Product Coalition Podcast

A team can deliver exactly what a customer requested and still solve the wrong problem. Norma Løvhaugen (Neat) has seen the difference come down to what leaders give their teams at the start. A prescribed answer narrows the work. Clear context lets engineers, designers and product managers find options nobody could see alone.

We get into how Neat learns what sits underneath a customer request, why concerns need to surface early, and how teams stop products before launch when the evidence changes. Norma also explains what genuine trust looks like in a room, why AI cannot replace customer relationships, and how Norway’s flat working culture encourages useful pushback.

What you'll hear

* Why describing the customer problem can unlock better options across engineering and design.

* How a company with hardware and software disciplines makes trade-offs without hiding concerns.

* Why detailed evidence makes it easier to challenge a direction from senior leadership.

* What passion, integrity and competence sound like when Norma interviews product candidates.

* How laughter, disagreement and informal conversation reveal whether a team genuinely trusts itself.

“When everyone feels that their opinion is taken seriously, the more they will share, right?” - Norma Løvhaugen, 16:28

Chapters

* 00:00 Why context matters more than direction

* 02:50 Designing technology that disappears

* 06:29 Learning what customers actually need

* 08:15 Finding the problem beneath the request

* 10:15 How context unlocks engineering and design ideas

* 12:50 Helping remote colleagues feel included

* 14:34 Building a culture where concerns surface early

* 18:20 Stopping products before launch

* 20:53 Challenging direction with evidence and detail

* 23:53 Keeping transparency as Neat grows past 500 people

* 25:34 Hiring for passion, integrity and competence

* 29:09 Keeping customer relationships human in AI workflows

* 31:07 Why lunch and coffee spark ideas

* 33:22 Recognising genuine trust in a team

* 35:41 Norway’s flat working culture and healthy pushback

* 37:53 Coordinated holidays and shared recovery time

Norma Løvhaugen has worked at Neat since 2020, helping multidisciplinary teams build video-conferencing products that connect people inside and outside the room.

A payment method can lift conversion and still leave the product worse off. Kevin Armstrong (Capital One, Walgreens, Amazon, SpaceX) saw that tension while leading Prime payment strategy across 13 markets. In Spain, the card-led assumptions that worked in the US missed customers already using Bizum. In Brazil, early PIX adoption looked encouraging, but retention told a different story because the QR-code flow could not support recurring charges.

This conversation gets into the product decisions behind those results. We cover involuntary churn from expired, lost or stolen cards, why local consent rules change subscription journeys, and how rewards depend on an existing reason to return. Kevin also explains why payment behaviour is a useful signal of trust, not merely a checkout metric.

What you'll hear

* Why a US-built card checkout missed Spanish customers who already relied on Bizum.

* How expired, lost and stolen cards can make involuntary churn look like cancellation.

* Why local consent rules changed the Prime sign-up journey Kevin’s team could offer.

* How PIX increased payment adoption while the missing recurring-charge flow weakened retention.

* Why rewards amplify an existing habit but struggle to create one from nothing.

“Payments is a representation of our trust. It tells who we are.” - Kevin Armstrong, 34:23

Chapters

* 00:00 Card payments and home-market assumptions

* 02:38 Moving from engineering into product

* 05:56 The complexity of launching across companies

* 08:22 Bizum and the Spanish customer gap

* 10:13 Household penetration, churn and virtual cards

* 12:36 Involuntary churn and wallet cycling

* 15:22 Consent and checkout in Spain

* 18:33 Rewards, retention and behaviour change

* 21:07 Making the customer case for rewards

* 23:56 What makes a rewards system work

* 26:03 Stickiness before rewards

* 28:56 PIX and misleading adoption signals

* 31:28 How early PIX adoption masked churn

* 33:44 Payments as a measure of trust

* 35:41 Learning the funnel through a small build

* 37:53 Cryptocurrency, privacy and consumer protection

Kevin Armstrong started as an iOS engineer at Capital One before working at Walgreens, Amazon and SpaceX. At Amazon, he led Prime payment strategy across 13 markets and launched Bizum for Prime in Spain. He now runs a product services business and coaches product managers through payments interviews.

