The Product Experience: a Mind the Product podcast

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Nov 2026

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The Product Experience features conversations with the product people of the world, focusing on real insights of how to improve your product practice. Part of the Mind the Product network, hosts Lily Smith (ProductTank organiser and Product Consultant) & Randy Silver (Head of Product and product management trainer) “go deep” with the best speakers from ProductTank meetups all over the globe, Mind the Product conferences, and the wider product community.

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November 11, 202648 min

A guide to building a smarter pricing strategy | Carrie Osman (Founder & CEO, Cruxy)

If anyone can copy your features in 20 minutes, how do you price what you build? Randy Silver talks to Carrie Osman, founder of Cruxy, a consultancy that works only with B2B tech companies on product value, packaging and pricing. Carrie trained at Mars, where she ran a pet food P&L, before moving into tech. They cover how to tell if your pricing is right, whose feedback to believe, why AI features that automate a basic job shouldn’t carry a price rise, and why every roadmap item needs a money figure attached. Key takeaways Weigh feedback by who gave it. In a deal review, score each piece of pricing feedback out of five. A senior, long-standing customer saying you’re underpriced is a five. Someone who saw one demo and has no budget is a zero. Carrie says procurement teams are incentivised to cut list price, so treat their pushback with caution. Expect to be told you’re overpriced. Carrie says no one will tell you the product is underpriced. Look for patterns across usage, support tickets, integration and API volumes, and buyer calls, and dig only where several signals agree. Don’t charge extra for what customers already expect. If someone buys your product to do reconciliation, they expect automation to be built in. Carrie’s test for AI features is whether they genuinely change the workflow, for example turning six steps into one or moving from a dashboard to a predictive score. Keep pricing simple enough to buy. Carrie says most CFOs and procurement teams won’t sign off a token-based “agentic calculator” with 70 variables. Include everything needed for a good experience in the base product. Charge for add-ons only where one segment has a clear, distinct need and willingness to pay. Work customer-in, not product-out. Map a day in the customer’s life and look for friction points they can’t articulate themselves. A moat is more than features. Carrie points to a narrow ICP, a strong partner or bottom-up channel, a customer-obsessed culture, and product marketing and enablement, including a sharp, short demo. Cheap needs explaining. If you’re pricing low to win market share, say so upfront. Carrie argues pricing too low can signal poor quality, even in B2B. To diagnose defensibility, overlay data by module. Map the customer workflow, then lay churn, usage, support tickets and tech stack data over your product modules and keep asking why. Put a size of prize on every roadmap item. Estimate the money saved or earned, for example churn avoided, win-rate gains and partner feedback, and refine it as you near launch. Carrie says inflated numbers are intellectually dishonest and stop you making honest trade-offs. Chapters 0:00 Intro: protecting your moat when features can be copied fast 0:43 Carrie’s route from Mars pet food to Cruxy 3:18 How do you know you’ve got pricing right? 5:38 How fast to act on a bad signal 6:42 Procurement pushback and scoring feedback by source 10:40 What AI has changed: skipping the fundamentals 12:32 Why AI on a table-stakes job isn’t a price rise 14:48 Changing the number versus changing the model 15:32 Packaging by segment and the trouble with token pricing 19:01 Table stakes, differentiators and add-ons 21:18 Customer-in, not product-out 24:21 Building a right to win beyond features 29:04 Pricing as a quality signal 32:20 Freemium and land grab 35:30 What defensibility means 37:13 Diagnosing where you sit in the stack 40:03 Handling uncomfortable conversations 41:51 Size of prize on your roadmap 44:51 Inflated impact numbers and counting the zeros 47:00 Where to learn more We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our Hosts Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant . He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs ( CPO Circles ), Product Managers ( Product In the {A}ether ) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19 . A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.

