Product Impact Podcast | Secrets to unlocking the value of AI

We asked Stefanie Hutka how much of our work can actually be automated. Her answer: a lot of it will be, a lot of it should be, and even then it won't replace us. Three things stay human — embodiment, tacit knowledge, and situated action. The danger isn't the automation. It's that most people can't name their own advantage, so they let it get automated away.
Stef is the author of What Your Machines Should Do, out now from Rosenfeld. She trained as a concert violinist, took a doctorate in cognitive neuroscience, put brain-computer interfaces into hard hats, led research for Adobe's immersive tools and spent two years at Meta Reality Labs. She now runs Sendfull and teaches at Berkeley. Her book is a field manual for deciding what to automate and in what order, built on human factors and cognitive science rather than this year's tooling.

In this episode we cover:
➜ Embodiment, tacit knowledge and situated action: the three things Stef argues generative systems cannot do.
➜ Why a lot of work should be automated, and why that still doesn't mean AI replaces the people doing it.
➜ Why 87% of workers use AI while only 13% say their organisation performs significantly better for it.
➜ Why AI-first strategy gets the order wrong, and what a human-centred automation sequence looks like.
➜ How to describe your own work so the parts that can't be handed over are visible to you and your employer.
➜ What the job becomes for everyone left inspecting the output rather than producing it.
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About Stefanie Hutka, PhD:
Stefanie Hutka is the founder and head of design research at Sendfull, and the author of What Your Machines Should Do: The Science and Strategy of Human-Centered Automation (Rosenfeld Media, foreword by Jeetu Patel). She holds a doctorate in cognitive neuroscience from Toronto, where she studied why musicians and tone-language speakers hear pitch differently. In 2015 she turned down a postdoc in Helsinki and moved to Los Angeles to join a startup putting brain-computer interfaces into hard hats. She led research for Adobe's 3D and immersive authoring tools, spent two years at Meta Reality Labs on AR glasses and the Quest 3, and now advises teams building AI and robotics products while teaching product designers, PMs and researchers at Berkeley.

Resources:
➜ What Your Machines Should Do (the book): https://rosenfeldmedia.com/books/what-your-machines-should-do/ — Product Impact listeners get 20% off at rosenfeldmedia.com with code PRODUCTIMPACT
➜ Stef's Substack, Sendfull: https://sendfull.substack.com
➜ Stef's site: https://www.stefaniehutka.com/
➜ Free Maven lesson, December 9 — 3 Ways Your Agentic Product Is Losing Customers: https://maven.com/p/4cc9ef/85a012f25e53

Also referenced in this conversation: Paz Perez on context engineering (Product Impact S02E25); Rebecca Hinds and the Glean Work AI Index (Product Impact S02E22).
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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.com
Join our Slack community to get honest and strategic advice for how to navigate the AI era: https://join.slack.com/t/aiproductimpact/shared_invite/zt-49ccrauua-CUJGWYPRKI89TUu6SRrPJg

This episode was brought to you by:
➜ PH1 (https://ph1.ca) —   We're the research and strategy partner for companies like Spotify, Microsoft, Mozilla, and large institutions that have relied upon us to help improve their websites, AI products, and research operations.

Agents are taking over our work and our lives. The problem with agents is that unless they have the right context, they won't be able to automate that task you hate or buy that ticket you wanted. Our guest today is the expert on designing context and one of the most fun guests we've had.

What you'll learn in this episode:
➜ How to design context, and why it is the most important lever you have over an AI product.
➜ The risks of connecting too much context, and how to decide what an agent should never be allowed to reach.
➜ Why more context improves an agent while the wrong context breaks it, and how to tell which one you are adding.
➜ How to organize context as an ecosystem assembled fresh for every request, rather than a window you fill.
➜ What to prune when a system starts drifting, and why maintenance is a design decision instead of a cleanup task.
➜ Why the role being hired to do this work pays a median north of a hundred and eighty-eight thousand dollars, and reads like a job description for a designer who understands context.

Our guest is Paz Perez. Paz built the Gen AI for UX Teams program at Google, where more than nine hundred designers went through her training, and she coined the term model designer for the job of shaping how a model behaves rather than how its interface looks. She is now co-founder of Nothing Fancy AI, and her course on designing context has sold out every time she has run it. Before Google she was at Autodesk working on chat, machine learning and natural language processing, which put her inside generative AI before it broke into the open. And before any of that she spent ten years as an architect building public schools in New York, where scale, transitions and natural light shape how a building feels without anyone noticing the decisions behind it. She describes the work she does now in almost exactly the same words.
Today's conversation matters because agents do get significantly better when you give them context, and the measured gains are large. They also get measurably worse when you give them the wrong context, retrieving with total confidence from material that had nothing to do with the task. Being trained on the power of context is the difference.
We also have some news to share, partly inspired by Paz's amazing course.
Arpy and I are also launching a course on Maven called Improve Your AI Agentic Product. It collects the methods we have heard from more than seventy guests on this show alongside the work we do at PH1 with Spotify, Microsoft and a long list of large institutions.
Register here https://maven.com/arpy/improve-agent
Listeners get 20% off with promo code PRODUCTIMPACT
So if you have taken a course like Paz's and built an agent, ours picks up where that leaves off: adding KPIs, measuring the outcomes it actually drives, and improving the quality of what it puts out.

