The Product Engineering Podcast

James Charlesworth
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The Product Engineering Podcast explores how software teams can move beyond shipping features and start delivering measurable outcomes. Hosted by engineering leader James Charlesworth, each episode covers practical ideas for product engineering, engineering leadership, team operating models and building products that create real impact.

Episodes

  1. 5d ago

    Should "Product Engineer" Be A Job Title? (with Phil Hornby)

    Where should the line between product management and engineering sit? And in a world where AI is making it easier for anyone to build software, do we even need that boundary anymore? In this episode of The Product Engineering Podcast, James is joined by Phil Hornby, product leadership coach, founder of For Product People and host of the Talking Roadmaps podcast, to explore the increasingly blurred boundaries between product management and engineering. Drawing on Phil's experience across both engineering and product leadership, we discuss whether organisations should define responsibilities around job titles or build teams around the strengths of the people within them. We explore what it really means to be a Product Engineer, why engineers should get closer to customers and business decisions, and whether AI could eventually enable individuals to take on responsibilities that previously required entire cross-functional teams. We cover: Where the boundary between product management and engineering should sit, and whether we need one at allWhy organisations should be designed around people's individual strengths rather than rigid job descriptionsHow engineers can participate directly in product discovery without becoming product managersThe difference between understanding users and understanding the customers who actually pay for a productWhy product management is as much about business strategy, pricing and commercial viability as it is about user experienceWhether AI makes the idea of a one-person product engineering team realisticUsing AI and personal knowledge management systems to capture context, accelerate thinking and improve writingWhy storytelling is an essential skill for communicating customer problems to engineering teamsThe danger of using AI to turn feature requests directly into working software without understanding the underlying problemThe trade-offs between generalist Product Engineers and deeply specialised engineering teamsHow different organisational structures, from functional organisations to cross-functional teams, can both be successfulWhy engineers should become more curious about customers, business models and the commercial outcomes their work supportsA recurring theme is that high-performing teams aren't created by finding people who fit neatly into predefined roles. They're created by combining people with complementary strengths and giving them the freedom to contribute where they can add the most value. We finish with Phil's advice for engineers who want to become more product-focused: stop worrying so much about the terminology and start developing genuine curiosity about your customers, your users and your business. Find Phil: For Product People: Phil's product leadership coaching and trainingTalking Roadmaps: Phil's podcast exploring product roadmaps and product leadershipLinkedIn: Search for Phil Hornby

    Should "Product Engineer" Be A Job Title? (with Phil Hornby)

  2. Oct 13

    Engineering Management In Product Engineering (with Ryan Murphy)

    What does engineering management look like in a world where AI can write much of the code, and engineers are increasingly expected to think like product managers? In this episode of The Product Engineering Podcast, James is joined by Ryan Murphy, founder of EM Accelerator and Crying on the Sofa, to explore the changing role of engineering managers and what it means to lead an effective Product Engineering team. We discuss the relationship between engineering managers and product managers, why managers sometimes become unnecessary barriers between engineers and the wider business, and how to build teams that focus on outcomes rather than simply delivering features. We also explore how AI is changing engineering management, why communication and stakeholder management are becoming more important, and whether engineering managers will need to become more hands-on again. We cover: How the relationship between engineering managers and product managers affects team effectivenessWhy engineering managers shouldn't act as gatekeepers between engineers and productHow misunderstandings between product and engineering create unnecessary conflictWhy engineers should spend time with sales, marketing and customer success to develop product understandingCelebrating product outcomes and learning rather than just technical excellence and deliveryHow AI coding tools are changing the skills engineers and engineering managers needHelping introverted engineers develop their communication and stakeholder management skillsUsing AI as a management assistant, sounding board and organisational toolThe debate around AI-generated writing and preserving authenticityWhy the human side of engineering management may become more important as AI takes on more technical workSupporting employees struggling with their mental health, and the importance of active listeningOne of the central questions is whether AI will turn engineering managers back into hands-on developers, or whether the increasing importance of coaching, communication and team wellbeing will push the role further towards people leadership. We finish by discussing mental health in engineering, including Ryan's experience and his work with Crying on the Sofa, an initiative aiming to start one million conversations about mental health. Find Ryan: EM Accelerator: https://emaccelerator.comCrying on the Sofa: https://www.cryingonthesofa.org

    Engineering Management In Product Engineering (with Ryan Murphy)

  3. Oct 6

    What Makes AI Code Production Ready?

