The AI podcast for product teams

Atlassian spent three years connecting 150 billion organizational objects before the results appeared: 44% more accurate AI answers, 48% fewer tokens, a coding agent that reviewed 2 billion lines of code in two minutes. That’s the proof enterprises are pointing to when they argue that context graphs are the unlock. What the benchmark obscures is the order of operations — the graph had to exist before any of those numbers were possible.The reorganization bet is running in parallel, and it’s moving faster than the infrastructure. Airbnb’s CHRO is converting documentation to markdown, building skills libraries, mining meeting recordings before institutional memory disappears — five structural prerequisites before the first agent goes live. Meta is posting $26.8 billion in Q1 profit, laying off 8,000 people, and reporting “horrifically, historically low” employee morale. Both are restructuring around AI. Only one is sequencing it correctly.In AI customer experience, Twilio is working against a Qualtrics finding that 1 in 5 AI interactions delivers zero benefit. Rikki Singh’s diagnosis is precise: the orchestration layer is there, but it’s running without the context layer underneath it. FAQ automation with better packaging is still FAQ automation. The unlock is real — but only when all three pieces are in place, in order. The knowledge worker playbook in this edition addresses the fourth variable: what happens to the people whose roles disappear when the gathering does.Rikki Singh leads product innovation at Twilio — what the company is calling its biggest launch in 17 years. Before Twilio she was at McKinsey, where she co-authored the foundational research on what makes a great PM. The Qualtrics 2026 CX Trends Report found nearly 1 in 5 consumers who used AI customer service saw zero benefit — the baseline she is working against.* Why AI CX is still FAQ automation with better packaging* Why AI spend is as unpredictable as AI upside* The wrapper that makes AI feel like it thinks* Vitamins vs painkillers: the product sense filter* How to protect long-horizon bets inside a big company* Why the brand — not the vendor — owns AI failureListen: Spotify | Apple PodcastsJamil Valliani leads AI product at Atlassian, where he has spent three years building the Teamwork Graph across 300,000 companies. Recorded live at Team ‘26 in Anaheim, where Atlassian demonstrated what connecting 150 billion organizational objects produces: 44% more accurate AI answers using 48% fewer tokens, and a coding agent that reviewed 2 billion lines of code in 2 m

Atlassian connected its AI agents to a richer layer of company knowledge (documents, projects, goals, people) and measured a 44% improvement in answer accuracy using 48% fewer resources. Same models. Different information. Brian Armstrong restructured Coinbase the same week: 14% headcount cut, five management layers maximum. When AI can surface what previously required institutional memory and senior tenure, the organizational layers built around that knowledge become harder to justify.The visible shift gets covered in tech headlines. What gets lost in the announcement energy: none of this works if the company hasn’t decided what it wants AI to do.The more widespread barrier is upstream of governance. Most executives approving AI budgets are working through the aftermath of pilots that underdelivered, first-generation deployments that didn’t survive contact with their actual data, and early model results that left skepticism the current tools have since substantially outrun. That trust deficit — organizations evaluating new AI investment based on experiences two generations old — is where enterprise AI projects most commonly stall. Shadow AI governance and deployment intent are real risks, but they’re downstream of that harder problem. There is no closing the capability gap inside an organization that is quietly waiting for the next deployment to fail too.John Willis co-wrote The DevOps Handbook because software teams were shipping code fast without feedback loops or governance. He sees the same pattern repeating with AI — and he spent five decades documenting what happens when the gap between vendor promises and operational reality gets this wide.* Why shadow AI is more dangerous than an outright ban* Why throughput without governance means instability at scale* Why governance creates flow instead of stopping it* Why most teams have ML evaluation tools when they need audit trails* Why even a five-person startup needs digitally signed records of agent decisions* What AI winters teach us about where we actually are nowListen: Spotify | Apple PodcastsRikki Singh leads product innovation at Twilio — what the company calls its biggest launch in 17 years. Before Twilio she was at McKinsey, where she co-authored the definitive research on what makes a great PM. The Qualtrics 2026 CX Trends Report found n

The Stanford AI Index’s headline is 88% — organizations using AI in some capacity. The Financial Times charted where it actually lands in the workforce: 62% of top-decile earners use it daily, versus 13% at the bottom. Board decks this quarter will cite Stanford. The FT chart is what they’re not showing.The economics that enabled this gap are under pressure. The three-year subsidized era is ending by financial necessity, not choice. The same optimization logic that built social media’s loneliness machine is now embedded in AI products at scale. And in the same week Anthropic’s most capable model autonomously found 271 zero-days in Firefox, two major platforms were breached through third-party integrations. The data and what to do about it follows.Episode 8: The Most Important Data Points in AI Right NowBrittany Hobbs solo — four segments moving from data to strategic implication. Essential for anyone making AI purchasing, hiring, or architecture decisions right now.The Stanford AI Index 2026. 88% organizational adoption is saturation, not a trend. $581 billion invested globally in 2025, up 129% year over year. The US-China AI performance gap collapsed from 17–31 percentage points in 2023 to 2.7% today — on 23 times less investment. China holds 69.7% of global AI patent filings. Architecture and application discipline closed a gap that capital alone could not. Stanford AI Index 2026 | The U.S. Can’t Buy an AI LeadToken economics. Anthropic’s current tiers: Haiku at $1/$5 per million input/output tokens, Sonnet at $3/$15, Opus at $5/$25. A 200-screen product built with Claude Design costs $0.22 for a first draft; the 50-iteration refinement cycle real design work requires runs to ~$2,600, plus $200–$900/month in system updates. Every comparable Figma interaction costs zero. Prompt caching provides ~90% discounts on repeated context; batch processing cuts 50%. Claude Design vs Figma cost breakdown | CNBC: Token economicsApple chose its hardware chief as next CEO. John Ternus — SVP of Hardware Engineering, architect of Apple Silicon — succeeds Tim Cook on September 1st. Johny Srouji, who designed every Apple Silicon chip, becomes Chief Hardware Officer. Apple posted $143.8 billion in Q1 FY2026 (up 16%, $109 billion in services, 92% retention) without shipping an industry-leading AI feature. The next decade of AI is decided at the silicon and device level. <a