And a reminder that this episode is brought to you by our consultancy PH1. We are the preferred research & strategy partner for teams building intelligent experiences. We partner with teams to improve the customer-facing value of their AI-powered products. This includes taking your evals and KPIs to the next level. Find us at https://ph1.ca.

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:
Paz Perez — https://www.linkedin.com/in/pazperez/
Nothing Fancy AI — https://www.nothingfancy.ai/
Designing Context for AI Systems (course) — https://maven.com/paz-perez/context-architecture

Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.com

Search sends fewer people to your website every month, and the answer engines keep most of the ones it does send. Kevin Wong, VP of Design at Webflow, doesn't think that means the website is dead — he thinks it means the metric you have been judging it by is.

Kevin leads product design, brand studio, content and user research at Webflow, where millions of businesses build their sites, which gives him a read on what is happening across the market rather than inside one company's analytics. Brittany and Arpy get into what that view shows: why the people who still click convert far better, how a brand earns its way into being the answer, why building one website for everyone is now the wrong goal, and — in a stretch this show has never covered — how Webflow turned design itself into something agents can execute against.

What you'll learn in this episode:
➜ Why traffic stopped being the metric that matters, and what Webflow watches instead.
➜ Answer engine optimization: how brands get cited as the answer, and why it isn't SEO with a new name.
➜ The museum is over: designing many versions of a site and serving the right one, not one design for everyone.
➜ Why Kevin won't drop users into a blank prompt box, and where he draws the line on probabilistic software.
➜ Webflow's design system rebuilt as instructions — skills that lint and review designs, recipes for each view type.
➜ Evals before you build: define what good looks like upfront, or find out too late that it never was.

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Kevin Wong is VP of Design at Webflow, where he leads the Product Design, Brand Studio, Content, and User Research teams for a platform used by millions of designers, marketers, and developers to build enterprise-ready web experiences. His work focuses on shaping Webflow into an AI-native website experience platform that balances ease of use with professional power. Previously he led design for Meta's Remote Presence team across Facebook, Instagram and Messenger, and before that led Airbnb's global CX Platform Design organization, building the foundational service design capabilities and tools that supported guests, hosts, and agents.

➜ Kevin on LinkedIn: https://www.linkedin.com/in/kvwong
➜ Webflow: https://webflow.com
➜ Google and LLMs Are Destroying Your Website & Traffic — Arpy's analysis (Product Impact):
https://productimpactpod.com/news/google-and-llms-are-destroying-your-website-and-traffic
➜ SparkToro's research on zero-click search: https://sparktoro.com/blog/in-2026-less-than-one-third-of-google-searches-still-send-a-click/

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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 insights and strategic playbook hub. Check it out.

Hosted by:
➜ Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/
➜ Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/

Join our Slack community to get honest and strategic advice for how to navigate the AI era: https://join.slack.com/t/aiproductimpact/shared_invite/zt-49ccrauua-CUJGWYPRKI89TUu6SRrPJg

This episode was brought to you by:
➜ PH1 (https://ph1.ca) — We solve the first and last mile of AI: use cases, KPIs, evals, and scaling impact.

Most companies handed their people a powerful new machine and said "go do something with AI." Melissa M. Reeve, author of Hyperadaptive: Rewiring the Enterprise to Become AI-Native, argues that's the whole reason the returns haven't shown up — and the reason so many of us feel like we're sprinting to stay in place.
Melissa has spent three decades inside organizational transformation, and her case is that AI is an organizational design problem rather than a tools problem. Brittany and Arpy get into what that means for the people living it: where the jobs actually go when work gets automated, why the career ladder is dissolving into something else, why she's genuinely bullish on UX, and what to do when it doesn't feel safe to raise your hand and ask to slow down.

