Product Impact Podcast | Secrets to unlocking the value of AI

Epizodes

• 15. Playbook for Increasing AI Adoption & Value Creation 25.06.2026 26min

  Four data reports from 2026 tell a consistent story, and none of it matches the adoption narrative. Writer surveyed 2,400 global workers and C-suite leaders: 97% of executives deployed AI agents in the past twelve months, 29% reported significant ROI. Glean's Work AI Index found a name for what most knowledge workers are actually experiencing: botsitting — spending more time supervising and correcting AI than gaining anything back. Section's biannual proficiency survey: 67% of workers use AI weekly, 5.5% are proficient enough to generate consistent value, and 79% of managers haven't demonstrated their own AI use to their team in the past month. Token consumption per organization grew roughly 320 times in twelve months while the share of organizations reporting significant ROI stayed at 29%.Brittany Hobbs and Arpy Dragffy work through what's causing the gap, why the teams trying hardest to close it keep hitting structural walls, and what it takes to move from measuring adoption to generating defensible value — for individual contributors, for teams, and for the organizations responsible for this investment.What you'll learn:OpenAI, Writer, Glean, Section: four reports, one consistent signal — the adoption story hides the value failure.Glean 2026: botsitting is the dominant AI experience. More knowledge workers are losing time to AI than gaining it.67% of workers use AI weekly. Only 5.5% are proficient. The problem isn't more training days. It's the model of change.Four years of measuring seats over outcomes has left AI leaders unable to defend their budgets. The window is closing.Salesforce agreed to acquire Fin for $3.6B. What they built before that exit is the lesson most orgs are ignoring.Boris Cherny no longer prompts — he builds loops. What that means for every team not yet running autonomous evaluation.Articles referenced in this episode:97% of Executives Deployed AI Agents. Only 29% See ROI. — Brittany's breakdown of the Writer 2026 survey and the 68-point deployment-to-value gapThe 10% Problem: AI's Value Gap Is Wider Than Anyone Is Admitting — Why AI value is concentrating at the top and what it means for the rest of the organizationWTF is an AI-native org anyways? Let's compare Airbnb & Meta's opposing plans. — The competing models for AI-native organization designOpenAI & Anthropic are charging us way more than we need — Arpy on token economics, model selection, and the cost side of AI value creationWe built productimpactpod.com to be your AI product strategy and AI product news hub. Check it out.Thank you for listening to the Product Impact Podcast — if you have feedback, guest recommendations, or want to chat — contact us.Hosted by:Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.comThis episode was brought to you by: PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products. AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.
• 14: AI Adoption is the Problem Everyone is Desperate to Solve — Dr. Molly Sands, Atlassian 16.06.2026 31min

  Six months of research into the world's leading AI-powered organizations reveals a consistent split: a handful of people are seeing 10x or 20x gains, most are seeing some movement, and a significant portion of the workforce is drowning in forced change — trying to keep up with tools and mandates while watching colleagues get laid off. The organizations pulling ahead aren't pushing harder. They're leading by example, building cultures where struggling out loud is allowed, and being honest about where they actually are in the AI journey. The ones still stuck are running on fear-based incentives, measuring adoption instead of value, and missing the governance infrastructure — no Chief AI Officer, no clear policies, no connective tissue between independent AI experiments.Atlassian's 2026 State of Teams report puts numbers to the pattern. Twelve thousand knowledge workers, 170 Fortune 100 executives, and a headline finding: the Fortune 500 is losing $160 billion a year to what Atlassian calls the AI fragmentation tax — the cost of everyone moving fast in different directions. Dr. Molly Sands leads the Teamwork Lab at Atlassian, where behavioral scientists study how teams work and what separates high-performing ones from the rest. Her team found that organizations seeing real AI ROI moved to team-level AI thinking first — redesigning shared workflows instead of letting individuals invent their own, creating AI working agreements that give people clarity instead of anxiety, and breaking down knowledge silos rather than restructuring org charts. Information flow turned out to matter more than reporting structure.The episode also gets into what the research shows about junior employees (they're more comfortable than their managers), whether 2026 is actually the year of the agent (it isn't — not yet, not at scale), and what it's going to take to stay relevant once simply adopting AI stops being enough.Why AI adoption is still uneven — and what "drowning in forced change" actually looks like inside organizationsWhy the governance gap — no CAIO, no policies, no connective tissue — is the real reason AI experiments don't compoundWhy the Fortune 500 is losing $160 billion a year to coordination chaos, and why better tools won't close that gapWhy team-level AI thinking drives faster ROI than individual adoption programs or usage mandatesWhat AI working agreements are, what Atlassian's research found when teams used them, and how to run oneWhy most companies are nowhere near the orchestration level — and what the AI maturity curve actually looks like from the inside"Just saying 'go off and try it' can actually feel really hard. The more clarity around what you have access to and how you can use it — the better the teams tend to do." — Dr. Molly Sands, Atlassian----If you found this episode useful, please like, share, and send it to anyone on your team who'd find it helpful.We built https://productimpactpod.com to be your AI product strategy and AI product news hub. Check it out.Hosted by:Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/Featured guest:Dr. Molly Sands — https://www.linkedin.com/in/mollysandsAtlassian 2026 State of Teams Report — https://www.atlassian.com/blog/teamworkGo to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.comThis episode was brought to you by:PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products.AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.
• 13. Why Managing AI Agents Is More Like Supervising Labor Than Using a Tool [Jonathan Su, Procurify] 08.06.2026 30min