Eshaan Jain (Amazon, PwC) gave me the example that made agentic AI stop sounding clever and start sounding expensive. An agent negotiating a discount on your behalf pushes past the point of sense and sells a product for $5 that cost $10 to buy. Nobody had told it there was a floor. Eshaan spent nearly five years at Amazon on a $40 billion annual contract portfolio, and now advises T-Mobile, so he has watched automation fail with real money attached.

What you'll hear

* The risk register Eshaan keeps, and which decisions he lets an agent make with no human at all.

* Why a discount pushed from 10 to 18 percent stops the agent, pulls in a person, and rolls back if nobody answers.

* How Amazon taught a machine learning model to read vendor contracts, and the 95 percent accuracy it reached.

* The chain of specialised agents he would build across quote to cash, from catalog lookup to contract review.

* Why the AI programmes still running in 2028 will be the ones measuring carbon, not the ones with the biggest models.

"So if you procure the product for, let's say, $10 and the agent is now selling the product for $5, that is a big no no. So that is a critical risk for me." — Eshaan Jain, [00:26:42]

"The AI projects and the programs that are going to last 2027 and will eventually move into 2028 will not be the ones that will have the most powerful models. They will be the ones who have clear visibility on both the cost and the carbon emissions for each of their inferences." — Eshaan Jain, [00:32:19]

Chapters

* 00:00 Why agentic AI lands in the back office first, not the chat box

* 03:00 What drives him: the book Agentic Enterprise and an IEEE paper on carbon-aware inference

* 05:44 What agentic AI actually changes about a product manager's job

* 07:30 The one thing AI has not taken over: prioritisation still needs human judgment

* 08:51 Quote to cash explained, and where a chain of agents would sit inside it

* 10:02 Inside a $40 billion portfolio: teaching a model to read Amazon's vendor contracts

* 13:44 What large language models do with contracts that older models could not

* 15:58 The specialised agents he would build for pricing, carts and contract review

* 19:02 Two scaling lessons from Amazon: never underestimate the data, then define accuracy

* 21:11 What he had to unlearn moving from in-house at Amazon to the T-Mobile account

* 23:25 In the boardroom: why product managers now have to think like risk officers

* 24:58 The risk register: severity tiers, and which calls an agent may make alone

* 25:53 Past 10 percent: human approval, the rollback window, and the $10 product sold for $5

* 27:36 What evaluation and multi-level approval look like before an agent reaches production

* 29:09 The token optimisers' manifesto: the inference cost nobody budgets for

* 30:29 Power, water and pollution: why communities are pushing back on new data centres

* 31:27 Dynamic routing: stop sending every email to a frontier model

* 33:26 One change to make next week: inventory every workflow, then pilot one

Eshaan Jain is a senior product manager at Mphasis Silverline on the T-Mobile account, after nearly five years as a senior technical product manager at Amazon and three years as a Salesforce consultant in PwC's digital risk practice. He is also publicity officer for the British Computer Society's open source specialist group.

Summary

In this episode, Jay Stansell interviews Jürgen Dauk (Oracle, OpenText, Avaya) about the common pitfalls that slow down product teams as organizations grow, and practical strategies to accelerate decision-making without losing clarity. They explore cultural differences, organizational structures, and mindset shifts necessary for faster, more effective product development.

