The Product Experience: a Mind the Product podcast

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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.
11. 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.

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.

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.

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

1. 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.
2. 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".
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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".
9. 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.
10. 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.

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.

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

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.

April Dunford is the positioning expert behind Obviously Awesome and a former repeat vice-president of marketing across seven venture-backed startups, most of which were acquired. Over the past decade she has advised roughly 300 B2B technology companies, all with sales teams, on how to position themselves against the competition. In this episode she joins hosts Lily Smith and Randy Silver to explain why so many companies are repositioning at once, how a strong point of view on the future underpins good positioning, and why the story a company tells about itself is a decision for product and sales, not just marketing.

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

1. Positioning is a company-wide decision, not a marketing exercise. Dunford's own experience repositioning a spreadsheet tool as an embeddable database shows how it reshapes pricing, sales motion and even the product roadmap, not just the words on a website.
2. Major shifts such as the move to AI force widespread repositioning because customers no longer have a stable set of alternatives to judge value or price against, and both AI-native companies and traditional SaaS businesses are wrestling with where they now sit.
3. Intercom's rebuild of its support product around Fin shows how staying anchored to the customer's underlying job, resolving a ticket, can guide a genuine AI-first redesign rather than a bolt-on feature.
4. Companies need a clear, credible point of view on the future of their market, or customers facing genuine uncertainty will simply freeze and delay buying decisions.
5. A compelling vision of the future has to flow from a company's real differentiated value, not be reverse-engineered to flatter its current position.
6. Selling too far into a hyped future risks stalling deals, so the message needs a maturity model that shows customers a credible, low-risk path from where they are today to where the market is heading.
7. Positioning should be revisited on a disciplined schedule rather than settled by whoever argues loudest in the room, and Dunford recommends a structured check every six months on competitors, differentiators and value themes.
8. Customer advisory boards, quarterly executive account calls and a company's own sales team are the most reliable early-warning systems for shifts in budget, buying process and competitive threats, long before they show up as lost deals.

Chapters

• (00:00) Introduction to April Dunford 
• (01:53) The story behind Obviously Awesome 
• (05:33) Positioning as a cross-functional decision 
• (07:23) Why everyone is repositioning right now 
• (09:32) Intercom, Fin and the AI-native rebuild 
• (11:53) Building a point of view of the future 
• (16:52) Is the vision genuine or self-serving 
• (18:00) How to develop your point of view 
• (22:13) Stack Overflow, Salesforce and staying silent 
• (23:45) Hyping the future without stalling sales 
• (27:01) When positioning needs to change 
• (28:42) Running a regular positioning check-in 
• (30:29) Customer advisory boards and voice of customer 
• (34:47) Predicting change after covid and AI 
• (37:20) 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.

Julia Barham leads a CX acquisition team at Progressive Insurance and is the author of The Product Management Playbook, published by Rosenfeld Media in July 2026. She wrote it to close what she sees as a coaching gap in the profession: a decade of product transformations has produced far more people with the title than people who have been taught end-to-end craft. In this episode she makes the case for treating ambiguity as a condition of the job rather than a problem to be solved, and walks through the things that quietly break when a product starts to scale.

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Key takeaways

1. Frameworks come and go, but the questions endure. Julia structured her book around why something matters, who it is for, what needs building and when, because no methodology eliminates ambiguity, it only reduces it.
2. Organisational ambiguity is the least discussed of the four ambiguities PMs face, and the one that separates individual contributors from leaders. Waiting for the perfect operating model, funding model or team shape is a way of not moving.
3. If you have to be in every room to answer every question, you are scaling chaos. Durable artefacts, bug severity rubrics and self-service dashboards let the team make calls without you.
4. Synthesis is where shared context gets built, and offloading it to an AI tool quietly removes the thing that made it valuable. Do it together, monthly, with the customer, platform and business data in the same room.
5. Chasing growth before product market fit turns your economics upside down. Products commonly take two years to find fit, sometimes five, and retention is the strongest signal that you have it.
6. Julia spent nine months fixing latency on an inherited platform because of shortcuts taken at launch. The cost was measured in millions of lost revenue, not just engineering time.
7. Attaching a monetary value to technical debt is what turns a product manager from a stick in the mud into a steward of the business. Treat your product like a P&L whether you own one or not.
8. A product at scale needs a portfolio, not a backlog: optimisation, strategic bets, capability work, and honest capacity set aside for run-the-business support.
9. AI belongs in the toolkit, not in the chair. If you cannot call BS on the output, you do not know enough to be doing the job.