  Managing an AI agent isn't using a tool — it's supervising labor. Most companies skipped that step. In Procurify's recent survey of finance leaders, 35% said trust — not model capability — is the single biggest factor in whether their organization can actually deploy agents. The teams already shipping report 63% ROI from time savings and 60% from improved data accuracy, but only after they did the unglamorous work first: defined the operating model, baked in governance and audit trails, and consolidated their data into a single source of truth. Frontier models keep commoditizing generic intelligence. The value is moving up the stack — to the workflow, the context, and the data your company actually runs on.Procurement has sat in the middle of every enterprise's audit trail for decades — budgets, contracts, suppliers, approvals, compliance, payments. It's the use case AI vendors have been quietly building toward, because if you can make procurement feel less clunky, you've solved governance for the rest of the business. We sat down with Procurify's Chief Product & Technology Officer Jonathan Su to understand what an AI-native operating model actually looks like, why production-grade is now ten times harder than prototype, and what shifts when the bottleneck in your team moves from execution to judgment.In this episode:Why 35% of finance leaders say trust — not model capability — is the biggest factor in whether agents actually shipThe operating model most companies skip: governance, audit trail, single source of truth — before the agent touches workWhat AI ROI actually looks like — 63% time savings, 60% better data accuracy, plus the business KPIs that prove itWhy value is moving up the stack as frontier models commoditize generic intelligence — workflow, context, data, distributionHow procurement teams redesign workflows around agents instead of tacking AI on top of an already broken processThe hire that beats 20 years of experience: grit, taste, judgment, and the ability to learn in 4-month cycles"Managing an agent is more than just using a tool. It's sort of like supervising labor." — Jonathan, Procurify"The cost of producing something is dramatically lower, but the bottleneck shifts to judgment, craftsmanship, and taste. Just because you could do something doesn't mean you should." — Jonathan, ProcurifyWe built productimpactpod.com to be your AI product insights and strategic playbook hub. Check it out.Thank you for listening to the Product Impact Podcast — if you have feedback, guest recommendations, or want to chat — contact us.About Jonathan: Jonathan is Chief Product Officer at Procurify, where he leads product strategy and AI initiatives across the company's spend management platform. He has spent his career in payments, fintech, and enterprise software, and now leads Procurify's transition to an AI-native product organization. Procurify serves finance teams managing budgets, approvals, invoicing, and payments — the workflows where governance and AI agents have to coexist. Procurify: ⁠https://www.procurify.com⁠Jonathan on LinkedIn: ⁠https://www.linkedin.com/in/jonathanhaosu⁠Hosted by:Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.comThis episode was brought to you by: PH1 (https://ph1.ca) — a strategy & research consultancy specialized in delivering evidence about the highest value use cases and customer profiles. AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.
• 12. How Atlassian's Chief Design Officer Builds for Agents 28.05.2026 32min

  Every 1% increase in the context your agents receive produces a 0.38% improvement in output quality. LangChain's State of AI Agents 2026 report makes that measurable — and it makes interface design the highest-leverage investment most product teams aren't treating it as. At Atlassian Team '26 in Anaheim last week, Chief Design Officer Charlie made the case: the interface is what determines how context gets captured, which means every design decision your team makes is now directly setting a ceiling on how well your agents perform. Eighty-eight percent of enterprise agent pilots fail to reach production, with context fragmentation as the top blocker. That is a design problem.For 25 years, adaptive interfaces were the holy grail — software that reads who you are and adjusts to how you work. Charlie's announcement at Team '26: the technology limitation is gone. What remains is a design question about where to set the balance point between a system that adapts and a system a team can actually share. And at the same time, designing for agents and designing for humans has converged into nearly the same problem — Atlassian's design system is consumed by agents and human users from the same object, with 10% variation. Every shortcut taken on design quality now shows up twice.Charlie Sutton is Chief Design Officer at Atlassian, where he leads design across Jira, Confluence, Rovo, and the newly announced Dia browser. He sat down with us at Team '26 in Anaheim. In this episode:Why 783 tab interactions a day means even tiny friction changes produce outsized aggregate gains — and where to look firstThe 25-year holy grail of adaptive interfaces is technically solved — what remains is the design question of how much is right for teamsWhy structured objects (goals, strategy, people) beat expensive inference — and why most vendors are paying more for worse resultsHow Atlassian's design system serves agents and humans from the same object with 10% variation — and what the 10% tells youWhy vibe coding raised the floor so everyone can build, which is exactly why the ceiling on what design must deliver also roseWhy video captures intent that text never can — and how Atlassian is encoding it into the Teamwork Graph"The floor goes up — everyone can make things awesome. But the ceiling has also gone up. Expectations increase, what is possible has increased. Design is still focusing on that ceiling." Charlie Sutton is Chief Design Officer at Atlassian, where he leads design philosophy and execution across the company's full product suite — including Jira, Confluence, Rovo, and the newly announced Dia browser. He was involved in building the demos showcased at Atlassian Team '26 and works at the intersection of enterprise product design and AI-native interface development. (Verify Charlie's full name before publishing.)Guest resources:Atlassian: https://www.atlassian.comDia browser: https://www.atlassian.com/software/diaCharlie on LinkedIn: https://au.linkedin.com/in/charliesutton We built productimpactpod.com to be your AI product insights and strategic playbook hub. Check it out.Thank you for listening to the Product Impact Podcast — if you have feedback, guest recommendations, or want to chat — contact us.Hosted by:Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.comThis episode was brought to you by: PH1 (https://ph1.ca) — a strategy & research consultancy specialized in delivering evidence about the highest value use cases and customer profiles. AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.
• 11. Context Graphs Will Reshape How We Work [Jamil Valliani - VP AI, Atlassian] 20.05.2026 29min