Key topics

* Decision bottlenecks in product organizations

* Organizational culture and its impact on speed

* Practical strategies for redesigning decision-making

* Cultural differences between Europe and the US

* The role of trust and empowerment in decision speed

Chapters

* 00:00 Introduction to the episode and guest

* 02:14 Main themes: decision bottlenecks, friction, and speed

* 07:42 Jürgen’s personal journey and passion for organizational speed

* 08:44 Understanding organizational change and cultural mindset

* 12:49 The importance of trust and decision autonomy

* 13:55 Root causes of organizational slowdown: command and control

* 15:12 The impact of market change and outdated systems

* 16:23 Shifting mindset: from hierarchy to cross-functional teams

* 18:23 Overcoming theater and superficial alignment

* 20:00 Strategies for socializing decision-making changes

* 22:17 Cultural differences in decision-making and communication

* 26:53 Assessing the stakes and reversibility of decisions

* 28:59 Guidelines and rules for distributed decision-making

* 31:25 Implementing decision guardrails in large organizations

* 34:22 Building psychological safety and trust at the grassroots level

* 36:12 Small steps for grassroots change and peer alignment

* 37:27 Measuring decision speed and effectiveness

* 39:11 Employee experience and empowerment metrics

* 41:06 Socializing change through informal conversations

* 42:42 Opportunities in the current technological landscape

* 44:30 Advice for newcomers and early career professionals

* 46:51 The future of work and embracing change

Summary

In this episode, Matt Wensing, VP of Products and Design at Customer.io, shares insights from his two-decade journey from founder to product leader. We explore decision-making, maintaining product standards, integrating AI, and scaling organizational taste.

Key topics

* Transition from founder to organizational leader

* Decision-making and product velocity

* Maintaining product standards and taste

* Integrating AI into product teams

* Scaling organizational culture and mentorship

Chapters

* 00:00 Introduction to Matt Wensing and his journey

* 00:10 Matt’s background and transition from founder to VP

* 01:23 Unlearning founder instincts and decision-making

* 04:23 Lessons on burnout and resilience

* 06:44 Product velocity governed by decision-making

* 10:35 Joining Customer.io at scale

* 14:11 Suppressing founder habits in a remote organization

* 17:53 Prioritization and understanding organizational funnels

* 24:40 Calibrating product standards and taste

* 29:41 Managing decision-making across a large organization

* 33:58 The importance of intangible leadership and mentorship

* 35:42 Implementing AI to scale product teams

* 39:33 Using AI responsibly in product management

* 42:30 The importance of quality checks and modeling

* 46:15 Final thoughts on distribution and startup success

Summary

In this episode, Jay Stansell interviews Dave West (Forrester) from Scrum.org about the evolving role of AI in product management. They explore how AI acts as a teammate, its limitations, and its impact

Key topics

* AI as a teammate in product management

* Limitations of AI in understanding context and judgment

* Impact of AI on team organization and decision-making

* The evolution of Scrum in an AI-driven world

* Practical use cases of AI in product management

Chapters

* 00:00 Introduction to AI in Product Management

* 01:48 The Evolution of AI and Its Impact

* 07:33 AI’s Role in Balancing Strategy and Tactics

* 14:51 The Limitations of AI in Empathy and Context

* 20:06 Practical Use Cases of AI in Product Management

* 28:13 The Future of AI in Product Ownership

Summary

In this episode, Jay Stansell interviews Saurabh Sharma, a product leader with extensive experience at Google and u.com, exploring how enterprise AI products are transforming product management, user engagement, and agent management. Discover insights on building primitives, managing evolving roadmaps, and the future of AI-driven workflows.