    AI can get you to something that looks finished remarkably quickly. But looking finished and being production-ready are not the same thing. In this episode of The Product Engineering Podcast, James explores the gap between an AI-built prototype and software that is actually ready to run reliably in production. A prototype might get you 80% of the visible result very quickly, while the remaining 20% — security, scalability, observability, maintainability and operational support — can require far more work than people realise.  We cover:  Why an impressive AI prototype isn't necessarily production-ready  The hidden engineering work users never see  Scalability, security, observability and maintainability  Why software needs to be designed for the right level of scale  The difference between code that demonstrates an idea and code you can support long term  Why prototypes are still incredibly valuable learning tools  When it's perfectly reasonable to build something you intend to throw away  The role engineers play when product managers prototype with AI  Why “production-ready” depends on the needs of your organisation  The danger of both under-engineering and over-engineering Why prototypes shouldn't be slowed down by unnecessary production requirements  Making the gap between prototype and production explicit A prototype doesn't need to answer every engineering question if its purpose is to answer a product question. If it helps you discover whether an idea is worth pursuing, it has already delivered value — even if none of its code eventually reaches production.  The important distinction comes once you've decided to keep it. At that point, the team needs to understand exactly what still has to happen before the software can safely and sustainably become part of the product.

    What Makes AI Code Production Ready?

  4. Sep 29

    Giving Product Engineers Direct Access To Users

    The Product Engineering Podcast

    Giving Product Engineers Direct Access To Users

  5. Sep 22

    When To Use AI In Product Engineering

    AI can write code incredibly quickly. But simply using AI to make your existing software delivery process faster misses much of the opportunity. In this episode of The Product Engineering Podcast, James looks at how AI changes Product Engineering when you treat it as a learning tool, rather than simply a faster way to turn requirements into production code. AI is excellent at implementing a well-defined plan, but human judgement is still needed to decide whether that plan represents the right thing to build.  We cover:  Why using AI to accelerate a legacy delivery model isn't enough  Where human product judgement still matters  Why AI changes the economics of experimentation  Moving engineers and product managers further upstream into product decisions  Using AI to explore several possible solutions rather than committing immediately to one  Creating prototypes and thin product slices that may never reach production  Why engineers can use AI to test ideas directly with users  How AI can compress the feedback loop between an idea and evidence  Why killing ideas quickly can be more valuable than shipping them quickly  Why discarded AI-generated code isn't necessarily waste  A useful diagnostic: how much of the code you're generating actually reaches production? Before AI, code was expensive enough that teams had strong incentives to decide what to build before implementation began. AI lowers that cost dramatically. That means teams can afford to build something partly to learn whether it should exist at all.  The goal isn't simply to produce more code. It's to explore more possibilities, gather evidence faster and make better product decisions.

    When To Use AI In Product Engineering

  6. Sep 15

    Giving Product Engineers Autonomy In Decision Making

    How do you give engineers the freedom to make product decisions without expecting them to become product managers? And how can product managers make technical decisions without needing to become engineers? In this episode of The Product Engineering Podcast, James explores how effective Product Engineering teams separate context from authority. The people with the most expertise shouldn’t necessarily make every decision. Instead, their job is often to transfer the context needed by the people who are actually accountable for the outcome.  We cover:  Why autonomy is essential if teams are going to be accountable for outcomes  The difference between asking for context and asking for approval Why relying on domain experts to make every decision creates bottlenecks  How engineers can be empowered to make product decisions  Why product managers sometimes need authority to make technical trade-offs  Using build-vs-buy decisions as an example of shared technical context  How micromanagement slows teams down and often produces worse results  The danger of the HIPPO — the Highest Paid Person’s Opinion  Why experts should educate teams without automatically taking authority away from them  Where autonomy needs clear guardrails, particularly around areas such as security  How faster decision-making creates faster experimentation, learning and iteration The central idea is simple: move context, not authority. Create mechanisms that allow expertise and organisational knowledge to flow towards the people making decisions, while keeping those decisions with the people who will ultimately be accountable for the result.  Short YouTube Description How do you create autonomous Product Engineering teams without expecting everyone to become an expert in everything? In this episode, we look at why organisations should move context, not authority — giving teams access to the expertise they need while keeping decision-making with the people accountable for the outcome. We cover engineering vs product decisions, asking for context instead of approval, the dangers of HIPPO decision-making, and where teams still need clear guardrails.

    Giving Product Engineers Autonomy In Decision Making

  7. Sep 8

    Accountability For Product Engineering Teams

    How should we hold Product Engineering teams accountable when different kinds of work require completely different ways of working? In this episode, James explores why accountability needs to match the work being done, rather than being applied uniformly to an engineering team. For well-understood, roadmap-driven work, success might mean delivering an agreed scope by an agreed date. But when a team is working towards an outcome — improving adoption, reducing churn or increasing system performance — measuring them by features shipped or delivery velocity can actively work against the learning required to achieve that outcome. James covers: Why accountability is essential for both teams and the organisations investing in themThe difference between Roadmap-Driven Engineering and Outcome-Driven Engineering accountabilityWhy delivery metrics can be harmful when applied to outcome-driven workHow technical initiatives such as improving API performance can require experimentation and learningA real-world example of one team simultaneously handling predictable document-generation work and uncertain product-adoption workWhy accountability models should be attached to pieces of work, not teamsWhy an unsuccessful result should usually lead to learning and improvement rather than blameHow regular, predictable reviews give teams clarity about what they will actually be judged onThe central idea: you cannot ask a team to optimise for an outcome and then hold them accountable for output. Different work requires different definitions of success — and teams need to know those definitions before the work begins. Find out more about The Product Engineering Podcast at doproductengineering.com. Feedback or questions? Email james@doproductengineering.com.