Let’s stop pretending. Most AI strategies are just a collection of pilots that nobody had the courage to kill. The data this period is brutal: 95% of genAI pilots stall. Only 11% reach production in financial services. Microsoft — the biggest company in the world, with the best distribution on the planet — just reorganized Copilot because nobody internally could agree on what it was supposed to be. And while enterprises burn cycles debating governance frameworks, a new class of startups is quietly replacing entire job functions. Not assisting. Replacing. The gap between the people who get this and everyone else isn’t a skills gap. It’s a courage gap. This edition is about which side you’re on.What You’ll Learn in This EditionThis edition confronts the uncomfortable reality that most AI investments are producing demos, not outcomes — and the structural reasons why.* 🎙 Why agents are automating your thinking, not just your tasks — and why that distinction matters more than any model release* ✍️ Copilot’s identity crisis is the most important product failure of 2026 so far* 👉 The single variable that predicts AI maturity 7x better than technology choices* 1️⃣ Why advertising AI use is now a financial liability for professional services firms* 2️⃣ The inference cost crisis that threatens every AI business model — including OpenAI’sEpisode 4: The Era of Agents — Your Cognition Is the Product NowWe mapped three years of AI evolution in this episode and landed somewhere uncomfortable. Era one gave us wrong answers. Era two gave us wrong context. Era three — agents — is giving us wrong actions. And the stakes compound with each era because AI is no longer just saying things. It’s doing things.Brittany brought the number that should haunt every product leader: only 6% of organizations have fully deployed any kind of agent. Copilot hit 30% weekly active usage after six months — meaning 70% of enterprise users basically stopped opening it. The tools are moving at an extraordinary pace. Almost nobody is keeping up.We profiled four startups winning the point-solution war that most people haven’t heard of. But the real conversation was about what happens when you hand your thinking to an agent. Not your typing. Not your scheduling. Your thinking — the research, the monitoring, the analysis, the synthesis. Something changes in you when you do that. And most people haven’t reckoned with what that means.“We’ve trained generations of people to think linearly. Step one, step two, step three, fill out this form, follow this process. Agents don’t work like that. Agents require you to think in terms of outcomes, connections, and context.” — ArpyListen now: Spotify | <a target="_blank" href="https://podcasts.apple.com/us/podcast/product-impact-p

Every influencer is drooling over Claude Code skills files. Every product team is chasing the next model release. But for two years, the data has been screaming the same thing: capability isn’t the bottleneck. Context is. This edition unpacks what that actually means — why structured business knowledge is the highest-leverage investment a product team can make, what the “context wars” look like from the inside, and why the teams winning aren’t the ones with the best models. They’re the ones whose AI actually understands their business.What You’ll Learn in This EditionThis edition confronts the structural reason most AI products fail — they’re missing the context that makes capability useful.* Why Juan Sequeda from ServiceNow says “hope is not a strategy” — and what to build instead of better prompts* The three-layer knowledge framework that gives AI a shared language across your entire organization* CNBC’s “silent failure at scale” investigation reveals why 91% of AI models degrade without anyone noticing* Microsoft just adopted ontology — the same concept Juan has championed for 20 years — as the foundation of its agentic AI architecture* Citadel Securities data shows software engineer job postings rising 11% YoY despite the displacement narrativeEpisode 3: Context Is the New Moat — Why Your AI Needs Business Knowledge, Not Better PromptsEvery influencer is drooling over skills files and prompt templates. Juan Sequeda, Principal Scientist at data.world (acquired by ServiceNow), has spent 20 years proving that none of it works without structured business knowledge underneath. In this episode, Juan breaks down the three-layer framework — business metadata, technical metadata, and the mapping layer that creates real semantics — and explains why the teams investing in ontology today will compound value across every AI use case they build next. His blunt assessment of skills files as a production strategy: “Hope is an interesting strategy. It’s not one that I add to my strategy.”“If you just edit in skills, I don’t think that’s gonna be the solution to your problem. You’ll have a great POC. It’ll work for the use cases you tested on. Are you willing to put your career on the line and put that in production?” — Juan SequedaListen on Spotify | Apple Podcasts | YouTubeContext isn’t a nice-to-have. It’s the architecture layer that determines whether your AI product delivers consistent, measurable value or drifts into silent failure. PH1 built this framework to illustrate what Juan Sequeda has been researching for two decades: intent, background,

AI products are shipping faster than ever. But shipping isn’t impact. The teams pulling ahead aren’t the ones with the best models — they’re the ones who can prove their product moves the business. This edition is about that gap. How to measure what matters, where the biggest barriers to impact are hiding, and what the latest research says about getting AI products to actually drive growth. Because the real competitive advantage isn’t AI. It’s knowing whether your AI is working.What You’ll Learn in This EditionThis edition cuts through the noise to focus on the measurement gap — the difference between shipping AI and proving AI drives growth.* The Power/Speed/Impact/Joy bullseye — a calibration framework for AI products that actually drive growth* A Nature paper reveals why removing friction from AI may be destroying the learning your team needs* John Maeda on why design teams are being hollowed out — and why PMs are next* Benedict Evans on why even OpenAI can’t solve product-market fit with capability alone* Research that should change how your team thinks about AI-assisted skill buildingThanks for reading Product Impact | AI Strategy, Value Creation, AI UX! This post is public so feel free to share it.Episode 1: Why Your AI Metrics Are Lying to You - Framework for improving AI product performanceYour AI product might be fast, capable, and technically impressive — and still not drive the growth your business needs. In this episode, Brittany Hobbs and I introduce the Power, Speed, Impact, and Joy bullseye — a calibration framework borrowed from F1 racing. The teams winning aren’t shipping more features. They’re measuring different things entirely. We break down a three-layer eval approach and why most completion metrics are hiding the signals that matter.“Success does not mean satisfaction. If someone stops engaging, does that mean they solved their problem — or that they were frustrated and left?” — Brittany HobbsListen on Spotify | Apple Podcasts | YouTubeYour Role Isn’t Shrinking. It’s Being Hollowed Out.John Maeda — Three major tech companies have restructured design teams into “prompt engineering pods.” Maeda’s #DesignInTech 2026 calls it what it is: the elimination of design judgment from the product process. “When you replace a designer with a prompt, you don’t lose the pixels. You lose the questions that should have been asked before anyone opened a tool.” Th

AI was supposed to help humans think better, decide better, and operate with more agency. Instead, many of us feel slower, less confident, and strangely replaceable.In this episode of Design of AI, we interviewed Ovetta Sampson about what quietly went wrong. Not in theory—in practice. We examine how frictionless tools displaced intention, how “freedom” became confused with unlimited capability, and how responsibility dissolved behind abstraction layers, vendors, and models no one fully controls.This is not an anti-AI conversation. It’s a reckoning with what happens when adoption outruns judgment.Ovetta Sampson is a tech industry leader who has spent more than a decade leading engineers, designers, and researchers across some of the most influential organizations in technology, including Google, Microsoft, IDEO, and Capital One. She has designed and delivered machine learning, artificial intelligence, and enterprise software systems across multiple industries, and in 2023 was named one of Business Insider’s Top 15 People in Enterprise Artificial Intelligence.Join her mailing list⁠ | Right AI | Free Mindful AI Playbook Why 2026 Will Force Teams to Rethink How Much AI They Actually NeedThe risks are no longer abstract. The tradeoffs are no longer subtle. Teams are already feeling the consequences: bloated tool stacks, degraded judgment, unclear accountability, and productivity that looks impressive but feels empty.The next advantage will not come from adding more AI. It will come from removing it deliberately.Organizations that adapt will narrow where AI is used—essential systems, bounded experiments, and clearly protected human decision points. The payoff won’t just be cost savings. It will be the return of clarity, ownership, and trust. This is going to manifest first with individuals and small startups who were early adopters of AI. My prediction is that this year they’ll start cutting the number of AI models they pay for because the era of experimentation is over and we’re now entering a period where deliberate choices will matter more than how fast the model is. Read the full article on LinkedIn. Do You Really Need Frontier Models for Your Product to Work?For most teams, the honest answe