November 4, 202641 min

Why good ideas get lost between teams | Jenny Martin (The Collaboration Coach & Author of OOPSI)

Jenny Martin is a collaboration coach and the author of OOPSI . She spent 25 years leading software delivery teams before setting up her own training company in 2011, and now focuses on the people side of product development: psychological safety, learning design, facilitation and Lego Serious Play. OOPSI is a framework she first developed a decade ago to keep teams aligned on outcomes. It has since been picked up by a major US client of Jeff Patton, a global consultancy and a large tech company. Jenny joins Randy Silver to explain how it works. Key takeaways OOPSI exists to balance thinking with doing. It gives teams enough collaboration at the right moment to build the right thing, without the analysis paralysis of the waterfall era. The Asteroids metaphor explains the method. Break down only the big chunk in front of you and leave the rest unexplored. Blow everything up at once and you get overwhelmed by thousands of tiny, fast-moving pieces. An outcome is a change in the real world that creates value for someone, usually a customer, internal or external. A number on a spreadsheet is not an outcome, though it should connect to one through a chain of whys. Outputs are not what the team produces. They are what a user or system visibly delivers along the way, such as a payslip or a set of search results. Roughly 20% of them deliver 80% of the value, so this layer is where teams prioritise. The process layer is the set of interactions that generates each output, and it maps closely to a well-sized user story. Working down from outcomes and outputs gives teams vertical slices without exploring every asteroid first. Scenarios and inputs borrow from behaviour-driven development and specification by example. They suit deterministic systems where there is one right answer. AI evals handle the qualitative, non-deterministic cases, and teams will need both. Concrete test data surfaces hidden assumptions early. On a retail project, Jenny’s team showed a customer the exact data they planned to test with, and the customer spotted a flawed assumption that would have cost three or four months of work. Most teams struggle with the outcomes layer. Jenny sees it as the biggest risk now that AI lets teams move fast in the wrong direction. Chapters 00:00 Introduction 00:57 Where OOPSI came from 01:34 Who is the collaboration coach? 03:26 The problem Oopsie solves 05:52 The Asteroids metaphor 09:16 Joining up discovery and delivery 13:09 Using OOPSIE at team and company level 15:46 Outcomes 19:37 Outputs 24:30 Process 28:44 Scenarios and AI evals 33:06 Inputs 34:53 Why examples matter at every layer 37:43 What teams get wrong 39:52 Wrap-up We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our Hosts Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant . He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs ( CPO Circles ), Product Managers ( Product In the {A}ether ) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19 . A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.

October 28, 202641 min

How to protect your creativity when everything is speeding up | Federico Casalegno, VP of Design, Samsung

What changes when designers can test an idea in minutes rather than weeks? And which decisions should stay firmly in human hands? Federico Casalegno, Executive Vice President at Samsung Electronics and Head of its Design Innovation Centers and Global Studies, joins Randy Silver to explain how Samsung combines human research, machine learning, digital twins and robotic testing to improve its products. Using examples from earbuds, watches and rings, Federico walks through the process of collecting body-scanning data, defining measures for comfort and fit, and testing variations before putting physical products in people’s hands. He explains how some design tasks have moved from weeks to minutes, while real-world testing and expert judgement remain essential. They also discuss building small, multidisciplinary teams, hiring for curiosity and collaboration, and keeping people’s needs at the centre of product development as the tools become more powerful. Key takeaways Define the problem before choosing the tools. Teams need a clear understanding of what they want to improve before building or applying AI systems. Set meaningful measures. Comfort, fit and sensor performance give teams specific goals to optimise against. Combine simulation with physical testing. Samsung iterates across digital twins, robots and real users. Keep experts accountable for decisions. Data supports designers and engineers; they remain responsible for making the call. Hire for curiosity and collaboration. New kinds of work require people who can apply their skills to unfamiliar problems and learn together. Give faster production a clear purpose. The goal is to improve people’s lives through useful products and experiences. Chapters 00:00 Introduction 01:01 Federico’s journey from social sciences and MIT to Samsung 04:42 Introducing computational design at Samsung 07:40 From body-scanning data to digital twins and robotic testing 12:23 Taking some design tasks from weeks to minutes 13:47 Giving designers better data for complex decisions 15:42 Hardware timelines and designing for a wider range of people 18:13 Generative data and the limits of synthetic research 21:20 Balancing automation, optimisation and creative judgement 24:11 Building tools around the problem 26:19 Prioritising requests and discovering new problems 28:18 Building multidisciplinary teams 30:57 Small teams and the value of creative friction 33:41 Why faster production still needs meaning 35:44 Keeping people at the centre as tools evolve 37:03 What leaders should ask before pushing teams to ship 38:43 What computational design could make possible next 40:56 Credits We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our Hosts Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant . He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs ( CPO Circles ), Product Managers ( Product In the {A}ether ) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19 . A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.