  The fastest teams didn't switch to a better AI model. They gave their AI memory. At Atlassian Team 2026 they showed us the next evolution of AI capabilities: 150 billion connected objects across an organization, an agent reviewing 2 billion lines of code in 2 minutes, and 44% better answers using half the tokens. Inside teams, the change is concrete: a junior analyst gets years of knowledge instantly, and a product leader can oversee an entire enterprise's deployment. Our guest, Jamil Valliani leads AI product at Atlassian, where he has spent three years building the context layer that will help 300,000 companies. They also shocked everyone by announcing that the Teamwork Graph — is open to be connected to your work in Microsoft, Adobe, and Google. In this episode you'll learn:Why Atlassian made their context graph openEvidence that context improves token usageWhat the future of work will look likeThe key to delivering value at scaleWe built https://productimpactpod.comproductimpactpod.com to be your AI product insights and strategic playbook hub. Check it out.Thank you for listening to the Product Impact Podcast — if you have feedback, guest recommendations, or want to chat — contact us.About Jamil Valliani: Jamil Valliani is VP / Head of Product, AI at Atlassian, where he leads Rovo and the Teamwork Graph across the company's full product suite. He has been building AI product strategy at Atlassian since before the Rovo launch and works across the enterprise customer base to understand where AI adoption is actually working and where it stalls. Atlassian's tools — Jira, Confluence, Bitbucket, and connected third-party systems — are used by over 300,000 companies worldwide.Atlassian: https://www.atlassian.comRovo: https://www.atlassian.com/software/rovoJamil Valliani on LinkedIn: https://www.linkedin.com/in/jamil-valliani-b131881/Hosted by:Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.comThis episode was brought to you by: PH1 (https://ph1.ca) — an strategy & research consultancy specialized in delivering evidence about the highest value use cases and customers profiles. AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.
• 10. Why Most AI Customer Experiences Fall Flat [Rikki Singh, Twilio] 11.05.2026 45min

  Most enterprise AI investments in customer experience are stuck somewhere between a demo and a disappointment. The Qualtrics 2026 Customer Experience Trends Report found that nearly one in five consumers who used AI customer service saw zero benefit from the interaction. The bar for what enterprises are calling AI innovation is shockingly low, and customers feel it every time they're routed to a bot that reads from an FAQ.Rikki Singh leads product innovation at Twilio. Before Twilio she was at McKinsey, where she co-authored the definitive research on what makes a great PM. Before that she was a PM at Microsoft. She's now running the team behind what Twilio is calling its biggest launch in 17 years — an agent-native channel with conversation memory across voice, text, and email.In this episode we cover:➜ Why most AI customer experiences are still just RPA with better packaging — and the right metric to anchor on instead➜ Why token consumption made AI spend as unpredictable as AI ROI, leaving enterprise decisions with uncertainty on both sides➜ Why the LLM wrapper creates false confidence — the model is not thinking, it's generating strings non-deterministically➜ Vitamins vs painkillers: how to parse the signals customers don't say out loud from the ones that don't actually matter➜ How to protect long-horizon bets inside a public company: separate PMs by horizon and celebrate what you disprove➜ Why the brand owns the accountability when AI gets a high-stakes interaction wrong, regardless of which vendor caused it..................If you found this episode useful, please like, share, and send it to anyone on your team who'd find it helpful.We built ⁠https://productimpactpod.com⁠ to be your AI product strategy and AI product news hub. Check it out.Thank you for listening to the Product Impact Podcast — if you have feedback, guest recommendations, or want to chat — contact us.Hosted by:➜ Arpy Dragffy Guerrero — ⁠https://www.linkedin.com/in/adragffy/⁠➜ Brittany Hobbs — ⁠https://www.linkedin.com/in/brittanyhobbs/⁠Go to Substack to get AI strategy frameworks, news, and jobs: ⁠https://productimpactpod.substack.com⁠This episode was brought to you by:➜ PH1 (⁠https://ph1.ca⁠) — an AI strategy consultancy specialized in improving the measurable success of AI products.➜ AI Value Acceleration (⁠https://aivalueacceleration.com⁠) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.
• 9. Shipping AI Fast Without Breaking Everything [John Willis, 6x author] 30.04.2026 48min

  Most companies are running AI in production right now without any plan to govern and secure their businesses. This week Claude Code wiped out a business' entire database in 9 seconds. Anything is possible when an agent is given access to everything without governance. John Willis co-wrote The DevOps Handbook a decade ago because software teams were shipping code the same way — fast, manual, no visibility. He sees the same pattern repeating with AI, and he has spent five decades watching what happens when the gap between vendor promises and operational reality gets this wide. He's written 6 books and also happens to be a historian about AI.In this episode we cover:Why shadow AI — no ban, no guidance, company data on personal phones — is the most dangerous place to beWhy higher throughput and higher instability at the same time is the predictable outcome of speed without feedback loopsWhy governance creates flow instead of stopping it — and how that lesson from DevOps applies directly to AI nowWhy most teams think they have AI observability when they actually have ML evaluation tools solving a different problemWhy every team — even a five-person startup with no CTO — needs digitally signed audit trails for agent decisionsWhat the history of AI winters and springs tells us about where we actually are in the current cycleIf you found this episode useful, please like, share, and send it to anyone on your team who'd find it helpful.We built https://productimpactpod.com to be your AI product strategy and AI product news hub. Check it out.Thank you for listening to the Product Impact Podcast — if you have feedback, guest recommendations, or want to chat — contact us.Hosted by:Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/Featured guest:John is an accomplished author and innovative entrepreneur with over 35 years of experience in enterprise IT and research, driven by a deep passion for exploring the intersection of Generative AI and the transformative principles of Dr. W. Edwards Deming. He is the author of Rebels of Reason, a book that traces the history of artificial intelligence while uncovering the human stories behind its rise, connecting today’s AI landscape to the ideas and people that shaped the field and offering a unique perspective on its future in business. As a co-author of foundational DevOps works, John brings a rare blend of technical expertise and insight into the human dynamics of innovation, helping leaders cut through hype to focus on creating real customer value through a deeper understanding of AI’s context and systems.John’s LinkedIn: https://www.linkedin.com/in/johnwillisatlanta/Link to John’s Book Rebels of Reason: https://www.amazon.com/Rebels-Reason-Aristotle-ChatGPT-Heroes-ebook/dp/B0FCD8TW8RGo to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.comThis episode was brought to you by:PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products.AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.
• 8. The Most Important Data Points in AI Right Now 24.04.2026 18min