In Episode 48 of the Design of AI podcast, we unpack why the most common AI promises are collapsing under real market pressure. AI was meant to unlock strategic work, expand opportunity, and elevate creativity. Instead, UX and design roles are disappearing, agencies are cutting creative staff while buying automation, and freelance work is being devalued as execution becomes cheap.This episode is not about panic. It is about reality. Value still exists, but it is concentrating among those who can integrate AI into real systems, navigate ambiguity, and own outcomes rather than outputs.🎧 Apple Podcasts🎧 SpotifyKey Insights About AI at WorkWhat the evidence shows once the optimism is removed.MIT Media Lab: ChatGPT Use Significantly Reduces Brain Activity (2025)Early AI use reduces attention, memory, and planning, weakening independent thinking when models lead the process.Wharton / Nature: ChatGPT Decreases Idea Diversity in Brainstorming (2025)AI-assisted brainstorming narrows idea diversity, producing faster output but more uniform thinking across teams.Science Advances / SSRN: The Effects of Generative AI on Creativity (2024)AI improves fluency and polish while consistently reducing originality and conceptual depth.arXiv: Human–AI Collaboration and Creativity: A Meta-Analysis (2025)Human-led AI collaboration improves quality slightly, but AI reduces diversity without strong framing and judgment.arXiv: Generative AI and Human Capital Inequality (2024)AI disproportionately benefits those with systems thinking and judgment, widening gaps between experts and generalists.Thanks for reading Design of AI: Strategies for Product Teams & Agencies! This post is public so feel free to share it.Realities of Being AI Early AdoptersThe Raised Floor Trap by Hang XuAI makes baseline output easy. What it doesn’t make easy is integration, orchestration, or delivery inside real teams. Most people reach adequacy. Very few compound value. We’re not able to generate the type of value we’re sold on.👉 Follow Hang Xu for insights about the realities an

Our latest guest is Maya Ackerman — AI‑creativity researcher, professor, and author of Creative Machines: AI, Art & Us (Wiley), as well as founder of WaveAI and LyricStudio (View recent colab with NVidia).Maya’s perspective is not just insightful — it’s a necessary reality check for anyone building AI today. She challenges the comforting narrative that AI is a neutral tool or a natural evolution of creativity. Instead, she exposes a truth many in tech avoid: AI is being deployed in ways that actively diminish human creativity, and businesses are incentivized to accelerate that trend.Her research shows how overly aligned, correctness-first models flatten imagination and suppress the divergent thinking that defines human originality. But she also shows what’s possible when AI is designed differently — improvisational systems that spark new directions, expand a creator’s mental palette, and reinforce human authorship rather than absorbing it.This episode matters because Maya names what the industry refuses to admit. The problem is not “AI getting too powerful,” it’s AI being used to replace instead of elevate. Businesses are applying it as a cost-cutting mechanism, not a creative amplifier. And unless product leaders intervene, the damage to creativity — and to the people who rely on it for their livelihoods — will become irreversible.Listen to the Episode on Spotify, Apple Podcasts, YoutubeWe’re engineering a global creative regression and pretending we aren’t.Generative AI could radically expand human imagination, but the systems we deploy today overwhelmingly suppress it. The literature is unequivocal:* AI boosts creative output only when tools are intentionally designed for exploration, not correctness.* When aligned toward predictability, AI drives conformity and sameness.* The rise of “AI slop” is not an insult — it’s the logical outcome of misaligned incentives.* New evidence shows that AI-assisted outputs become more similar as more people use the same tools, reducing collective creativity even when individual outputs look “better.”* Homogenization is measurable at scale: marketing, design, and written content generated with AI converge toward the same tone and syntax, lowering engagement and cultural diversity.* Repeated reliance on AI weakens human ori

Our latest episode features Jessica Randazza Pade, Head of Brand Activation & Commercialization at Neurable. Named to Campaign US’s 40 Over 40 and ELLE Magazine’s 40 Under 40, Jessica is an award-winning global digital marketer, business leader, and storyteller. She explains why AI is not a value proposition, how to turn vague use cases into measurable outcomes, and why making technology invisible is often the strongest competitive advantage.“If the user can’t articulate what’s different in their life because of your product, you’re selling a vitamin—not a painkiller.”Listen on Apple Podcasts | SpotifyShape Our 2026 ResearchWe’re mapping where teams are struggling with AI adoption and what tools, frameworks, and support they need in 2026. Your input directly shapes our annual research and the topics we cover.Take the survey → https://tally.so/r/Y5D2Q5AI has lowered the cost of prototyping but raised the bar for adoption. Most AI products fail because they launch demos instead of durable workflows, rely on large models where small ones would work better, ignore trust, or sell “time savings” instead of business outcomes. Organizations resist tools that feel risky, inaccurate, unproven, or misaligned with real workflows. Complicated architecture, poor UX, weak personalization, and unclear ROI all compound the problem. Here’s a sample of it:#3: Your product doesn’t actually learn. Fake personalization destroys trust.#4: One hallucination can end adoption permanently.#8: “Saving time” is not a business case—outcomes are.#11: Organizational silos suffocate AI products.#17: Without a workflow and measurable ROI, you don’t have a product.AI will not save your product. Only reliability, trust, workflow clarity, governance readiness, and measurable value delivery will.Read the full article → https://ph1.ca/blog/why-your-AI-product-will-failsThe Year of AI ValueThis video covers why 2026 marks a turning point where AI is judged not by novelty or intelligence but by measurable ROI, workflow impact, and operational reliability. It explains why businesses are shifting from “AI features” to fully redesigned AI-enabled systems.We are past the point of buying AI based on promisesAI buyers no longer invest because the tech is impressive. They invest when it:* delivers measurable ROI* reduces operational and compliance risk* inte

Our latest episode explores the moment AI stops being a tool and starts becoming an organizational model. Agentic systems are already redefining how work, design, and decision‑making happen, forcing leaders to abandon deterministic logic for probabilistic, adaptive systems.“Agentic systems force a mindshift—from scripts and taxonomies to semantics, intent, and action.”🎧 Listen on Spotify🍎 Listen on Apple PodcastsAnd if you want to go deeper, check out Kwame Nyanning’s book, Agentics: The Design of Agents and Their Impact on Innovation. It’s the definitive field guide to designing agentic systems that actually work.Most striking for me was when discussed that we need to move from pixel-perfect to outcome-obsessed. Designers and product teams have for so long been more obsessed on the delivery of the output and now is time to be most concerned on the impact on customers.The hard truth: Most organizations are trying to graft AI onto brittle systems built for predictability. Agentic design demands something deeper: ontological redesign, defining entities, relationships, and intents around customer outcomes, not internal structures. If you can’t model intent, you can’t build an agent.Key takeaway: Intent capture is the new UX. Products that succeed will anticipate user context, detect discontent, and adapt autonomously.Featured Articles: Where Reality Collides with AmbitionAI Has Flipped Software Development — Luke WroblewskiWroblewski lays out how AI has upended the software stack. Interfaces now generate code. Designers define the logic while engineers review and govern it. The result? Faster cycles but a dangerous illusion of progress. Design intuition becomes the new compiler, and prompt literacy replaces syntax. The real risk is velocity without comprehension; teams ship faster but learn slower.Takeaway: Speed isn’t the problem; blind acceleration is. Governance, evaluation, and feedback loops are now design disciplines.Agentic Workflows Explained — The Department of ProductThis piece exposes what it really takes to build functioning agents: memory, planning, orchestration