Chapters

• (00:00) Don't scale chaos
• (01:01) Introducing Julia Barham
• (03:01) The coaching gap that prompted the book
• (03:57) Why the book is built around questions, not frameworks
• (06:06) Has product management ever been deterministic?
• (07:47) The four ambiguities every product manager faces
• (10:00) Does organisational ambiguity change with company size?
• (12:57) The 21 plays and how to use them
• (15:35) Getting your reps in across the product lifecycle
• (17:07) What breaks when products and teams scale
• (17:59) Mistake one: needing to be in every room
• (20:30) Building shared context across distributed teams
• (23:51) The ways of working exercise
• (24:19) Mistake two: chasing growth before product market fit
• (25:56) How to measure product market fit
• (27:16) Mistake three: scaling expensive problems
• (29:42) Putting a price on technical debt
• (31:54) When taking on debt is the right call
• (34:22) Naming the mode your product is in
• (35:09) Mistake four: running your product as a portfolio
• (37:39) The one problem Julia would make disappear
• (39:25) What AI changes, and what it does not
• (42:26) Wrap-up

Referenced

• The Product Management Playbook, Julia Barham, Rosenfeld Media: https://rosenfeldmedia.com/books/product-management-playbook/
• Use discount code MTP20 for 20% off at Rosenfeld Media
• Sample chapter: https://rosenfeldmedia.com/sample-chapter-the-product-management-playbook/
• Dan Olsen and the product market fit pyramid: https://dan-olsen.com
• Testing Business Ideas, David Bland
• Progressive Insurance: https://www.progressive.com
• Pioneers, settlers and town planners, Simon Wardley: https://blog.gardeviance.org/2015/03/on-pioneers-settlers-town-planners-and.html

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.

Tiama Hanson-Drury is CPTO at Opus 2, the legal technology company behind the systems that run some of the world's largest and most high-stakes litigation. She started her career in sales, moved into product in 2010 and has spent the last 16 years split roughly evenly between individual contributor and leadership roles. Her verdict on AI in product organisations is neither evangelism nor scepticism: the tooling has removed software as a bottleneck, but the markers of quality that mattered before AI have not changed, and teams that forget them end up shipping slop and burning out. In this episode, we discuss:

1. Why a sales background is better preparation for product than an engineering one, and how to lead engineers without pretending to be an expert
2. How the promise of doing more with AI is producing overload and burnout, and what leaders owe their teams in response
3. Why high AI adoption scores mean nothing if pull request sizes are ballooning and someone else has to review the output
4. The three things Tiama looks for in her teams now: outcome focus over craft, curiosity, and a culture of sharing failure as readily as success
5. Why the right move is not to hand three of your seven steps to AI, but to start again from the outcome you want
6. How Opus 2 went from shipping every six to 12 weeks to shipping daily, and why that made documentation and planning more important, not less
7. The case for betting on humans in the loop when your customers explicitly prefer it
8. Why Opus 2 is still hiring juniors, and how to build commercial judgment when the groundwork can be skipped
9. The one situation where Tiama tells people to let AI write the first draft rather than the second

We're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! 

Chapters

• (01:31) From sales to product
• (03:24) Why the move is less obvious than it sounds
• (04:34) Leading engineering without an engineering background
• (06:28) How outsider questions sharpen engineering teams
• (07:36) What AI has changed for a CPTO
• (08:16) Overload, burnout and the pressure to token max
• (10:32) Adoption scores versus quality signals
• (11:39) What AI adoption actually looks like inside law firms
• (12:42) Outcomes over output
• (14:07) How Opus 2 built its AI strategy
• (16:07) Removing friction from an AI rollout
• (16:53) Partnering with AI instead of splitting up the steps
• (18:08) A message from Mike Belsito
• (19:40) Three things AI asks of a team
• (21:47) More agency, and why planning matters more
• (23:51) Scaling a team on conflicting evidence
• (26:09) Betting on humans in the loop
• (27:36) Why Opus 2 keeps hiring juniors
• (29:44) Building judgment when the work moves faster
• (32:36) Sharing your own AI operating system
• (33:22) AI slop, CVs and the hiring algorithm
• (36:17) Top tips for a product career in the age of AI
• (38:36) Wrap-up