October 21, 202650 min

How to make your customers stay when anyone can copy your product | Rob Ferguson (VP, Fireworks AI)

Rob Ferguson is VP of Technology and Strategy at Fireworks AI, the inference and model-customisation platform used by AI-native companies such as Cursor and Notion. He previously served as Global Head of AI at AWS and CTO of Microsoft for Startups, and has been a startup CTO through three exits. Rob joins Randy Silver to ask what a moat looks like when features can be cloned in days. They cover why competition is a good signal, why data and customer proximity now matter more than shipping speed, and why “done” is the most dangerous word in AI product development. Key takeaways Competition is evidence that a market is worth being in. Rob’s advice is to out-care and out-invest rivals rather than worry about copycats, as he saw at Automatic and Rdio. Features alone no longer protect you. A launch that took two years to build was matched to 95 to 97 percent parity within days. What kept the original ahead was its cookbooks, community, clear point of view and understanding of its audience. Rising above the noise is now the hard part. Rob’s example is a startup he advises whose cold email open rate has fallen from four percent to a fraction of one. You can no longer be just technically good. You need to be a tastemaker with a point of view. The most capable AI systems use a mixture of models, not a single one. Teams that win start by measuring what matters, splitting work into small, verifiable parts and separating “hunch” tasks from deterministic ones. Prototypes are now the start of the conversation, not the end of it. Rob’s best designers and PMs arrive with clickable prototypes, but the real work is the 50 or 60 iterations after that, and agreeing what “done” means before you show anything to a customer. The second 80 percent of product development is still hard. Replacing a function with AI overlooks the many small jobs inside it. That is why forward-deployed teams, embedded with customers and focused on the work itself, are becoming standard. Your data is worth more than the labs will tell you. Rob says Fireworks tried to calculate the discount it would need to offer customers to train on their data and couldn’t make the maths work, because the value was too high. Product fundamentals haven’t changed: build, measure, learn. Good data, a good experience, knowing your customer and asking great questions still decide who wins. Management teams should rebuild around customer proximity. Long-range plans matter less than teams that engage directly with customers, because a relationship is far harder to copy than a design. Chapters 00:00 Introduction 01:55 What a moat actually is 03:33 Why customer relationships are the most defensible moat 05:40 When features can be copied in days 09:30 Distribution, drafting and staying power 12:57 Where compute costs are heading 17:56 Fireworks and the gold rush analogy 20:44 Why the big labs don’t offer model choice 26:00 Beyond the chat interface: mixtures of models 29:48 Prototypes, vibe coding and the seductive word “done” 33:17 The second 80 percent of product development 37:09 Is AI producing revolutionary software? 40:38 Curation as a core skill 42:14 What product teams should do about moats 47:48 What management teams should plan for 50:11 Wrap-up We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our Hosts Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant . He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs ( CPO Circles ), Product Managers ( Product In the {A}ether ) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19 . A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.

October 14, 202643 min

How Google prototypes and ships at scale — Soufi Esmaeilzadeh (Google Search)