In our latest episode, Michelle Lee (IDEO Play Lab) makes the case that play unlocks the next billion-dollar AI market. She reminds us that kids don’t stop at answers—they ask what if and turn shoes into cars or planes. That divergent mindset is exactly what product teams have lost.“Play is one of the best ways to challenge the norms, to think wide, imagine new possibilities.”Michelle shares:* How IDEO discovered billion-dollar opportunities (like PillPack, later acquired by Amazon) by staying curious.* Why teams should sometimes use older, glitchier versions of AI tools, because the “mistakes” spark better ideas.* Why incrementalism burns teams out and how designing for attitudinal loyalty beats chasing short-term metrics.🎧 Listen here → Play unlocks the next billion-dollar AI marketUncomfortable Truth: Most “AI strategies” today are adult strategies — converging too quickly, chasing predictability, and mistaking incremental progress for innovation. That’s why the breakthroughs are happening elsewhere.Product Workshop: Find your Disruptive PathIf your roadmap looks like everyone else’s, you’re already behind. Our next AI Product Strategy Workshop (Oct 30) is built for teams who want to:* Go beyond features and efficiency to discover truly disruptive opportunities.* Use LLMs as intelligent sparring partners to pressure-test fragile ideas before they waste time and budget.Spots are limited → Register hereHard-Cutting Take: If your roadmap reads like your competitors’, it’s not strategy—it’s risk management dressed up as vision.Incrementalism is the Silent KillerWe’ve all felt it: the slow grind of incremental product decisions that look safe but quietly kill ambition. My new piece argues that incrementalism is the silent killer of AI products—a trap for teams rewarded for predictability instead of progress.Read it on LinkedIn → Incrementalism is the Silent Killer of AI ProductsUncomfortable Truth: Incrementalism feels safe because it rarely fails spectacularly. But it guarantees mediocrity—and in AI, mediocrity is indistinguishable from irrelevance.AI Launches to WatchA wave of new releases will reshape how we design and ship AI products:* OpenAI: Stripe/Shopify integrations + new pre-designed prompts for professionals.* Anthropic: Chrome plugin + Claude 4.5 Sonnet, a fas

Our latest episode features Nicholas Holland (SVP of Product internal fraud detection automation; AI for helpdesk triage.Takeaway: Pick the ugliest, least scalable problem your users hack around with spreadsheets. Then automate that.Q4: How do we handle data privacy and ethics when integrating AI features?Uncomfortable Truth: Most tools don’t offer true privacy—they use your data to train their models. That’

🎙️ Listen on Spotify | Apple Podcasts | YouTubeI recently spoke with Maor Shlomo, founder of Base44—the platform that lets anyone build apps, tools, and games just by describing them to an AI. In six months, he built Base44 solo and sold it to Wix for $80M. It’s the clearest signal yet: the rules of building have changed, and most teams aren’t ready.We dug into:* Why vibe coding crushes the myth that innovation requires big teams and big funding.* How cross-domain generalists will thrive while narrow specialists get sidelined.* Why software that doesn’t become agent-driven will be left for dead.* The ruthless advantage of starting over quickly when the build cost is near zero.Maor’s blunt take: “If one person can go this far alone, do we need whole teams to achieve the same things?”🎧 Full episode: Listen on SpotifyThanks for reading Design of AI: Strategies for Product Teams & Agencies! This post is public so feel free to share it.The uncomfortable truth: Interfaces are vanishingVibe coding strips away menus, clicks, and UIs. You speak, and the machine builds. The UX profession must decide—adapt to this new layer of interaction, or watch relevance slip away.* Speak ideas, skip interfaces.* Abstraction layers are collapsing.* Creation is now a conversation.🔗 Read the full post on LinkedIn📅 AI Product Strategy Workshop — Register hereThis isn’t a “future of work” talk. It’s a hands-on reality check.* Spot where AI will gut existing workflows—and where the real opportunities lie.* Pressure test your product strategy against the agent-driven future.* Learn how to pivot faster than incumbents weighed down by legacy.If you think you can wait this out, you’ll already be too late.There’s a 2-for-1 deal right now using this link.* SSRN study: AI is already displacing workers across industries.* Challenger, Gray & Christmas: 10,000+ AI-driven layoffs in the

🎙 Episode 40: Atlassian’s Secrets to Successful AgentsIn this episode, Jamil Valliani (VP & Head of Product AI at Atlassian) shares how they embed AI across Jira, Confluence, and Trello through intelligent agents that blend into workflows—far from mere “+AI” buttons. He emphasizes starting small with tangible prototypes to build momentum and leadership alignment, showing that AI gains stick when they're experienced, not explained.Highlights from the episode:* Hands-on AI adoption at Atlassian: transforming workflows, not just products* From friction to flow: how prototypes bridge skepticism and trust* AI as teammate, not feature—designing for collaboration, not automation* Adoption baked into experience—make AI habitual, not optional“The most successful teams will treat AI not as a button you press, but as a teammate you collaborate with.”Listen on Spotify | Listen on Apple | Watch on YouTube —and share one workflow where AI acting more like a teammate could unlock unexpected value.About the Guest:Jamil Valliani brings two decades of product leadership (including 15 years at Microsoft) to Atlassian, where he’s spearheading AI-powered design.* LinkedIn* Atlassian RovoUpcoming Workshop: AI Product StrategyProduct teams everywhere are facing the same challenge: leadership wants AI integration for competitive advantage, but without certainty about which AI products will actually be valuable to customers.When: Thursday, September 18, 2025 (online)What you’ll gain:* Diagnose the highest-leverage AI use cases* Prototype with precision—avoid costly detours* Craft a resilient strategy that scales beyond pilot phaseRegister on Eventbrite and get a 2 for 1 promo.Learn to Synthesize or ElseIn a world awash with data, the real advantage lies not in knowing more—but in drawing clarity from the noise. Product and design leaders must become the translators of complexity, turning abundant knowledge into purposeful, actionable insight.h/t Stuart Winter TearEmerging Shift: Role-Dissolving AI</

After a frustrating week of trying to wrangle AI outputs, we decided to explore the risks of overreliance on AI. It’s good for us to question our tools. It enhances our processes and challenges us to find the right tools.Listen on Spotify | Listen on Apple Podcasts | Watch on YouTubeIn this episode, we say the quiet parts out loud. Not only are LLMs often feeding us incorrect information, but over-trusting these systems poses a serious risk.We can look at this Rolling Stone article headline and immediately laugh it off. It is insane to believe this will happen to anyone we know. However, in Mark Zuckerberg’s vision of the AI future, your friends will be bots. The loneliness epidemic is real. One in three Americans feels lonely every week. Data from Harvard’s Making Caring Common Project supports that loneliness is tied to increasing feelings of anxiety, not part of this country, and being about more than social isolation. 65% of respondents blame “our society,” pointing to a lack of confidence in our way of life and institutions.So, it should be no surprise that Harvard Business Review found that the top three use cases of 2025 involved loneliness and navigating life's stresses.AI could quickly become the next addiction for a world desperate for solutions. The fact that there’s demand for robo-companionship shouldn’t be treated as validation for building more tools to disassociate them from life. Let’s go back to exploring this topic from the perspective of business users.Understanding GenAI’s Productivity GainsAs we barrel into the AI-powered era, we can take one of two perspectives:* GenAI products are the next evolution of SaaS: Precise tools for specific workflows* LLMs are the next evolution of social media, where instead of degrading our interpersonal relationships, AI will addict us to easy and of