  Stanford's 2026 AI Index just dropped. China closed a thirty-point AI performance gap to under three percent — on twenty-three times less investment. Apple picked their head of hardware as the next CEO. Anthropic's Mythos model found 271 zero-day vulnerabilities in Firefox. And Vercel and Lovable both got breached this month.We break down the numbers that should be on every product leader, designer, and founder's desk this week — what they mean, and exactly what to do about each one.In this episode we cover:➜ Stanford AI Index 2026: 88% organizational adoption, $581 billion in investment, and why China closing the gap on a fraction of the budget is the most important data point in the report➜ Token economics explained — what tokens are, what they cost, and why the shift from flat-rate licensing to usage-based pricing changes your AI budget math overnight➜ Why replacing Figma with Claude Design costs $0.22 for a first draft and $2,600 at refinement scale — and what that reveals about real-world AI costs➜ Why Apple chose John Ternus as CEO and elevated Johny Srouji to Chief Hardware Officer — and what that says about where AI value will actually live➜ Mythos, Vercel, Lovable: why vibe coding has never been easier and information security has never been more important..................If you found this episode useful, please like, share, and send it to anyone on your team who'd find it helpful. ⁠https://productimpactpod.com⁠ — Our news platform just launched. It is the best place to get the AI product news that matters.Hosted by: ➜ Arpy Dragffy Guerrero — ⁠https://www.linkedin.com/in/adragffy/⁠ ➜ Brittany Hobbs — ⁠https://www.linkedin.com/in/brittanyhobbs/⁠Go to Substack to get AI strategy frameworks, news, and jobs: ⁠https://productimpactpod.substack.com⁠This episode was brought to you by:➜ PH1 (⁠https://ph1.ca⁠) — an AI strategy consultancy specialized in improving the measurable success of AI products. ➜ AI Value Acceleration (⁠https://aivalueacceleration.com⁠) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls............Sources referenced in this episode:Stanford AI Index 2026 — https://productimpactpod.com/news/stanford-ai-index-2026-product-team-takeaways Stanford: US can't buy an AI lead — https://productimpactpod.com/news/stanford-ai-index-proves-us-cant-buy-ai-lead Claude Design vs Figma — https://productimpactpod.com/news/figma-claude-design-source-of-truth-for-design Apple CEO transition — https://productimpactpod.com/news/how-tim-cook-leaves-apple-future-of-ai Anthropic Mythos Preview — https://techcrunch.com/2026/04/07/anthropic-mythos-ai-model-preview-security Vercel breach — https://techcrunch.com/2026/04/20/app-host-vercel-confirms-security-incident Lovable vulnerability — https://thenextweb.com/news/lovable-vibe-coding-security-crisis-exposed AI token pricing — https://www.cnbc.com/2026/04/17/ai-tokens-anthropic-openai-nvidia
• 7: $490 Billion in AI Spend Is Delivering Nothing — Orchestration Is the Fix 17.04.2026 29min

  A small cohort of engineers — Andrej Karpathy, Mitchell Hashimoto, Simon Willison — are producing in a week what used to take a month. Meanwhile, seventy-eight percent of enterprise AI deployments show no bottom-line impact. Ninety-five percent of pilots fail within six months. The gap between the people getting extraordinary results and the organizations getting nothing is not talent. It's architecture. And it has a name. In this episode of the Product Impact Podcast, Arpy and Brittany break down why enterprise AI is failing at scale, what the engineers who are eighteen months ahead have figured out, and the two radically different futures that orchestration makes possible. In this episode we cover:The $490 billion AI value crisis — why adoption is surging and returns are near zero, and what Forrester, McKinsey, PwC, and Gartner are documentingFive failure patterns hiding inside every enterprise deployment — and why more training, more change management, and more executive support won't fix any of themThe pioneers building the future of work in public — Karpathy's vibe coding, Hashimoto's production-code throughput, Willison's hundreds of public experiments — and what they've proven about orchestration as engineering disciplineTwo outcomes of orchestration that most organizations aren't ready for: building bespoke deterministic software at a scale that was never economic before, and building an operating system where agents work autonomously on your behalfWhy markdown — not PDFs, not databases, not dashboards — is emerging as the knowledge substrate for the agent era, and why Karpathy himself is now calling for AI to organize wikis rather than chat"These are not technology failures. They are failures of imagination about what work actually is and how AI fits into the way we work." — Arpy Dragffy"The primary failure mode in AI adoption is not capability. It is transferability." — Brittany Hobbs (citing Harvard Business Review)https://productimpactpod.comThank you for listening to the Product Impact Podcast (formerly Design of AI) — Prove impact. Improve impact. Scale impact.Hosted by:Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.comThis episode was brought to you by:PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products.AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.
• 6. Robert Brunner Was the Secret to Beats' & Apple's Success — Now He's Redefining AI for the Physical World 09.04.2026 44min