Soufi Esmaeilzadeh is product director for AI experiences in Google Search, where she leads AI Overviews and AI Mode. In 15 years at Google, she has watched search move from ten blue links to multi-turn conversation. She joins Randy Silver to explain how her team prototypes and ships at Google’s scale, why she runs lean “SWAT team” pods, and how she balances speed against the quality bar users expect from search. Key takeaways Users changed their behaviour first. Once natural language models let people drop keywords, queries grew longer and more specific, and follow-up questions became an expectation rather than a feature request. Soufi frames AI in search as an evolution, not a disruption. Positioning it as a better way to serve the user’s existing need, rather than a threat to the core product, made it easier to win internal support. Her team goes from hypothesis to scrappy prototype to real-data demo to a small beta as fast as possible. Debating in decks is the thing she most wants to avoid, because these are not one-way doors. Fast prototyping makes the hard question “is it worth it?” Ideas are cheap, so product managers must weigh people, attention and tokens against other opportunities, and build well, not just quickly. Quality bars can make or break a launch. Gen UI simulations and audio-over-image explanations landed because the team defined “good” early. A subpar first version would have fallen flat. Soufi runs lean pods: one senior strategic lead and a small bench of ICs, with flat lines of communication. Extra layers strip out context, and without context it is hard to move fast and ship well. Product basics matter more at speed. Clear hypotheses, shared goals, agreed metrics, and a joint definition of what good and not-good look like still come first. A leader’s job in a fast-moving team is to clear the noise. Urgency energises people. Chaos, spinning and cross-team debate drain them. For hiring, Soufi looks for three things: consumer product instinct, close attention to where the underlying technology is heading, and a zero-to-one builder mentality. AI tools accelerate the work, but the team keeps challenging and cross-checking their output. Sharing where each person has hit the limits is a standing conversation, because those limits keep shifting. Org design needs stable problem areas even when the technology shifts. Accountability over an area for two to three years lets people build deep opinions about what success looks like, while their methods change every few months. Chapters 00:00 Introduction 01:28 Soufi’s role and background 03:09 How AI has changed search 05:30 How AI experiences in search evolved 07:08 Delivering AI answers at Google’s speed and scale 09:04 Evolution, not disruption 12:16 What AI means for publishers 14:25 Overpersonalisation and quality feedback 16:40 Testing and betas at Google’s scale 20:40 Gen UI, simulations and visualisations 23:39 Balancing cost and ambition 25:30 Structuring AI product teams 28:21 Keeping quality high as pace increases 31:07 Changing processes and measuring speed 33:47 Avoiding burnout and context switching 36:10 Hiring and promoting 37:53 Working with AI tools and where they fall short 40:27 Designing teams for the long term 42:52 Wrap-up We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our Hosts Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant . He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs ( CPO Circles ), Product Managers ( Product In the {A}ether ) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19 . A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.

October 6, 202643 min

Evals, Analytics, and How to escape the feature factory: Francois Lopitaux (SVP Product Management, ThoughtSpot)

Francois Lopitaux is SVP of Product at ThoughtSpot, the analytics company built on the premise that you should be able to search your data like you search Google. He began his product career 18 years ago, when Salesforce acquired his Paris company, and went on to lead customer service, analytics and AI products there before joining two startups and then ThoughtSpot. He joins Randy Silver to explain how building agentic products differs from shipping deterministic software, from designing without control of the user path to keeping an LLM trustworthy when the answer has to be the same every time. Key takeaways AI has cut the time to prototype and validate, and ThoughtSpot estimates its development is two to three times faster. Productionisation still needs sound architecture, guardrails and experienced engineers. When everyone can vibe code, discipline matters more. Lepiteau compares fast AI-assisted development to driving a Formula One car on a road built for a mini Austin: the speed is only useful if you can control it. Agentic features are probabilistic, so product teams are now building guardrails around an LLM rather than a fixed step-by-step flow. That means more hypotheses and more testing. Keep the end goal fixed and stay flexible on the technology. ThoughtSpot is now revisiting existing features to see where newly released models can replace what it built with LLMs. The eval system is becoming the new PRD. It defines what good looks like and what must be avoided, in a product where the UI is a prompt and the path can't be predicted. For analytics, where "what is my revenue this year?" must always return the same answer, trust comes from using an LLM only where it is needed and supplying a semantic layer. That layer defines how your business works, so the model behaves less like a new intern guessing at your terminology. Adoption and repeat usage are the metric that matters most, backed by anonymised sampling of answer quality and follow-up questions. Enterprise customer interviews supply the qualitative temperature check. Embedded and white-label products raise the bar on trust, because customers ship the product to their own customers and carry the accountability. Features must be rebrandable and opt-in, with flags so each customer can roll out at its own pace. An AI feature is never finished. Evals are only a guess at how people will use the product, so teams must keep observing real usage after general availability. ThoughtSpot organises teams by product track, which makes that follow-up a natural continuation of the work rather than extra load. Pruning matters more now that code is cheap to generate. Lepiteau's own team has built three versions of its conversational agent, and without cutting back old versions he warns of a monster where every addition breaks something else. Lepiteau still hires technical, curious product managers. For a recent associate PM role, he asked candidates for a Git repo and a video explaining why what they built solves a problem. Execution is speeding up, so he expects smaller teams and a falling PM-to-developer ratio. Deciding what is meaningful to customers is the remaining bottleneck. 2025 was about proving AI works. Lepiteau expects 2026 and 2027 to be about proving ROI, so teams should be able to switch models easily and pick the cheapest one that does the job. Chapters 00:00 Introduction 00:48 Meet François and ThoughtSpot 02:55 How AI is changing the product development lifecycle 05:18 Why discipline matters more in the AI era 08:00 Building probabilistic features 09:43 Why evals are the new PRD 13:57 Building an eval system for agentic analytics 17:15 Creating trust with a semantic layer 19:30 Measuring the success of AI products 22:12 Trust in embedded analytics 24:19 Go-to-market for white-label and OEM products 26:54 Rolling out at your customers' pace 28:37 Why AI features are never finished 31:20 Organising teams by product track 33:42 Pruning and sunsetting features 35:38 What to look for when hiring product managers 37:56 The future of team size 40:41 Managing AI costs and model flexibility 42:49 Wrap-up We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our Hosts Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant . He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs ( CPO Circles ), Product Managers ( Product In the {A}ether ) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19 . A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.