When reports like Adecco’s Global Workforce of the Future survey find that the average saving for workers using AI is 1 hour a day, we should question this. * What did those workers do with their time savings? * Should that time savings benefit the employer or the employee?* Can we trust such a hard-to-measure stat?Our latest episode tackles this and other disruptions happening to the creative and production processes. Matthew Krissel is the Co-Founder of the Built Environment Futures Council and a Principal at Perkins&Will. For over two decades, he has led transformative architectural projects across North America and internationally. We discussed how AI is disrupting architecture and lessons for digital product teams. He really struck powerful points many times during our conversation about questioning the role of time and permanence in a world when we want more, faster.Other points covered in the conversation:* Commoditizing design makes production easier, enabling societies to tackle challenges like housing shortfalls* Commoditizing design devalues other vital processes, like community engagement, respectful place-making, and longevity of projects* Over-indexing AI’s potential as a workflow optimizer, while under-indexing the potential to reimagine how complex projects are planned and operationalizedListen on Spotify | Listen on Apple PodcastsIn this newsletter, I’d like to tackle the concept of time saving and what it means from the perspective of crafting an AI strategy. Here was the most important quote from the episode: So just because something took half the time it did before, what happened is we just did more. So we just filled the time. Is there something higher and better use? I suspect that somewhere along the line the designs got better. Also I suspect that somewhere along there was diminishing returns. We were just doing more because we could not that it was actually yielding anything better.  Are you gonna focus on fewer, but better increase your quality? Are you going to spend more time on business development or some entrepreneurial side hustle? Just go home early? What you decide to do as we start to gain productivity time is going to shape a lot of where this is a

As much as image generation is fun, the power of GenAI is prediction. The technology operates very similarly to people you might meet: * Some people have studied and are experts in a single topic for a decade. They’re experts in that topic and can easily infer, correct, and complete tasks. They’re unreliable for everything else.* Some people are generally knowledgeable and have a good understanding of many topics. They aren’t experts but can reliably assist you in many ways. But they’ll also be wrong sometimes.OpenAI, Anthropic, etc.— are highly knowledgeable in almost every topic. That’s the result of being trained on all accessible information online, data they’ve licensed, plus data they’ve allegedly stolen. AI products built on these frontier models are immediately powerful for completing any task. But if you build a point solution on proprietary data explicitly trained on a narrow topic, it can achieve an expert level. That was the focus of our conversation with Tyler Hochman, the Founder and CEO of FORE Enterprise. We discussed unlocking AI’s predictive power by focusing on expensive and repeating problems. How any business or founder can leverage and/or specialized data sets to train AI models to deliver powerful prediction capabilities.Listen on Spotify | Listen on Apple Podcasts | Watch on YouTubeHe’s built AI-powered software to predict when employees may leave their jobs, offer fashion advice, and help professional sports teams improve performance. This video explains how to train your model using Figma files.This conversation highlights how important your first party will become. This data includes more than just your customer data; it should include documenting workflows, quantifying initiatives, and developing a matrix of your offerings/capabilities. Anything repeatable must be quantified as a learning tool.Example of a data collection strategy for AI trainingWhen OpenAI launched a new image generation feature in ChatGPT, everyone jumped on it. AI-generated images infested our feeds in the Studio Ghibli style. These images sparked a lot of worthy <a target="_blank" href="https://www.techradar.com/computing/artificial-intelligence/is-chatgpts-studio-ghibli-craze-a-copyright-timebomb-heres-the-ver

“The future is already here, it's just not evenly distributed” Science fiction is inspiring, frightening, and often the best lens into the future. Many ideas about the future are b******t —just like this quote being misattributed to the ever-amazing William Gibson— but even the wildest idea shares truths worth discussing.This week’s newsletter is an exercise in imagining how AI will transform the way that we work. The future will impact us differently because some already live with a future-centred mindset, while others prefer to shift their thinking daily. One such future-centred thinker is John Whalen, the author of Design for How People Think and the Founder of Brilliant Experience. He shifted from being an AI skeptic to an advocate because he sees a tidal wave of change coming to how product teams operate.Listen on Spotify | Listen on Apple Podcasts | Watch on YouTubeIn the episode, we discuss how he’s implemented AI into his workflows and how he can now accomplish projects in one week that used to take seven weeks to complete. He makes a compelling case for why every team should use AI-moderation and synthetic users to enhance product outcomes. But most importantly, he’s become an AI advocate because, over his three-decade career, introducing new tools has always been met with doubts and resistance. Ultimately, businesses force the adoption of tools that deliver a clear ROI. There’s still much to debate about AI. Reports like this one from Microsoft continue to show that AI isn’t ready to replace humans at key tasks. Another 2024 study found that ChatGPT delivered inconsistent results on a key qualitative research task, compared to humans. The most important thing about this study wasn’t that humans outperformed LLMs; it was the significant performance improvement from GPT-3.5 to GPT-4.0. AI is getting much better at tasks that seemed unimaginable to automate. <

There are many reasons to debate the ethics and implications of AI. But while we do that, hundreds of the world’s biggest brands are rushing to implement the technology into creative and coding workflows. At a time when shareholders are being unforgiving and policy making is volatile, business leaders are looking to AI to gain any advantage possible.Jan Emmanuele is one of the experts that these Fortune 500 corporations rely on to identify and build GenAI creative workflow augmentations and automations. He works for Superside —whom you might remember from our episode with Philip Maggs (Listen here)— because they’re on the leading edge of creating an LLM that interprets your briefing process, design system, brand guidelines, marketing campaigns, and data to automate high-volume creative tasks. In this episode, we focus on how and where AI is applied within organizations and workflows. It details how organizations can prepare themselves for implementing AI and how to address the core barriers and risks of the technology.Listen on Spotify | Listen on Apple PodcastsWhat was most interesting about this conversation was his prediction that the adoption of AI will explode in enterprise orgs starting in 2026 and that it could continue into the 2030s. He believes that the value of AI in enterprise has already been proven and that more use cases exist than anyone can believe. That adoption thus far has only been limited because of legal and procurement policies.If this is true, organizations that aren’t already at least planning for this workflow-automated future will soon be at a huge competitive disadvantage. Finding 10x augmentations of creative output is routinely achieved, and more will be possible for organizations with highly-structured and easily-repeatable workflows. The gains will be largest in orgs that leverage the uniquely-LLM capability of contextualizing outputs based on data. Examples include localizing campaigns to micro-niche segments or regions of the world. Thanks for reading Design of AI: Strategies & insights for product teams! This post is public so feel free to share it.Headwinds will reduce the number of creatives earning a living wageAs we barrel towards the increasingly inevitable reliance on LLMs, it puts creatives in

