a compounding effect, since one flawed assumption can multiply into hundreds of outputs within hours. She has seen pressure to ship faster push teams into trusting defaults, which creates the illusion of objectivity even when the system leans heavily toward one group’s behavior. She wants marketers to recognize that AI audits function like quality control, and she encourages them to build review rituals that continue as the model learns. She believes this daily maintenance protects teams from subtle drift where the model gradually leans toward the patterns it already prefers.Pam views long term monitoring as the part that matters most. She knows how fast AI systems evolve once real customers interact with them. Bias shifts as new data enters the mix. Entire segments disappear because the model interprets their silence as disengagement. Other segments dominate because they participate more often, which reinforces the skew. Pam advocates for ongoing alerts, periodic evaluations, and cross-functional reviews that bring different perspectives into the monitoring loop. She believes that consistent visibility keeps the model grounded in the full customer base.Key takeaway: You can reduce AI bias by treating audits as part of your standard workflow. Trace the origin of every dataset so you understand who shapes the patterns. Stress test during development so you catch distortions early. Monitor outcomes after launch so you can identify drift before it influences targeting, scoring, and personalization. This rhythm gives you a reliable way to detect biased fingerprints, keep systems accountable, and protect real customers from skewed automation.Why Emotional Intelligence Improves AI Prompting QualityEmotional intelligence shapes how people brief AI, and Pam focuses on the practical details behind that pattern. She sees prompting as a form of direction setting, similar to guiding a creative partner who follows every instruction literally. Women often add richer context because they instinctively think through tone, audience, and subtle cues before giving direction. That depth produces output that carries more human texture and brand alignment, and it reduces the amount of rewriting teams usually do when prompts feel thin.Pam also talks about synthetic empathy and how easily teams misread it. AI can generate warm language, but users often sense a hollow quality once they reread the output. She has seen teams trust the first fluent result because it looks polished on the surface. People with stronger emotional intelligence detect when the writing lacks genuine feeling or when it leans on clichés instead of real understanding. Pam notices this most in content meant for sensitive moments, such as apology emails or customer care messages, where the emotional miss becomes obvious."Prompting is basically briefing the AI, and women are natural context givers. We think about tone and audience and nuance, and that is what makes AI output more human and more aligned with the brand."Pam brings even sharper clarity when she moves into analytics. She observes that many marketers chase the top performer without questioning who drove the behavior. She describes moments where curiosity leads someone to discover that a small, highly engaged audience segment pulled the numbers upward. She sees women interrogating patterns by asking:Who showed upWhy they behaved the way they didWhat made the pattern appear more universal than it isThose questions shift analytics from scoreboar...

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