September 30, 202644 min

Four questions to ask before building AI into your product — Kendra Vant (Chief Product Officer, Tapi)

When you use an AI tool, how much time do you spend correcting it? And would your customers be willing to do the same? Kendra Vant, Chief Product Officer at Tapi, joins Randy Silver to explore the “reliability layer”: the people, checks and engineering that make AI products dependable. She explains why enthusiastic AI users often provide that layer themselves, and why product teams can’t assume their customers will. Kendra shares four questions to ask before building AI into your product, from who catches mistakes to what reliability costs at scale. They also discuss designing for failure, the technical literacy she expects from product managers, and why faster prototyping makes experienced judgement so valuable. Chapters 00:00 Introducing Kendra Vant 02:51 The gap between an AI demo and a reliable product 06:12 Accuracy, consistency and the user as the reliability layer 13:31 Four questions to ask before building AI into your product 18:27 Sponsor: Jira Product Discovery 19:01 Planning for failure and protecting customer trust 24:06 Customisation, pricing and the cost of scaling 26:37 Do product managers need to learn to code? 31:40 How Tapi uses AI to experiment and build 33:25 Why small teams have an advantage 35:05 Product judgement and the experience gap 38:08 Using AI to maintain legacy code 39:41 Ask better questions, write down answers and test your thinking 43:11 Where to follow Kendra Key takeaways Find the work your users are doing for the AI. Correcting answers, refining prompts and spotting mistakes can make a tool feel more reliable than it is. Consider whether your customers have the time, motivation and expertise to do that work. Make someone responsible for reliability. Establish who specifies, builds and operates the checks around your AI. An impressive demo can conceal the fact that nobody owns this work yet. Price the whole product. Human review, additional models, guardrails and ongoing maintenance all affect the cost of delivery. Check whether your reliability layer remains commercially viable as usage grows. Design for the moments when it fails. Saying a model is wrong 15% of the time prompts a different conversation from saying it is 85% accurate. Identify the consequences for customers and plan how the product will recover. Build your technical literacy. Kendra expects product managers to be comfortable interacting with Git and learning from the codebase. Understanding how software works helps you ask better questions and collaborate with engineers. Recognise the value of experience. Faster tools increase what teams can build, but recognising flawed suggestions still requires judgement. Kendra raises an open question: how will newer practitioners develop that judgement as the work changes? Use writing to sharpen your thinking. Break difficult problems into smaller questions, write down your answers and explain them to a colleague. The gaps often become clearer when you have to articulate your reasoning. We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our Hosts Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant . He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs ( CPO Circles ), Product Managers ( Product In the {A}ether ) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19 . A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.