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 ca

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

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. (<a target="_blank" href="https://www.linkedin.com/pulse/ai-agents-how-businesses-must-adapt-ri

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 a

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: [email protected] 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 directl

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💡 <a target="_blank" href="https://www.l

Speaking to Phillip Maggs on Design of AI had so many💡 moments:1. Want to use AI to get a career advantage? Consuming AI content isn't enough to get ahead, you need to experiment with the new material. Stretch what you believed was possible and you'll gain new capabilities.2. New careers and role are being defined right nowGenAI makes it possible for anyone to quickly learn about a topic or skill. You might think you're average but can quickly put together a unique skill profile that makes you a unicorn, especially if you're more committed to being curious about new technologies and how to leverage them.3. Much of design should be automatedWe forget that a lot of design tasks are literal assembly-line outputs: Banners, emails, ad variants. These rightfully should be automated because they exist in the world for such a short period. However, assets that represent your brand to millions or which will be in market for years must be hand-crafted.4. Design systems and brands are rulesThe more we codify what our products and brands should be, the more we unlock the augmenting powers of AI. Phillip imagines that a day will come when the LLMs about our brands will shine light on ideas we otherwise wouldn't have considered because of our own biases.5. A lot of AI design products are "party tricks"Sure a tool that can generate designs based on text prompts are cool but are they significantly saving time? Are they aware of what qualifies a good output for your brand? Do they understand how you communicate with customers? The outcome of these tools likely is not a significant ROI.Listen on Spotify | Listen on AppleAI tool of the week: Cove.aiCove.ai is like Miro meets Claude. You can prompt and build assets, just like in Claude. But what makes this tool fascinating is that you can save our work to a visual board and invite others to collaborate with you. The most surprising finding from using this platform is recognizing that in a typical project I’m outputting so many assets. The volume makes infinite scroll interfaces painful, and even makes Claude Project’s interface seem deficient. The visual board interface is much more functional since I can sort dozens of cards into a work surface that makes sense.Thanks for reading Design of AI: News & resources for product teams! This post is public so feel free to share it.Our First 20 Episodes: 20 Lessons for How to Advance Your Career in the Era o

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,

In this newsletter:* Podcast episode with Kristie J. Fisher, PhD, the Sr. Director of Global User Research, PlayStation Studios.* Guide to designing a GenAI product: From vision to content strategy* Poll for the AI communityThe biggest challenge facing AI products isn’t whether they would use your product, it’s whether you’re delivering reasons to convince them to switch from their existing solution. This is extra difficult when leveraging an emerging technology, like GenAI, because of key factors:* GenAI tools ask users to give up control and have faith that the system knows what’s right—the exact opposite of what we’ve been training users to expect from productivity tools* GenAI is still nascent and doesn’t always get it right, meaning that in some situations it will deliver an inferior output (and need to be re-prompted)* Users quickly run out of ideas about what to prompt because they don’t know what the tech is capable ofSo as much as product teams can focus on the incremental delivery of value to users, those efforts are likely to fail because we’re asking users to take a leap of faith. Something that users, especially B2B and enterprise, don’t want to do.Thanks for reading Design of AI: News & resources for product teams! Subscribe for free to receive new posts and support my work.That’s why this week’s episode with Kristie J. Fisher, PhD was so fascinating. Having worked on launching new products and features at XBox, Google, and Playstation, she has learned how to dive deeper into the psyche of users and gamers. In there is the secret to making a product enjoyable: defining metrics to ensure a user’s time is well spent.When building and researching we must be committed not only to delivering value, but ensuring that the experience is enjoyable and worth changing your workflows for. So when building your GenAI product, always create evaluative metrics for the level of impact. The higher you score, the more likely a switch. It also offers and opportunity to qualitatively investigate where and how the impact is happening so you mine valuable product ideas.💡 Have questions about your GenAI project, post them on the Design of AI LinkedIn page.💡 Or contact me via email to privately discuss your projectKristie J. Fisher, PhD, has spent the last 15 years conducting user experience research and building and leading research teams across a variety of product domains, primarily in gaming. She currently leads the global

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

Our guest is Yasemin Cenberoglu, who was the first designer to work on Microsoft’s Copilot, all in secret, before the world was exposed to ChatGPT for the first time.Yasemin is a Principal Design Manager at Microsoft, leading the Copilot product for Teams Meetings, Calling, and Devices. She’s the first designer to shape what Copilot is today. Previously, she served as the Director of Design at Digitalist. Yasemin is an advisory board member at IDEA School of Design at Capilano University. She studied in Germany and then at Cal State, in the Bay area.00:49 Yasmin's Background and Role 02:09 Design Differences: Europe vs North America 03:44 Service Design Methodologies 03:58 Co-Creating with OpenAI 04:38 Blueprints and Customer Journeys 05:27 Rapid Prototyping and Testing 06:20 Reconnecting with Yasmin 07:06 The Excitement of Innovation 10:04 Defining Value Drivers 11:50 Building High-Level Scenarios 12:49 Managing Feasibility and Vision 15:53 Lessons Learned from GenAI 21:05 Testing and User Feedback 22:51 Iterative Design and AI 31:52 Building Trust in AI 34:12 Service Design in AI 39:11 Deciding Between Co-Pilot, Agent, or Chatbot 43:41 Future of Assistive Software 47:27 Advice for Aspiring AI DesignersEpisode 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/Thank 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/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

How should product teams be leveraging GenAI? Product teams are struggling to find the use cases which deliver the most value to customers and where the technology can be effective. And teams that have built AI products are finding that there’s often a mismatch between what customers find valuable and what the technology can do. Our guest is Arpy Dragffy Guerrero, the founder of PH1 Research where he has consulted Spotify, Microsoft, Mozilla, National Football League, to research and strategize how to best leverage emerging technologies. He’s worked on products across AI, personalization, Web3, location-sensing, and more. His focus is creating product & testing strategies to quickly pinpoint where the best opportunities are for new products. Follow him on social:⁠https://www.linkedin.com/in/adragffy/⁠⁠https://twitter.com/arpyd⁠Arpy maps out Futures Design: How to build AI products that customers want. We discuss strategies for product teams:‣ Learning from failure & the struggles of early AI‣ The challenge of identifying the impactful use cases of AI‣ The importance of value drivers (& why they aren’t JTBD)‣ Applying systems thinking to AI products & strategies‣ People hate chatbots —agents will open new possibilities‣ Examples of how agents could transform use cases and rolesPlease subscribe to: Design of AI: The podcast for product teams, on Spotify, Apple podcasts, Youtube, substack. We interview leaders and practitioners at the forefront of AI to help product teams navigate where and how to leverage AI.Substack newsletter ⁠https://designofai.substack.com/⁠ Join the conversation on LinkedIn ⁠https://www.linkedin.com/company/103164463/⁠This Design of AI episode is brought to you by PH1: A research & strategy consultancy that helps clients build AI products that customers want https://ph1.ca This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