Key topics

* Impact of user-built workflows on product teams

* Building and managing primitives in AI platforms

* Measuring success with primitives vs features

* Evolving product roadmaps based on user signals

* Agent management and enterprise AI adoption

Chapters

* 00:00 Introduction to Product Coalition Podcast

* 00:10 Meet Sarab Sharma: A Journey Through Product Management

* 02:39 Understanding U.com: AI Web Search Infrastructure

* 05:02 Shifting from Consumer to Infrastructure Product Management

* 08:20 Defining Primitives in Product Management

* 11:23 Recognizing When Features Outgrow Their Relevance

* 12:47 The Importance of Using Your Own Product

* 15:45 Agent Management in the Age of AI

* 19:14 Research Discipline in Product Management

* 21:03 Evolving Product Management Skills in the AI Era

* 23:25 User Experience in API Products

* 25:33 Challenges in Moving from Prototype to Production

* 27:31 A Memorable Product Management War Story

Summary

Andrea Bronzini, a seasoned expert in experimentation and conversion optimization, shares insights on the limitations of traditional A/B testing frameworks, the impact of noise on experiment results, and introduces innovative tools to improve decision-making in product growth.Tools MentionedNoise ExplorerExplore the effect of stochastic noise on your experiments and fine-tune your decision policy. https://confidentstory.com/noise/

Noise CheckEvaluate the results of split tests or compare KPIs to rule out noise wobbling. https://confidentstory.com/noisecheck/

GTMsplitRun split tests in Google Tag Manager (100% free). https://confidentstory.com/gtmsplit/

Key topics

* Limitations of traditional A/B testing frameworks

* Impact of stochastic noise on experiment outcomes

* The importance of uncertainty in decision-making

* Tools for simulating experiment outcomes and understanding noise

* Reevaluating the use of statistical significance in experiments

* Decision policies and experiment duration considerations

* The role of noise in KPIs and long-term metrics

Chapters

* 00:00 Introduction to Andrea Bronzini and the episode’s focus

* 01:13 The industry’s stagnation in experimentation practices

* 03:15 Fundamentals of healthy experimentation and decision policies

* 08:44 Understanding stochastic noise and its visual representation

* 12:53 The importance of uncertainty over point estimates

* 19:34 Simulating experiment outcomes to inform decision-making

* 23:43 The impact of noise on KPIs and long-term metrics

* 27:21 Reevaluating decision thresholds and significance levels

* 32:33 Practical steps for product teams to improve decision quality

* 36:27 Tools for noise detection and better experiment analysis

* 38:12 Closing remarks and where to learn more about Andrea’s tools

Summary

In this episode, Weiwei Hu, an analytics and AI leader, discusses how data analytics can be transformed from mere reporting to driving strategic decisions. She explores the disconnect between analytics and business strategy, the impact of AI on analytics workflows, and how to leverage data for better decision-making.

Take aways

* Disconnect between analytics and strategy

* AI’s role in transforming analytics workflows

* The importance of decision-focused analytics

* Storytelling and stakeholder communication in analytics

* The evolving role of analytics teams in the AI era

Chapters

* 00:00 Introduction to Analytics and AI in Product Leadership

* 02:53 The Disconnect Between Analytics and Strategy

* 10:08 The Role of AI in Enhancing Analytics

* 22:04 Shifting from Data-Driven to Outcome-Driven Culture

* 28:53 Deciding What Analytics to Retire

* 33:31 The Importance of Tailored Storytelling in Analytics

Brought to you by ExecReps.ai — AI-powered executive presence coaching for teams: go.productcoalition.com/podcast-sponsor

98% of PMs use AI daily, but only 39% have role-specific training. Beatrice led a major GA research survey of 117 PMs across US, UK, Canada, and Singapore. Episode covers the skills gap, shadow AI risks, vibe coding, and what AI can't replace.

Key topics

* Essential skills for future-proof PMs

* Safe AI experimentation practices

* Critical evaluation of AI outputs

Chapters

* 00:00 The Journey of Beatrice Partain

* 04:14 Future Proofing Product Management

* 10:36 Developing Skills Safely

* 13:22 Innovation vs. Governance in Product Management

* 16:29 The Human Element in AI

* 19:47 Lived Experiences in Product Management

* 23:46 Training for Product Management

* 26:54 Agentic AI in Daily Workflows

* 28:21 Personal Use of AI

* 34:40 Curiosity and Cultural Insights

Got a product story worth telling? Whether you’re an exceptional talent shaping the future of product, or you’ve got a career journey others can learn from — we want to hear from you. Apply to be a guest on the Product Coalition podcast: go.productcoalition.com/become-a-guest