September 23, 202634 min

How to fix your product roadmap — Matt Feczko (Director of Product Ops, Ocado Technology)

Matt Feczko is director of product operations at Ocado, the UK grocery technology company whose warehouse robots and delivery automation now run for retailers including Kroger in the US and Coles in Australia. He spent the first half of his career as a customer-facing product manager, starting on SharePoint and Office at Microsoft before moving to the UK to join Skype and then four different startups over six years. A LinkedIn recruiter's typo — dropping a comma from his job title — led him to Ocado three and a half years ago, where his remit has since expanded to cover both product and engineering operations. Talking to host Randy Silver backstage at MTPcon London, he explains how he replaced Ocado's roadmap planning process, rebuilt its Jira taxonomy from scratch, and built a product management skills framework that other functions have since copied without being asked. Key takeaways Ocado's roadmap problem wasn't a lack of roadmaps, it was too many of them, rebuilt by hand every quarter. Before Feczko joined, product teams spent two months of every three-month cycle remaking roadmap slides in Google Slides, with no shared document explaining what the company was building or why, and no way for anyone outside the planning meetings to weigh in. He traced that chaos to a single root cause: an inconsistent Jira taxonomy. Different teams used "epics" to mean different things, some nesting parent epics inside parent epics, so there was no agreed definition of what a feature or an initiative actually was. Feczko fixed the hierarchy first, using Denise Tilles and Melissa Perri's book Product Operations as his reference, before touching the roadmap process at all — his team nicknamed the project the "Jira Revolution". The hard part wasn't designing the new structure, it was getting roughly 3,000 developers to adopt it. Feczko frames that adoption problem as the actual job of product operations: not producing an artefact, but changing how a large organisation works, thinks and describes its own output. Two years on, Ocado has replaced its quarterly roadmap slide decks with an internal app, built on top of Jira data, and Feczko is aiming to run the next planning cycle with zero slides. His team also worked with finance to translate operational metrics — such as items added per basket, or driver deliveries per shift — into the financial value each initiative is expected to produce, so planning conversations use the same language as the business case. To answer a question Ocado's product managers couldn't answer for themselves — what "good" looks like in the role — Feczko built a 12-skill framework across four themes, including execution, influence and strategy. He combined an existing Reforge framework with his CPO's own view of the job and his own 15 years of experience, then spent six months negotiating the exact wording with the wider team before any of it shipped. The skill Feczko insisted on calling "storytelling and evangelising", over his CPO's preferred "effective communication", has become the framework's anchor. His argument: the ability to tell a clear story about a product is itself evidence of every other skill, because it requires command of the data, a defined strategy and the ability to prioritise. The framework's real test came when other functions adopted it unprompted. Engineering, roughly 2,000 people, built its own version, as did design and data — keeping Feczko's four themes but writing skill definitions specific to each craft. That took total usage from around 150 product people to roughly 3,000 across the wider organisation. One skill, product adoption, was added specifically because Ocado's product managers didn't see driving adoption as part of their job — they considered their job done once a feature shipped, and expected the business to work out how to use it. Feczko flags this as a gap worth checking for in any company's own skills work, whatever it ends up being called: he expects most will need their own version, along the lines of "value realisation". Feczko's rule of thumb for when a company needs a dedicated product operations function is somewhere around 30 to 40 product managers, though he says the pressure shows up earlier: even at 10 people split across teams, a cross-functional roadmap with real dependencies is where the gap first becomes visible. Asked what to do first, his advice is to resist fixing everything at once. Product operations, he says, is the "connective tissue" of an organisation: find where there's tension, solve that one problem fully, then move to the next — because switching between half-solved problems just turns the job into whack-a-mole. Chapters (00:00) Slides are dead for product planning (00:26) Backstage at MTPcon London (00:49) From computer science nerd to Microsoft PM (02:02) The Skype mafia (02:58) What Ocado actually builds (04:38) How a LinkedIn typo led to Ocado's product ops job (07:14) The minimum viable bureaucracy problem (08:14) The Jira Revolution (11:46) Killing the roadmap slide deck (12:58) Translating metrics into value (14:43) Building a skills framework for product managers (17:52) Do skills differ across platform and customer teams (19:40) Storytelling and evangelising as the master skill (20:38) Using the framework for promotion and fairness (22:29) Scaling the framework to 3,000 people (23:43) How universal is the framework beyond Ocado (24:53) Vibe-coding the skills tool with AI (26:34) When does a company need product ops (28:24) Product ops in the age of vibe-coded apps (29:58) Product ops as the connective tissue (31:37) Proving the value of a thankless job (32:46) Bringing Ocado's product community together (34:35) Wrap-up Referenced Ocado — https://www.ocadogroup.com Product Operations: How Successful Companies Build Better Products at Scale, Melissa Perri and Denise Tilles — https://www.amazon.com/Product-Operations-successful-companies-products-ebook/dp/B0CKFKX94Z Reforge — https://www.reforge.com We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our Hosts Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant . He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs ( CPO Circles ), Product Managers ( Product In the {A}ether ) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19 . A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.