What is the path to building responsible AI products? We have a special guest: Jess Holbrook, the Head of UX Research for Microsoft AI.We discuss:‣ Responsible AI: What it is and how orgs need a clear vision for it‣ Data transparency: Ensuring you are communicating appropriately‣ Becoming one of Google’s first user researchers working on machine learning‣ Philosophical differences to user research at Google, Meta, and Amazon‣ Bridging academic research and the practical development of AI products‣ The paradigm shift that big tech is expecting AI to deliver‣ Why the last thing you should want is a user over-trusting your productAs one of the first user researchers working on AI products, Jess offers a deep and informed perspective on the challenges and opportunities of working with this new technology. He challenges organizations to build values into their products, unwaveringly and without vagueness. Jess Holbrook is the Head of UX Research for Microsoft AI. Prior to that he was Director of UX Research for Generative AI and Responsible AI at Meta. He got his start in human-AI research about 10 years ago at Google where he was a founder and lead of Google’s People + AI Research group (PAIR). Prior to joining Google, he was a UX Researcher at Amazon and Microsoft. He received his Ph.D in Psychology from the University of Oregon and a B.S. in Psychology from the University of WashingtonFollow Jess: https://linkedin.com/in/jessholbrook/ https://x.com/jesssconResources mentioned by Jess:https://pair.withgoogle.com/https://research.google/teams/responsible-ai/https://runwayml.com/Please subscribe to: Design of AI: The podcast for product teams, on Spotify, Apple podcasts, Youtube, substack. We interview leaders and practitioners at the forefront of AI to help product teams navigate where and how to leverage AI. Have questions? Join the conversation in our LinkedIn community: https://www.linkedin.com/company/designofai/ Hosted by: Brittany Hobbs https://www.linkedin.com/in/brittanyhobbs/ Arpy Dragffy Guerrero https://www.linkedin.com/in/adragffy/ This Design of AI episode is brought to you by PH1: A research & strategy consultancy that helps clients build AI products that customers want https://ph1.ca This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

AI is changing the role of the designer and shifting how product teams succeed. We have a special guest: Scott Jenson, formerly from Apple, Google, and Frog Design.We discuss:* Why designers feel like their entire job will go away* What advice he offers to the teams and individuals he coaches* How AI is over--hyped and where it will have impact* Lessons from working at the forefront of mobile technology* Why Google, Apple, Meta, Microsoft are all racing to get there first* Recommendations to build successful products todayThis conversation is more of a coaching session for the designers, researchers, and product teams trying to navigate this time of great change.We try and cut through the hype to distill out key lessons that will help you all in your careers.Scott Jenson has worked in user interface design and strategic planning for over 30 years. The first member of the System Software Human Interface group at Apple in the late 80s, working on System 7, the Apple Human Interface guidelines and the Newton digital assistant. After Apple, was a freelance design consultant, doing work for Netscape, Mayo Clinic, American Express, and several web startups. Then director of product design for Symbian, then managed Mobile UI design at Google for 6 years. Left to become creative director at frog design for 2 years but returned to Google to explore advanced UX concepts for IoT and Android at Google. 35+ patents.  https://www.linkedin.com/in/scottjenson/Please subscribe to: Design of AI: The podcast for product teams, on Spotify, Apple podcasts, Youtube, substack. We interview leaders and practitioners at the forefront of AI to help product teams navigate where and how to leverage AI.Have questions? Join the conversation in our LinkedIn community: https://www.linkedin.com/company/designofai/Hosted by:Brittany Hobbs https://www.linkedin.com/in/brittanyhobbs/Arpy Dragffy Guerrero https://www.linkedin.com/in/adragffy/This Design of AI episode is brought to you by PH1: A research & strategy consultancy that helps clients build AI products that customers wanthttps://ph1.ca This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

This conversation is a deep case study into what the capabilities of the technology are today and how product teams must leverage both creative experts and these emerging technologies, side-by-side. Our guest is Trisha Causley from Shopify.Topics we discuss:▪ Why Trisha went from an AI skeptic to a champion▪ What types of creative tasks GenAI is best at▪ Tactical lessons for leveraging GenAI across product experiences▪ Why prompt engineering must become part of your toolkit▪ Shopify’s plan to leverage GenAI to scale & personalize brand-building▪ Why GenAI enhances the role of creatives by expanding what you doTrisha Causley is a Senior Staff Content Designer at Shopify in Toronto, Canada, where she works on AI-powered product features. She previously worked with IBM and on the Watson team. https://www.linkedin.com/in/tcausley/The Design of AI podcast is available on Spotify, Apple Podcast, and Youtube.Have questions? Join the conversation in our LinkedIn community: https://www.linkedin.com/company/designofai/Subscribe to the Design of AI podcast for more in-depth resources for product teams.Hosted by:Brittany Hobbs https://www.linkedin.com/in/brittanyhobbs/Arpy Dragffy Guerrero https://www.linkedin.com/in/adragffy/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

Why are brands investing into AI? How can they succeed? What can we learn from how experts in the field of innovation lead transformation projects? Where will AI actually deliver impact in the near term? Joining us is Nick Sherrard, who is involved in these conversations across Fortune 500, government, and startups.He is a co-founder of Label Sessions, the global innovation expert network, and Label Ventures, the venture studio. He is also a board member at Substrakt, the digital agency, and Collective art gallery in Edinburgh. Nick is often said to be the only person to have run an innovation lab inside a bank, a government department, a big 4 consultancy and a circus. His approach to making change happen in organisations fuses his more classic brand and product development background, with the devising mindset of arts producer. Nick advises boards and entrepreneurs globally.In this episode we cover:* Top-down and bottom-up approaches to leading AI projects* History of art and innovation is the history of rejection* Leaders of AI projects often don’t anticipate what’s needed* The problem with design thinking when building AI products* How the creative & consulting worlds are enhanced by AI* Use cases where AI will have impactAlso find us Apple Podcast & SpotifyHave questions? Join the conversation with other product leaders on LinkedIn https://www.linkedin.com/company/designofai/Subscribe to the Design of AI podcast for more in-depth resources for product teams. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

Building products with GenAI brings powerful new capabilities but also a whole new set of uncertainties. Teams can't rely on best practices because the technology is changing so quickly and users are cautiously adopting change. Designing and shipping products can no longer be thought about as a linear process.Alexandra Holness, Senior Lead Product Designer at Klaviyo, joins to share lessons, cautions, and a path forward to help product teams build AI products that customers want. She sees that successful product teams will depend on designer, data scientists, engineers working more closely than ever because it is very hard to predict how customers will use models until you've shipped them.Topics discussed:* How she created her role leading AI design * Assumptions the team had about how to leverage AI * What works and doesn’t from a design perspective* AI models being so nascent that its hard to design a UX* Designers-data-engineers working together in new ways* Building AI products is very different than traditional * Building effective AI products requires culture change* Why you need to test out potential futuresHave questions? Join the conversation https://www.linkedin.com/company/designofai/Subscribe to the Design of AI podcast for more in-depth resources for product teams. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