  The Apple Industrial Design Group. The original PowerBook. Beats by Dre. The June Oven. The Polaroid Cube. Square Stand. Lyft Amp. One designer is behind all of them. Now Robert Brunner is turning his attention to something the entire AI industry is getting wrong: how intelligence should actually feel in the physical world.In this episode of the Product Impact Podcast, Robert Brunner — founder of Apple's Industrial Design Group, the man who hired Jony Ive, design partner on Beats by Dre, and founder of Ammunition — makes the case that the next generation of AI products needs less technology and a lot more taste.In this episode we cover:➔ Why the best AI feature is the one you never notice — and why engagement-driven AI is already eroding the trust every product is built on➔ The Apple and Beats lesson every AI founder should steal: technology enables, but design establishes➔ Why "AI for everyone" is the trap that guarantees mediocrity — and how to pick the right audience without shrinking the market➔ The cognitive asset AI will never have: taste, insight, and judgment shaped by a life actually lived➔ What Brunner's new venture Object is building: calmer, distributed consumer AI that respects attention instead of competing for it"The companies that build things that matter always have a clear point of view about people." — Robert Brunner"The next great technology companies will be the ones people trust with their lives, not just their data." — Robert BrunnerRobert Brunner founded Apple's Industrial Design Group (Apple IDg), hired Jony Ive, and led the design of the original Macintosh PowerBook and Newton. After a partnership at Pentagram, he founded Ammunition in 2007, where he co-created Beats by Dre with Jimmy Iovine and Dr. Dre and designed the June Intelligent Oven, Polaroid Cube, Square Stand, Lyft Amp, and the Limitless Pin. He is co-author of Do You Matter? How Great Design Will Make People Love Your Company and is currently building Object, a new venture developing AI-powered consumer electronics designed to improve digital wellbeing.Ammunition Group — https://ammunitiongroup.comRobert Brunner on LinkedIn — https://www.linkedin.com/in/robertbrunner/Do You Matter? How Great Design Will Make People Love Your Company — https://www.amazon.com/Matter-Great-Design-People-Company/dp/0137142447Robert Brunner on Prototyping Your Life, Leaving Apple, and Forging Beats by Dre (Yanko Design) — https://www.yankodesign.com/2025/09/28/robert-brunner-on-prototyping-your-life-leaving-apple-and-forging-beats-by-dre/https://productimpactpod.comThank you for listening to the Product Impact Podcast (formerly Design of AI) — Prove impact. Improve impact. Scale impact.Hosted by:Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.comThis episode was brought to you by:PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products.AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.
• 5. The Human Impact of AI We Need to Measure [Helen & Dave Edwards] 30.03.2026 57min

  We have benchmarks for model performance, metrics for productivity, and KPIs for everything the economy can quantify. But the impact of AI on how we think, who we become, and what we lose in the process? Nobody's measuring that — because nobody knows how.Helen and Dave Edwards have spent a decade studying what AI does TO humans, not just what it can do for us. In this episode, they challenge the binary of AI hype vs. AI fear and lay out a framework for something far more important: cognitive sovereignty — our ability to remain the authors of our own thinking in an era of automated cognition.In this episode of the Product Impact Podcast:Why the AI industry's business model is capital replacing labor — and why that's a path to economic collapse, not growthThe concept of cognitive sovereignty and why preserving your ability to think independently is the real competitive advantageResearch showing AI is increasing scientific citations but decreasing exploration — pulling everyone toward the medianWhy the one-person billion-dollar company is a fantasy that breaks down the moment you do the mathThe products getting it right: Bass (trust-first healthcare AI), Latimer (data sources that don't exist anywhere else), and creative tools treating AI as collaborator, not replacement"If AI can replace the humans in your business, does your business have any value at all?" — Dave Edwards"There is no point having this technology if it makes us dumber, if it makes us less kind, if it makes us more lonely, if it makes us less able to show up for others." — Helen EdwardsHelen and Dave Edwards are researchers and founders of the Artificiality Institute, where they lead a transdisciplinary community of scientists, designers, philosophers, and artists exploring what it means to be human in the age of AI. They are currently publishing Stay Human — a chapter-by-chapter book on how AI changes our thinking, identity, and relationships.Guest resources:Artificiality Institute — https://artificiality-institute.orgStay Human (free, published weekly) — https://journal.artificiality-institute.orgLinkedIn: Helen Edwards (https://www.linkedin.com/in/helenedwards/) | Dave Edwards (https://www.linkedin.com/in/daveedwards/)Artificiality Summit 2026 — Oct 22-24, Bend, Oregon. A human gathering to figure out what it means to be human. Learn more at artificiality-institute.orgproductimpactpod.comThank you for listening to the Product Impact Podcast (formerly Design of AI) — Prove impact. Improve impact. Scale impact.Hosted by:Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.comThis episode was brought to you by: PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products. AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stall
• 4. The AI Agent Era Will Change How We Work 19.03.2026 46min

  AI went from chatbots to assistants to agents in three years — and each era moved the failure one layer deeper. First we got wrong answers, then wrong context, now wrong actions. The tools are moving at an extraordinary pace, and almost nobody is keeping up.In this episode of the Product Impact Podcast we tackle The Agents Era Will Change How We Work:* Why vibe coding was the proof of concept for the entire agent era* Agents aren't automating tasks — they're automating your thinking* Why you'll be using a dozen agents within a year, not because you chose to, but because the work will demand it* The better you understand how you think, the more you'll succeed with agents* How we need to retrain ourselves — because decades of linear, process-driven work haven't prepared us for this* The startups most people haven't heard of that are already replacing how entire functions operatehttps://productimpactpod.comThank you for listening to the Product Impact Podcast (formerly Design of AI) — Prove impact. Improve impact. Scale impact.Hosted by:* Arpy Dragffy Guerrero —https://www.linkedin.com/in/adragffy/* Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/Go to Substack to get AI strategy frameworks, news, and jobs:https://productimpactpod.substack.comThis episode was brought to you by:PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products.AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.
• 3. Win The AI Context Wars — Unlock The Value of Data [Juan Sequeda ] 12.03.2026 52min