September 16, 202642 min

What is AI product management, really? Jonathan Evens (AI Product Lead, Google DeepMind)

Jonathan Evens is a product lead at Google DeepMind, where he has spent more than a decade applying machine learning and AI across industries — from the smart grid at AutoGrid, to detecting roads and buildings from satellite imagery at Planet, to recommender systems, Google Search's AI Overviews and AI Mode, and now live avatars. He is also an advisor to the Evens Foundation, where he is building a "digital citizenry": a democracy sandbox that uses synthetic citizens to pre-test how the public might react to a policy before it is written. Jonathan returns to The Product Experience, where hosts Lily Smith and Randy Silver pick up the conversation they started at MTPcon London, to dig further into what actually separates an AI product manager from a product manager who simply uses AI tools, why product principles have to come before evaluations, and how synthetic users can help — and mislead — at very different scales of product. We discuss: 1. Why "AI product manager" has become a near-meaningless label, and the two distinct roles hiding underneath it: the modelling product manager working on core model capabilities, and the AI feature product manager building AI-powered products 2. Why using an LLM as a thinking partner or a coding assistant does not make someone an AI product manager — it makes them a product manager using AI tools, full stop 3. How Google Search's North Star metrics have stayed constant even as the proxy metrics beneath them — side-by-side win rates, user ratings, RLHF signals — have had to be rebuilt from scratch 4. Why product principles, not evaluations, are the real starting point for any AI feature, and how Google Search resolved the problem of trustworthy sources disagreeing on basic facts 5. How Google Search builds trust into its AI Overviews through sourcing, citation placement and UX cues such as highlighting, so users can judge at a glance what to verify 6. Where synthetic users genuinely help — cold-start problems, privacy-sensitive research, automated regression testing — and where they fall short 7. Building the Evens Foundation's "digital citizenry", and the core technical problem behind it: AI-generated personas that are less diverse and more extreme than real people 8. How team size and structure differ between a fully resourced lab like Google DeepMind and a resource-constrained non-profit team, and why Jonathan resists a single answer for the "right" team size 9. How the product manager's job is shifting as engineers absorb more of the evaluation work themselves through prompting and iteration 10. Jonathan's advice for product managers building AI features, and his case for following the Makers Manifesto Key takeaways "AI product manager" covers two distinct jobs. The modelling product manager defines and measures a model's core capabilities — factuality, reasoning, long context — and that role is concentrated almost entirely inside frontier labs. The AI feature product manager builds a product or feature on top of an existing model, and needs domain expertise and user empathy far more than technical depth. Conflating the two is why the title has become so diluted. Using an LLM to think faster or write code faster does not make someone an AI product manager. It makes them a product manager using AI as part of their toolkit — the same as any other knowledge worker. The distinction matters because it clarifies what skills are actually being tested. Product principles have to come before evaluations, not after. Before Jonathan starts building an eval set for a new product, he first asks what the product is meant to feel like and what values it should encode. Google Search's response to sources disagreeing on a monument's construction date, or to large language models hallucinating at scale, came from principles about trustworthiness established before any metric was built. Trust in an AI feature is built through sourcing and interface design as much as through the model itself. Google Search's AI Overviews are constrained to draw only from ranked, trustworthy documents rather than the model's own memory, and users are given UX signals — citation placement, highlighting — that let them judge at a glance how much to verify. Synthetic users add genuine value in cold-start scenarios, privacy-sensitive research and automated regression testing. Where they fall short is diversity: AI-generated personas tend to be less varied and more extreme than real people, which is the central technical problem behind the Evens Foundation's digital citizenry project. There is no fixed answer to the right team size. Jonathan sees a gradient, from a senior developer working entirely alone, up to the Evens Foundation's single product manager with AI-assisted development skills, up to a fully staffed Google team — with the deciding factor being how unsolved the underlying problem is, not company size. As engineers absorb more evaluation work themselves through prompting and iteration, roles are blending. What still sits with product management is the judgement calls that follow from product principles — deciding, for example, which technical trade-offs actually matter to the use case, rather than which are easiest to measure. Features links - Evens Foundation — https://evensfoundation.eu - Makers Manifesto — https://makersmanifesto.org - Google DeepMind — https://deepmind.google - AutoGrid — smart grid AI company where Jonathan began applying machine learning to industry - Planet — satellite imagery company where Jonathan worked on automated road and building detection We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our Hosts Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant . He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs ( CPO Circles ), Product Managers ( Product In the {A}ether ) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19 . A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.