Dr. Amy Bucher literally wrote the book on behavior change.She joined the podcast to discuss how hashtag#GenAI can transform what tech is possible of achieving on a human outcome level:- How AI can open entire new possibilities for behavioural change and lead to monumental outcomes- Opportunities and risks of leveraging AI personalization- Reinforcement learning and what it is- Objective-driven AI and how we should start focusing more on outcomes - Why wearables may open new possibilities- Considerations around proprietary vs. commercially-available AI- And - Why having a AI scientist will be critical for any team and that it may not be as hard to hire for as you think This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

How can AI make our workflows and products more effective? It’s a question every product team is asking itself as they decide to invest into developing or licensing products. Let’s learn from two building and leveraging AI today.Two case study presenters from the upcoming Rosenfeld Design with AI Conference (June 4 & 5) will be with us to detail out how they leveraged GenAI. Savannah Carlin, Staff Product Designer at Marqeta, will detail how to design conversational interactions with AI. Weidan Li, the Design Research Lead at SEEK.com, will outline AI’s performance in analyzing qualitative data. Design of AI, the podcast for product teams Hosted by Brittany Hobbs & Arpy Dragffy Guerrero. Find us on LinkedIn https://www.linkedin.com/company/designofai/Subscribe on Spotify, Apple, YouTube for weekly interviews with leaders at the forefront of AI.And join our substack newsletter to get resources, insights, and strategies for product teams This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

Building products using emerging technologies is more difficult. As we’re seeing with building AI products today, teams are often chasing which use case and customer profiles to focus on. It’s harder because the new technologies make us obsess over what’s possible rather than what people actually need. Dr. Llewyn Paine joins us to share lessons and strategies from her advising teams working on spatial computing, virtual reality, and robotics. Her expertise is helping teams make better product decisions through research. We’ll discuss how to identify your best potential customers and design higher-value products and services they’ll love to use. She is an innovation strategy consultant with nearly two decades of experience in emerging technologies, including mixed reality and AI at Microsoft, and experimental media for Disney. She has helped emerging technology teams launch flagship products and secure investments of over $300M. designofai.substack.com to get additional resources.Apple: Spotify: She’s speaking at the Designing with AI conference on June 4-5 where she’ll be diving into her most recent work: Protecting biometric data of research participants by leveraging AI This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

Building products using emerging technologies is more difficult. As we’re seeing with building AI products today, teams are often chasing which use case and customer profiles to focus on. It’s harder because the new technologies make us obsess over what’s possible rather than what people actually need. Dr. Llewyn Paine joins us to share lessons and strategies from her advising teams working on spatial computing, virtual reality, and robotics. Her expertise is helping teams make better product decisions through research. We’ll discuss how to identify your best potential customers and design higher-value products and services they’ll love to use. She is an innovation strategy consultant with nearly two decades of experience in emerging technologies, including mixed reality and AI at Microsoft, and experimental media for Disney. She has helped emerging technology teams launch flagship products and secure investments of over $300M. designofai.substack.com to get additional resources.

There are so many new GenAI products coming to market that it is hard to believe even a fraction of them will become sustainable businesses. Ben Yoskovitz, Founding Partner of Highline Beta and author of Lean Analytics, joins us to discuss how many of these startups will fail to find a product-market fit. By rushing to get to market they’re likely skipping key steps that would typically improve their likelihood of success. We discuss the process his venture studio uses and where he sees opportunities for AI products to deliver more value to consumers.Ben’s newsletter: https://www.focusedchaos.co/Design of AI, the podcast for product teamsHosted by Brittany Hobbs & Arpy Dragffy Subscribe to the podcasthttps://www.youtube.com/@DesignofAI This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

There are so many new GenAI products coming to market that it is hard to believe even a fraction of them will become sustainable businesses. Ben Yoskovitz, Founding Partner of Highline Beta and author of Lean Analytics, joins us to discuss how many of these startups will fail to find a product-market fit. By rushing to get to market they’re likely skipping key steps that would typically improve their likelihood of success. We discuss the process his venture studio uses and where he sees opportunities for AI products to deliver more value to consumers. Ben’s newsletter: https://www.focusedchaos.co/ Design of AI, the podcast for product teams Hosted by Brittany Hobbs & Arpy Dragffy Subscribe to the podcast https://open.spotify.com/show/3O11vQKPpKI5ZlJhdRGwnf https://podcasts.apple.com/us/podcast/design-of-ai-the-ai-podcast-for-product-teams/id1734499859 https://www.youtube.com/@DesignofAI And join our newsletter to get resources, plus key insights and strategies https://Designof.AI

AI has the potential to be a transformational technology. But how is it trained and how can you track authenticity? Virginie Berger, Chief Business Development and Rights Officer at Matchtune, joins us to discuss the developments with copyright issues related to creative fields in hopes of shedding light on what this means for other industries. A particular issue is what happens to business models when you can get replicas elsewhere and have no clarity on how they were derived?We explore how product teams can and should adapt. Important is protecting the rights of your users and leveraging LLMs that are ethically processing the data that you input into them.Episode of hosted by Brittany Hobbs & Arpy Dragffy Guerrero. Please subscribe to the Design of AI, the podcast for product teams who want to leverage AI to transform their industries.Visit https://designof.ai to get AI news & tools that matter to product teams. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

Emily Campbell joins us to discuss the future of UX. Her Shape of AI newsletter and community have become the go-to resource for AI product design patterns. She sees AI products getting to market with far less involvement from design than they should have. Design will undoubtedly experience shocks —with roles changing, and anti-patterns emerging— but also entirely new opportunities for design to shape adaptive experiences that offer users new capabilities to personally interact with products. We discuss what comes next after prompt-based, text interfaces. Episode of hosted by Brittany Hobbs & Arpy Dragffy Guerrero. Please subscribe to the Design of AI podcast. We speak to leaders at the forefront of AI to learn how great AI products are designed and how they’re transforming industriesTo contact us visit our website designof.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

Maarten Walraven-Freeling, co-editor of MUSIC x and the co-CEO of Symphony Media joins the Design of AI podcast to discuss how AI will impact the music industry. We look at how digital streaming platforms and algorithmic discovery have already led to monumental changes to the business and what to expect now that generative AI tools, like Suno, are making music creation easier and more accessible. It is clear that music is one of the first and most important battleground where we see the potential of AI as a creative tool but also where concerns are growing about GenAI platforms being trained on content without the permission of copyright holders. The show is hosted by Brittany Hobbs & Arpy Dragffy GuerreroSubscribe on Spotify, Youtube, or Apple to get our latest episodesWe speak to leaders at the forefront of AI to learn how great AI products are designed and how they’re transforming industriesTo contact us visit our website designof.ai This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

Peter Van Dijck, Founding Partner of AI agency Simply Put joins us to discuss how his team designs and builds AI products. Peter —formerly of Huge and Work & Co— share insights from how his background as an information architect and designer enable his team to see opportunities to discover and build the right product for orgs. We discuss the growing potential of LLMs to take on more use cases and the ways in which human-centred design inform decisions that need to be made. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com

Ad agencies have always had to be ahead of their curve. They need to predict what clients need tomorrow. But AI has the potential to change everything about their workflows, business models, and value. We speak with JP Holecka, CEO of POWERSHIFTER, to find out how agencies will need to adapt. He's spent the last year training agencies on GenAI capabilities, as well as pushing the limits of the tools in his own projects. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit productimpactpod.substack.com