  Benchmark wars are over. Claude Code just proved it — the AI products winning right now aren't the ones with the best models, they're the ones that know their customers best. Context is the new moat.Juan Sequeda has spent 20 years solving the problem most product teams don't even know they have: your AI is only as powerful as your business's ability to predict what a customer wants to do and why. That intelligence isn't in the model — it's buried in your data. And the secret to unlocking it isn't writing better skills files or crafting smarter prompts. It's re-architecting how your business knowledge is structured, connected, and made available to AI. Juan shows you exactly how.Product teams who've made this move are seeing accuracy improvements of over 50%, and every new use case they ship compounds on the last. In this episode of the Product Impact Podcast, Juan introduces his three-layer knowledge framework — business metadata, technical metadata, and the mapping layer that connects them — and shows how this foundation transforms what your AI can deliver. You'll leave with a clear starting point, a way to tie your AI investment directly to business outcomes, and a mental model for how the best product teams are pulling ahead. This will only grow as we depend on agents and governance becomes more critical.In this episode you'll learn:➡️ Why context — not model quality — is now the primary driver of AI product performance➡️ The three-layer knowledge framework that gives AI a shared language across your entire organization➡️ Three concrete first steps to build your context foundation starting tomorrow➡️ How to tie every AI initiative directly to your company's top OKRs and earn lasting executive buy-in➡️ Why knowledge-first teams compound their advantage — each new use case gets faster and more powerfulThank you for listening to the Product Impact Podcast (formerly Design of AI) — Prove impact. Improve impact. Scale impact.Go to productimpactpod.com to rate the impact of AI products you use at work.Hosted by:Arpy Dragffy Guerrero — ⁠https://www.linkedin.com/in/adragffy/⁠Brittany Hobbs — ⁠https://www.linkedin.com/in/brittanyhobbs/⁠Support the show: subscribe, share this episode with a product leader, and leave a rating/review—it’s how this podcast reaches the teams building what comes next.Subscribe for frameworks + AI strategy resources: ⁠https://productimpactpod.substack.com⁠Brought to you by PH1 (⁠https://ph1.ca⁠) — an AI strategy consultancy specialized in improving the measurable success of AI products.About our guestJuan Sequeda is Principal Scientist and Head of the AI Lab at data.world, now part of ServiceNow. He has spent 20 years at the frontier of knowledge graphs, ontologies, and semantic architecture — focused on one question: how do you give AI a genuine understanding of your business so it can deliver answers you can actually trust?His lab's research proved that pairing knowledge graphs with LLMs improves enterprise question-answering accuracy by over 50% — findings that helped define the industry's thinking on context and AI reliability. He co-founded Capsenta (acquired by data.world), coined the concept of "context wars," and recently published his landmark LinkedIn series: "20 Lessons from 20 Years of Building Ontologies and Knowledge Graphs."He also co-hosts Catalog & Cocktails, one of the most respected podcasts in the data community, and publishes regularly on LinkedIn and Substack.Resources➡️ Juan Sequeda on LinkedIn: https://www.linkedin.com/in/juansequeda➡️ Catalog & Cocktails Podcast: https://data.world/podcasts/catalog-and-cocktails➡️ Juan's Substack: https://juansequeda.substack.com➡️ "20 Lessons from 20 Years of Building Ontologies and Knowledge Graphs" — https://www.linkedin.com/posts/juansequeda_i-finished-posting-my-20-lessons-from-20-activity-7429147437681864704-C7ki/ ➡️ Software Wasteland — Dave McComb ➡️ The Data-Centric Revolution — Dave McComb
• 2. Five steps to defend your AI product value 03.03.2026 34min

  AI is entering an abundance era: models get smarter, faster, and cheaper—so capability alone is no longer defensible. Feature cloning accelerates, pricing compresses, and many application-layer products get sampled and abandoned unless they prove measurable outcomes and earn long-term commitment. In this episode of the Product Impact Podcast, we break down why defensibility now matters more than capability—and what to do about it. You’ll leave with five actions to take this quarter: run a silent failure audit, map peak cost exposure, stress-test defensibility, fix the missing middle in pricing for power users, and build outcome visibility directly into the product.➡️ Dangerous economics of a capital-rich and value-poor market➡️ Master the unit economics of power users➡️ Proof that capability is no longer defensible➡️ 5 steps to defend your product valueYou’ll leave with five concrete actions to take this quarter because in a market where everyone has access to the same models, your moat is not capability. It’s customer success, trust, and measurable impact.Links & resourcesRead the strategy we reference: https://ph1.ca/blog/strategy-for-measuring-improving-ai-productsTake the AI Benchmarking Survey (measure your product’s impact): https://bullseyebenchmark.fillout.com/aiproductsThank you for listening to the Product Impact Podcast (formerly Design of AI) — Prove impact. Improve impact. Scale impact.Hosted by:Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/Support the show: subscribe, share this episode with a product leader, and leave a rating/review—it’s how this podcast reaches the teams building what comes next.Subscribe for frameworks + AI strategy resources: https://productimpactpod.substack.comBrought to you by PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products.
• 1. Why Your AI Metrics Are Lying to You - Framework for improving AI product performance 24.02.2026 35min

