Product Impact Podcast | Secrets to unlocking the value of AI

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 powerful

Thank 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 guest

Juan 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

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 value

You’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 & resources

• Read the strategy we reference: https://ph1.ca/blog/strategy-for-measuring-improving-ai-products

• Take the AI Benchmarking Survey (measure your product’s impact): https://bullseyebenchmark.fillout.com/aiproducts

Thank 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.com
Brought to you by PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products.

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 checkmarks

Read the Strategy for Measuring & Improving AI Products we reference in the episode here: https://ph1.ca/blog/strategy-for-measuring-improving-ai-products

Thank 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 resources

Brought to you by PH1 https://ph1.ca an AI strategy consultancy specialized in improving the success of your AI product.

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.com

Our 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.

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/B0DPFZMDGZ

If 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/.

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 monitoring

• Why shopping shifts from browsing to delegation

• Where value shows up first in real workflows

• What trust requires before agents can transact

• The path from alerts → actions → autonomy

In 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

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 success
• Orchestration matters more than prompting
• Token costs quietly reshape UX decisions
• When small models outperform large ones
• How AI design roles must evolve in 2026

Episode chapters

01:21 Reflecting on Two Years of AI Adoption

02:52 The Rise of Copilot and AI's Impact on Creativity

03:37 Challenges and Concerns with AI Safety

04:24 Designing AI for Human-Centric Use Cases

04:53 Meta's Investment and Intelligence as a Service

09:25 Hallucinations and the Reliability of LLMs

11:14 The Business Value and Limitations of Gen AI

18:55 Founders and the Rush to Monetize AI

19:25 Token Optimization and UX Challenges

21:31 Personalizing AI Interactions

21:48 Challenges in AI Adoption

22:27 PH One's AI Solutions

22:53 The Orchestration Problem

24:22 AI's Role in Everyday Tasks

26:08 AI in UX and Design

27:55 Future of AI and Small Language Models

30:35 Human in the Loop and UI Generators

37:35 Accountability and AI's Future

42:39 Closing Thoughts and Future Directions

The 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

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About PH1
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. Visit ph1.ca to ask about your project.

Go Deeper
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.

Connect with the Hosts

• Contact 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.

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 defensibility

• How enterprise data is unintentionally exposed and reused

• Where “responsible AI” breaks down in practice

• When generative AI is the wrong tool

• What safer, controllable AI systems look like instead

If 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-code

• Right AI (consulting & advisory): https://www.rightainow.com/

• Free Mindful AI Playbook (QR Code): https://docs.google.com/presentation/d/1Tzsr25r4o0g0Szz4oOSnUvrrrxAuXfhpqcB08KzdTyA/edit?usp=sharing

This 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.

2025 is almost over, and it’s time to stop pretending everything is fine.

If you work in design, writing, product, research, or agencies, you’ve felt it: fewer jobs, lower rates, shrinking teams—and an industry telling you AI is here to free you while quietly replacing you.

In AI Trap, Episode 48, we break down the biggest myths we’ve been sold:

• AI will free creatives to do more meaningful work

• AI will create more jobs than it destroys

• AI will make us smarter and more creative

Some of these are partially true. That’s what makes them dangerous.

We look at real data, real job market signals, and what’s already happening inside agencies and tech companies. We talk about why creativity is being commoditized, why value is collapsing for most creatives, and the line too many people are crossing: outsourcing their thinking instead of outsourcing their work.

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Please help us: we’re running a short survey alongside this episode. If you work in a creative or knowledge role, your input is critical. It takes about three minutes, and it helps us separate hype from reality. https://tally.so/r/Y5D2Q5

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This is episode 48 of the Design of AI podcast.

If you found this conversation valuable, please rate and share the show — your support shapes what we explore next.

For more AI strategy, creative research, and product insight, subscribe to designofai.substack.com

Hosted by Arpy Dragffy Guerrero & Brittany Hobbs

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Most AI projects fail—not because the technology is weak, but because they’re not designed to deliver real customer value.

PH1 Research helps organizations reimagine their customer experience with AI. We pinpoint what customers actually need, prototype and test solutions, and audit AI products before they ship.

We’ve worked with teams at Microsoft, Spotify, and fast-growing startups. Learn more at ph1.ca, or reach out directly to our host, Arpy Dragffy.

AI is threatening creativity, but that's because we're giving too much control to the machine to think on our behalf.

In this episode, Dr. Maya Ackerman — AI-creativity researcher, professor, and author of Creative Machines: AI, Art & Us — explains why the danger isn’t AI itself, but the way we’re designing AI products. She breaks down how today’s tools are unintentionally flattening originality, how “Oracle-mode” models limit imagination, and why we must shift toward building systems that expand human creativity rather than automate it away.

For designers, product managers, and builders, this conversation is a blueprint for developing AI tools that inspire exploration, push users beyond predictable patterns, and create space for genuine ingenuity. If you design tools for creative work, this episode reframes what it means to build technology that actually elevates the human mind rather than quietly replacing it.

➤ Why most AI products suppress creativity
How over-alignment and “correctness” kill imaginative output, and what to do instead.

➤ How hallucinations can fuel originality
Why they’re not failures, but essential sparks for new creative directions.

➤ How AI is reshaping cultural expectations of music, art, and design
And what this means for teams building creative platforms today.

➤ How to design “Humble Creative Machines”
AI that enhances a creator’s skill and taste instead of taking over the process.

➤ How to build incentives that reward curiosity and exploration

02:50 The Role of AI in Enhancing Human Creativity03:26 Historical Perspective on AI and Creativity04:39 The Importance of Novelty and Value in AI Creativity05:52 AI's Potential Beyond Current Applications07:32 Hallucinations: Feature or Bug?08:51 Ethical Considerations in AI Creativity11:13 Humble AI: Elevating Human Creativity24:18 The Role of AI in Creativity25:49 Integrating AI into Creative Workflows26:47 AI as a Creative Assistant27:31 AI in Coding vs. Creative Fields29:51 Incentives in the Creative Domain31:43 Balancing Technology and Humanity39:33 The Future of Education with AI43:37 Practical Tips for Adapting to AI44:04 Collective and Individual Actions for the AI Era45:44 Final Thoughts and Book Promotion

Shifting product strategy away from speed and efficiency toward human ingenuity.

• Creative Machines: AI, Art & Us — Dr. Ackerman’s book
  https://maya-ackerman.com/creative-machines-book

• LyricStudio — AI-powered lyric writing
  https://lyricstudio.net

• “Humble Creative Machines: Creating AI to Elevate You”
  https://www.theaioptimist.com/p/humble-creative-machines-creating

This is Episode 47 of the Design of AI Podcast.

If you found this conversation valuable, please rate and share the show — your support shapes what we explore next.

For more AI strategy, creative research, and product insight, subscribe to https://designofai.substack.com.

Hosted by Arpy Dragffy Guerrero https://www.linkedin.com/in/adragffy/
Brought to you by PH1, a strategy consultancy specializing in prototyping the future of your products and business.https://ph1.ca

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It’s time for our year-end survey: Professional Success in the era of AI 2026

So many of you that we speak to confide in us that you’re worried about your jobs and how to stay relevant as AI is rapidly evolving.

You’ll also help us shape next year’s podcast topics. https://tally.so/r/Y5D2Q5

You can participate at   – it will only take 5 minutes of your time.