Referenced

• Opus 2: https://www.opus2.com
• Opus 2 announces addition of Tiama Hanson-Drury as chief product and technology officer: https://www.opus2.com/news/opus-2-announces-cpto-tiama-hanson-drury/
• Minna Technologies, where Tiama was previously chief product officer: https://minnatechnologies.com
• DX, the developer intelligence platform used to track squad performance: https://getdx.com
• DORA metrics: https://dora.dev
• Vote for Mike Belsito's South by Southwest 2027 session: https://tinyurl.com/belsitosxsw

Where to find Tiama Hanson-Drury

• LinkedIn: https://www.linkedin.com/in/tiamahansondrury

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.

Blaine Billingsley is a Member of Technical Design Staff at OpenAI whose path into the field ran through music composition, spare-change website work at college, and a decade across Gmail, Airbnb, YouTube and Slack. At OpenAI he was hired to work on Presence, then redirected to solve a more immediate problem: making ChatGPT genuinely useful as a daily tool for designers. Working with a single engineer, he built the ChatGPT product design plugin — a bridge between the raw power of Codex and the day-to-day workflow of product teams. In this conversation with Randy Silver, he talks about what design actually means when your interface is a text box, why volume beats perfection in an age of infinite iteration, and why the fundamentals of good product thinking are more durable than the tools used to apply them.

Key takeaways

— The designer's job at an AI company has shifted from pixels to outputs. Deciding what "good" looks like, building the criteria to evaluate it, and heuristically assessing results is now a core part of the role — one that didn't exist in the same form at Gmail or Slack.
— LLMs don't replace structured creativity techniques; they scale them. The crazy eights exercise squeezes eight ideas from a room of people in eight minutes. A well-directed ChatGPT session can return 80 in the same window, alone, while you're at lunch.
— Volume beats perfection. The best way to make a pot isn't to try to make the best pot — it's to make a thousand pots. That logic now applies directly to prototyping: generate at scale, stay unattached, and find the nugget in the noise.
— Evals are closer to synthetic user research than quality assurance. The hardest part isn't building the rubric — it's correctly anticipating what people will actually try to do. Show it to one more person and your assumptions will immediately break.
— The second 80% problem hasn't gone away. Getting to a working prototype is dramatically faster; getting that prototype to production is still gruelling — and becomes harder still when platform direction shifts mid-sprint.
— Small teams with AI assistance need to protect the rituals that keep them aligned. When two people can each produce a week's worth of work in an afternoon, parallel drift becomes the real collaboration risk.
— Role boundaries are dissolving, but specialisations still matter. The question is less "what is your title" and more "what does the band need right now, and can you play that part?"
— Experience brings judgment; freshness brings juice. The best work often comes from junior designers unconstrained by years of accumulated assumptions — and both things need to be in the room.

Chapters 

• (00:00) Introduction
• (01:14) Blaine's background: from music composition to product design 
• (02:32) What design means at OpenAI 
• (04:21) The ChatGPT product design plugin 
• (06:12) Deciding what to build: the early exploration 
• (10:05) Structured creativity and LLM-powered ideation 
• (13:37) Volume over perfection: the thousand pots approach 
• (16:13) Designing as a two-person team 
• (20:29) What is the job now? 
• (23:33) Evals as synthetic user research 
• (27:08) Testing at scale when you can't know your users 
• (30:15) How they actually did the research 
• (33:01) The second 80%: from prototype to production 
• (35:06) Staying aligned without roadmaps 
• (38:19) Demo: the product design plugin 
• (44:52) When a prototype isn't ready to ship 
• (48:26) Design sprints, reimagined 
• (49:27) Advice for joining an AI-first product team 
• (52:17) The jazz analogy: experience, freshness and your role in the band 

Featured Links

• ChatGPT for Work: https://openai.com/chatgpt 
• Codex: https://openai.com/codex 
• Figma: https://figma.com 
• FigJam: https://www.figma.com/figjam 
• Linear: https://linear.app

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.