  How is it that Microsoft and OpenAI’s CEOs are telling us to panic because white collar jobs are going to be replaced by AI,Then there’s endless evidence of the opposite: Most companies that implement AI see little gains, with execs from over 80% of companies reporting no productivity gains at all.In this episode of the Product Impact Podcast we tackle Why Your AI Metrics Are Lying to You. We’ll provide you with a framework for improving AI product performance. We discuss how Evals can't answer the most important questions you have about your product's impact and the importance of calibrating your products for success by balancing key pillars.In this episode you’ll learn:- Agents hide friction from view, creating dangerous impact blindness - Balance power, speed, impact & joy to win in the AI era, like F1 cars - Success doesn’t equal satisfaction—you must measure both outcomes - Measure outcomes and feelings, not just activity logs and checkmarksRead the Strategy for Measuring & Improving AI Products we reference in the episode here: https://ph1.ca/blog/strategy-for-measuring-improving-ai-productsThank you for listening to the Product Impact Podcast (Formerly Design of AI)Prove impact. Improve impact. Scale impact.Learn frameworks and strategies to ensure your product is delivering impact to users, teams, businesses, and communities. We investigate enterprise adoption and highlight builders/startups disrupting value creation.Hosted by:Arpy Dragffy Guerrero https://www.linkedin.com/in/adragffy/Brittany Hobbs https://www.linkedin.com/in/brittanyhobbs/Subscribe to https://productimpactpod.substack.com for AI Strategy resourcesBrought to you by PH1 https://ph1.ca an AI strategy consultancy specialized in improving the success of your AI product.
• Why Design of AI is becoming the Product Impact Podcast 23.02.2026 16min

  We started the Design of AI podcast at the end of 2023 at a time when GenAI was a black box of possibilities. Our focus was to help people working in tech navigate a great time of change and unpack how to experiment this new technology. We've now moved into the next era of AI —scaling value and our podcast must adapt too. Where season one was focused on explaining AI and how roles will be forced to change, season 2 will focus on how to measure impact and how to scale impact.Keep up to date with the new podcast: https://productimpactpod.comOur focus is highly strategic and pragmatic. We want business, product, design, and research leaders who can unpack the uncomfortable truths about delivering impact at scale. We are seeking dedicated academics and thought leaders who challenge the status quo on how to measure and improve impact delivery. We want voices that challenge the belief that all impact is positive and who can provide a compass to guide the evolution of tech and industries.
• 52. Clawd Bot & Moltbook: When Demos Hijack Reality [Jim Love] 10.02.2026 43min

  Viral agent demos are training product teams to trust spectacle instead of outcomes—and that’s how unsafe automation slips into real workflows. In this episode we welcome Jim Love, one to the most respected voices in technology news to unpack what “Claude Bot / open claw” and Moltbook-style experiments actually prove, what they exaggerate, and why the hardest problems aren’t capability—they’re control, security, and measurement.In this episode we cover:Why viral demos distort reality: Hype spotlights novelty, not reliability—so teams miss what breaks when the demo meets real users.Local agents raise risk fast: Local access turns assistants into operators—writing, deleting, impersonating, and expanding blast radius.“It learns” is overstated: Many stacks “learn” by saving state—easy to inspect, steal, poison, and manipulate.Emergence isn’t intelligence: Weird behaviors can emerge at scale without intent—don’t mistake patterns for agency or judgment.Outcomes > inputs, always: Great teams define success, measure impact, and kill distractions—even when the tech looks magical.You’ll leave with a sharper lens for evaluating agent stacks before they create collateral damage you can’t see or stop.Jim Love has spent more than 40 years in technology, working globally as a consultant, leading an international consulting practice, serving as a CIO, and building his own consulting company. He was also CIO and head of content at the iconic publication IT World Canada.Today he runs a new publication Tech Newsday and hosts two widely followed technology podcasts, Cybersecurity Today and Hashtag Trending. He continues to advise a select group of companies, mostly startups looking to deal with AI.Jim is the author of both fiction and non-fiction, including Digital Transformation in the First Person.His latest novel, Elisa: A Tale of Quantum Kisses, explores a near-future shaped by artificial intelligence and became an Audible bestseller shortly after release.Tech Newsday — Jim Love’s publication covering tech, AI, and security.https://technewsday.com/Hashtag Trending — the podcast feed for fast tech headlines + commentary.https://technewsday.com/podcasts_categories/hashtag-trending/Elisa: A Tale of Quantum Kisses — Jim’s near-future AI novel (Amazon listing).https://www.amazon.com/Elisa-Quantum-Kisses-Jim-Love/dp/B0DPFZMDGZIf this episode helped, follow/subscribe so you don’t miss what’s next. And if you’re listening on Apple Podcasts or Spotify, leave a rating and a review—it’s the simplest way to help more product teams find the show.Get the ideas, frameworks, and episode takeaways as a written brief—subscribe to the Design of AI Substack.PH1 Research helps product teams improve digital experiences in the AI era—across strategy, benchmarking, and UX evaluations—so you can measure what matters, reduce impact blindness, and ship systems customers actually trust and adopt. Learn more at https://www.ph1.ca/.
• 51. Agents Will Disrupt Search & Shopping [Devi Parikh, CEO Yutori, ex Meta 02.02.2026 42min

  While the world is obsessed with the Moltbot/Clawdbot AI agent, founders like Devi Parikh are laying the foundation for how agents will transform search and shopping—agents that monitor, negotiate, and navigate on behalf of users, securely.Search is becoming proactive. Shopping is becoming delegated. And the next interface won’t be a results page—it’ll be agents running quietly in the background, surfacing what matters when it matters.How agents turn search into continuous monitoringWhy shopping shifts from browsing to delegationWhere value shows up first in real workflowsWhat trust requires before agents can transactThe path from alerts → actions → autonomyIn this episode, Devi breaks down how Scouts reframes search as “future-facing discovery”: track price drops, in-stock alerts, sales leads, funding news, flights, and local events—then get notified the moment conditions change.We also explore what comes next: moving from monitoring to task completion—where agents can execute purchases and bookings with explicit confirmations, hard guardrails, and a deliberate “trust staircase” designed to prevent surprises.If you enjoyed this episode, follow the podcast and leave a rating + review—it helps more builders find the show.Subscribe to the Design of AI Substack for in-depth AI product strategy resources, operator-grade analysis, and frameworks on what makes AI products succeed (and why they fail).This episode is brought to you by PH1 Research—a strategy + research partner for product leaders shipping AI-enabled experiences. We help teams define success metrics that actually matter, validate value before scaling, and reduce trust and adoption risk through AI strategy, UX evaluation, and evidence-driven product decisions.Devi Parikh is the co-founder and co-CEO of Yutori, and was previously a Senior Director in Generative AI at Meta and an Associate Professor at Georgia Tech. Her research focuses on human–AI collaboration, generative AI, multimodal AI, and AI for creativity. She holds a Ph.D. from Carnegie Mellon University and has received recognitions including the PAMI Mark Everingham Prize.Try Scouts: https://scouts.yutori.com/Blog: The Bitter Lesson for Web Agents: https://yutori.com/blog/the-bitter-lesson-for-web-agents
• 50. Designing AI for 2026: Trust, Cost, Orchestration [Yaddy Arroyo] 20.01.2026 44min