In this episode we cover:
➜ The "hand wave": how "go do something with AI" cascades down a company and produces activity without impact.
➜ Melissa's AI learning flywheel — activation hubs and AI leads, so not everyone learns every new model alone.
➜ When work gets automated, jobs shift to building, monitoring, and maintaining the thing that now does it.
➜ Roles are breaking down into skills. The ladder is going away, and portfolio careers are replacing it.
➜ Why Melissa is bullish on UX: the pie expands, and high-craft work gets more valuable, not less.
➜ The rocks-and-sand problem: protect the time AI frees up, or noise collapses straight back into it.
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Melissa M. Reeve is the author of Hyperadaptive: Rewiring the Enterprise to Become AI-Native (IT Revolution, with Ryan Martens). She has spent more than three decades at the intersection of executive leadership, organizational transformation, and process excellence, with a Lean foundation that started on a factory floor in Japan in the mid-nineties. She went on to run the Agile Marketing Alliance, bringing agile software practices into marketing, and now advises leaders on becoming AI-native. In the book she lays out a five-stage maturity path for rewiring how an organization actually operates — governance, champions, agentic AI, and reorganizing around customer value streams.
➜ Hyperadaptive (the book): https://www.melissareeve.com/book
➜ Melissa's site: https://www.melissareeve.com/
➜ Her course, Running Hyperadaptive Organizations: https://hyperadaptive.solutions

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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 insights and strategic playbook hub. Check it out.

Hosted by:
➜ Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/
➜ Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/

Join our Slack community to get honest and strategic advice for how to navigate the AI era: https://join.slack.com/t/aiproductimpact/shared_invite/zt-49ccrauua-CUJGWYPRKI89TUu6SRrPJg

This episode was brought to you by:
➜ PH1 (https://ph1.ca) — We solve the first and last mile of AI: use cases, KPIs, evals, and scaling impact.

Most companies are burning millions chasing an edge with AI and getting almost nothing back. Rebecca Hinds runs the Work AI Institute at Glean, and her research maps exactly what the small group of teams actually winning with AI do differently — and why everyone else stalls. In Glean's 2026 Work AI Index, 87% of workers said AI improves their work, but only 13% of their companies say they're actually performing better because of it. The winners closed that gap on purpose, and this episode is about how.
We get brutally honest with Rebecca about our own experience — feeling like the AI's assistant, drowning in unreliable output, riding waves of excitement and dread — and she ties each one back to the data: what the leading teams do to turn individual speed into real organizational value, the 6.4 hours a week most workers lose to "botsitting," why context (not more tokens or MCP connections) is the real unlock, why domain expertise decides who actually benefits, and how the best organizations measure value without chasing vanity metrics.

In this episode we cover:
➜ What the teams winning with AI actually do
➜ How top teams turn AI into real value, fast
➜ Why context beats bigger prompts and MCP
➜ Measure value with your old KPIs, not new ones
➜ The domain expertise that decides who wins
➜ Your team wastes a day a week botsitting AI

About Rebecca Hinds:
Rebecca Hinds, PhD, is a leading expert on organizational behavior and the future of work. For more than 15 years, she has helped organizations rethink how work gets done, challenging outdated management playbooks and offering leaders a more ambitious—and more human—vision for the future.
Rebecca has founded and led two first-of-their-kind research institutes dedicated to reinventing work. In 2022, she launched the Work Innovation Lab at Asana, building it into an influential source of practical workplace research. In 2025, she founded the Work AI Institute at Glean to help organizations navigate the next transformation of work.
Called a "professional meeting hacker" by Adam Grant, Rebecca is the author of Your Best Meeting Ever, an instant national bestseller named a must-read by the Next Big Idea Club. Her award-winning research has been featured in Harvard Business Review, The New York Times, The Wall Street Journal, TIME, Forbes, and Inc. She is a co-instructor of the CNBC Make It course How to Use AI to Be More Successful at Work and has served as a columnist for Inc. and Reworked. She also serves on the advisory boards of the Remote Work Institute and the Doodle Time Institute.
Rebecca holds a B.S., M.S., and Ph.D. from Stanford University, where her doctoral research examined how emerging technologies reshape the way people work together.
 https://www.linkedin.com/in/rebecca-hinds/
https://www.rebeccahinds.com/
https://www.glean.com/work-ai-institute

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Research featured in this episode:
➜ The Work AI Index (2026)  ⁠https://www.glean.com/work-ai-institute/reports/work-ai-index⁠
➜ The AI Transformation 100 (December 2025, with Bob Sutton) —  ⁠https://www.glean.com/work-ai-institute/reports/ai-transformation-100?page=1⁠
➜ The ETH Zurich vibe-coding study  ⁠https://ethz.ch/en/news-and-events/eth-news/news/2026/04/what-skills-do-people-need-to-successfully-program-with-ai.html⁠
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Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.com
Join our Slack community to get honest and strategic advice for how to navigate the AI era: https://join.slack.com/t/aiproductimpact/shared_invite/zt-49ccrauua-CUJGWYPRKI89TUu6SRrPJg

This episode was brought to you by:
➜ PH1 (https://ph1.ca) — We solve the first and last mile of AI: use cases, KPIs, evals, and scaling impact.

Hosted by:
➜ Arpy Dragffy Guerrero — ⁠https://www.linkedin.com/in/adragffy/⁠
➜ Brittany Hobbs — ⁠https://www.linkedin.com/in/brittanyhobbs/⁠