September 9, 202651 min

What I learned from building, and exiting a startup — Kirsten Mann (Strategic Advisor)

Kirsten Mann is a board director, product leader and, with this episode, a three-time guest on the podcast. Last time she was here, she was building Vizory, an AI tool that helps company directors get through board packs. This time she's back to explain why she isn't building it any more. Talking to Randy Silver, she walks through exiting Vizory, the friction tests she ran before writing a line of code, and why she now believes distribution, not the build, is where most AI products actually die. We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Key takeaways Before writing a line of code, Mann tested for force rather than sentiment: she asked directors for two confidential board packs and an audio recording of themselves reviewing one, and only started building once 10 people agreed to do it. Coffee-shop enthusiasm is cheap; handing over a company's most sensitive document is not. Vizory's most-loved feature in demos, cross-pack search, was barely used once shipped. Directors said they wanted it and reacted strongly when they saw it, but in practice they went straight to an automated triage view instead — a clean case of stated preference diverging from revealed preference. Board software runs on quarterly, not weekly, cycles, and trust compounds slower than value does. Mann modelled two review cycles before directors would rely on the tool; in practice it took three, meaning some customers on quarterly boards needed close to a year before the value became obvious, a timeline no 30-day trial can accommodate. Pricing got caught in a double anchoring trap. Directors first benchmarked Vizory against the $20-a-month ChatGPT and Claude price point, so Mann positioned against the cost of a governance failure instead, then created a second anchor by pricing early pilots at $150 a seat against a $300 target, which made the later increase read as a 100% price rise. Cutting friction is the wrong instinct early on. Mann deliberately added friction, asking for sensitive documents and charging as early as possible, to separate real demand from polite interest, arguing that most founders run tests designed to confirm they're right rather than to find a reason to stop. Mann's main lesson for other founders is that distribution is now the product problem. AI has collapsed the cost of building software but done nothing to the cost of reaching people who trust you, which is why she pushed to add distribution, adoption and willingness to pay as a third discipline in the Makers' Manifesto, alongside building the right thing and building it right. Multi-model products carry a new operational risk. One of Vizory's AI "judges" silently stopped being selected because the underlying model had been deprecated overnight without warning, a reminder that model drift means the QA and evals work never really stops, however fast the initial build was. We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our Hosts Lily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She’s currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant . He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury’s. He participated in Silicon Valley Product Group’s Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs ( CPO Circles ), Product Managers ( Product In the {A}ether ) and Product Coaches. He’s the author of What Do We Do Now? A Product Manager’s Guide to Strategy in the Time of COVID-19 . A recovering music journalist and editor, Randy also launched Amazon’s music stores in the US & UK.