  2026 will reward AI products that get three things right: trust, cost, and orchestration. This episode looks ahead at how those forces are reshaping AI product strategy—and what teams need to pay attention to now.Brittany and Arpy are joined by Yaddy Arroyo, who has spent a decade designing multimodal AI systems in financial services, where reliability and governance are table stakes. She's also been one of the key community builders amongst the design community who are leaders within AI orgs.Together, they reflect on what the last two years of AI adoption revealed and how those lessons are directly informing decisions teams are making in 2026.Why trust now shapes AI product successOrchestration matters more than promptingToken costs quietly reshape UX decisionsWhen small models outperform large onesHow AI design roles must evolve in 2026Episode chapters01:21 Reflecting on Two Years of AI Adoption02:52 The Rise of Copilot and AI's Impact on Creativity03:37 Challenges and Concerns with AI Safety04:24 Designing AI for Human-Centric Use Cases04:53 Meta's Investment and Intelligence as a Service09:25 Hallucinations and the Reliability of LLMs11:14 The Business Value and Limitations of Gen AI18:55 Founders and the Rush to Monetize AI19:25 Token Optimization and UX Challenges21:31 Personalizing AI Interactions21:48 Challenges in AI Adoption22:27 PH One's AI Solutions22:53 The Orchestration Problem24:22 AI's Role in Everyday Tasks26:08 AI in UX and Design27:55 Future of AI and Small Language Models30:35 Human in the Loop and UI Generators37:35 Accountability and AI's Future42:39 Closing Thoughts and Future DirectionsThe conversation connects early generative AI optimism with today’s realities—probabilistic systems, rising costs, and scaling pressure—and surfaces where momentum is building, from smaller models to on-device intelligence.This episode also marks Episode 50 of Design of AI and two years of conversations with builders, researchers, and leaders shaping AI-powered products—follow the podcast to stay ahead as this next phase unfolds.About PH1The Design of AI podcast is brought to you by PH1, an AI strategy consultancy. PH1 has worked with the biggest corporations in tech to redefine CX in the era of AI through strategic research, prototyping, and aligning product to power. Visit ph1.ca to ask about your project.Go DeeperFor deeper, unfiltered thinking on AI strategy, governance, and product decisions, our Substack (https://designofai.substack.com) is the best place to follow our work. It’s where we go beyond the episodes—breaking down what’s actually changing, what’s overhyped, and what leaders should do next.Connect with the HostsContact Arpy if you’re navigating AI product strategy, platform architecture, orchestration, or high-stakes system decisions that need to scale.Contact Brittany if you need clarity on AI UX, research, service design, or evaluating whether an AI product is actually delivering value for users.
• 49. AI Was Supposed to Help Humans. What Happened? [Ovetta Sampson] 02.01.2026 48min

  If you’re building your product on private large language models, you are outsourcing control of your business—your data, your roadmap, and your long‑term defensibility—to companies whose incentives do not align with yours.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.In 2025, Ovetta left her role as Director of AI and Compute Enablement at Google to found Right AI, a consultancy focused on helping organizations minimize the human, organizational, and strategic risks of building and deploying AI.In this episode you'll learn about:Why LLM‑first architectures undermine control and defensibilityHow enterprise data is unintentionally exposed and reusedWhere “responsible AI” breaks down in practiceWhen generative AI is the wrong toolWhat safer, controllable AI systems look like insteadIf this episode challenged how you’re thinking about AI, make sure you’re following Design of AI wherever you listen to podcasts. Rating and reviewing the show helps more founders, product leaders, and designers find these conversations.For deeper, unfiltered thinking on AI strategy, governance, and product decisions, our Substack (https://designofai.substack.com) is the best place to follow our work. It’s where we go beyond the episodes—breaking down what’s actually changing, what’s overhyped, and what leaders should do next.Ovetta’s work focuses on helping leaders, designers, and organizations reduce human and systemic risk in AI—without defaulting to hype-driven architectures or opaque models.Follow Ovetta on LinkedIn: https://www.linkedin.com/in/ovettasampson/About Ovetta & her work: https://www.ovetta-sampson.com/Join her mailing list: https://www.ovetta-sampson.com/mailing-list-qr-codeRight AI (consulting & advisory): https://www.rightainow.com/Free Mindful AI Playbook (QR Code): https://docs.google.com/presentation/d/1Tzsr25r4o0g0Szz4oOSnUvrrrxAuXfhpqcB08KzdTyA/edit?usp=sharingThis is episode 49 and was hosted by Arpy Dragffy Guerrero. Follow him on LinkedIn: https://www.linkedin.com/in/adragffy/The Design of AI podcast is brought to you by PH1, an AI strategy consultancy., PH1 has worked with the biggest corporations in tech to redefine CX in the era of AI through strategic research, prototyping, and aligning product to power.