    Accountability For Product Engineering Teams

  8. Sep 1

    Outcome Driven Engineering Explained

    What changes when you stop giving an engineering team features to deliver and instead give them an outcome to achieve? In this episode of The Product Engineering Podcast, James explores Outcome-Driven Engineering: an operating model where product managers and engineers share accountability for creating measurable product impact. Rather than starting with a predetermined solution, an outcome-driven team starts with the change it wants to see in customer behaviour or the product. The team then works together to understand the problem, decide how success will be measured, experiment with possible interventions and learn from the results. In this episode What an outcome actually means in Product EngineeringThe difference between delivering a feature and delivering an outcomeExamples including basket abandonment, product engagement and feature adoptionWhy Product Engineering is a cross-functional team model, not simply a job titleHow product managers and engineers retain different skills while sharing the same measure of successWhy individual engineering metrics shouldn't replace team-level outcome accountabilityHow an outcome-driven team moves from an outcome to measurement, experimentation and deliveryWhy you should decide how you'll measure success before you buildUsing prototypes and small interventions to test ideas quicklyHow learning can cause a team to refine, change or even abandon its original outcomeWhy shipping software is only part of the job — the real question is whether anything changedThe central idea is simple: instead of asking a Product Engineering team “What did you ship?”, ask them “Did you drive the outcome?” Next episode: Accountability — how to create an accountability structure that matches the kind of work a Product Engineering team is doing. Find out more at doproductengineering.com.

    Outcome Driven Engineering Explained

  9. Aug 25

    When Your Product Roadmap Works Against You

    In this episode of The Product Engineering Podcast, James explores The Roadmap Shield — what happens when the mechanisms designed to protect an engineering team from disruption also prevent it from responding to new information. Product roadmaps provide valuable stability. They protect teams from scope creep, create predictable timelines and allow the wider organisation to coordinate around upcoming work. But as commitments build around a proposed solution, changing direction can become increasingly difficult — even when the evidence says you should. James breaks the Roadmap Shield into four reinforcing layers: Committed scope — the agreed definition of what the team will buildCommitted estimates — the delivery forecast the team is expected to meetCommunicated timelines — dates that become promises to customers and stakeholdersDelivery pressure — the organisational and psychological pressure to complete what was promisedTogether, these layers can subtly change the question from “Is this still the right thing to build?” to “Are we successfully delivering the thing we committed to?” Using an example involving an AI integration and MCP, James shows how quickly a sensible technical decision can become outdated — and why teams often continue delivering the original solution anyway. The answer isn't to abandon roadmaps. Instead, teams need explicit exit ramps that allow credible new information to challenge existing commitments. That might mean working in smaller roadmap increments, identifying which deadlines genuinely cannot move, adding caveats to commitments where appropriate, and creating enough trust for a team to say: we've learned something important, so we're changing direction. The goal is to find the balance between predictability and adaptability — protecting teams from noise without protecting their solutions from evidence.

    When Your Product Roadmap Works Against You

  10. Aug 5

    The Product Equivalent Of Technical Debt

    Software teams are used to talking about technical debt, but products can accumulate another kind of debt too. Outcome debt builds up when features are shipped without confirming whether they achieved the result they were intended to produce. Over time, products become filled with decisions, assumptions and features that may once have made sense, but are no longer delivering enough value. In this episode, James explains how outcome debt compares with technical debt, why it does not necessarily mean that anyone made a bad decision, and how changes in customer behaviour, technology and the wider market can cause previously successful features to lose their effectiveness. The episode also explores how outcome measurement helps teams identify outcome debt, why sunk costs make weak features difficult to remove, and how teams can address technical debt and outcome debt together. In this episode What outcome debt is and how it accumulatesThe similarities between outcome debt and technical debtWhy good product decisions can become outdatedHow changing customer behaviour creates outcome debtWhy measuring outcomes reveals problems that delivery metrics missThe role of sunk-cost thinking in product decisionsHow to reassess older features using current evidenceCombining product improvements with technical refactoringHow leaders can create space to address outcome debt without assigning blameWhy established products must continually revisit earlier decisionsOutcome debt is not evidence that a team failed. It is a natural consequence of building products in a changing environment. The important question is whether teams regularly return to previous decisions, measure their continued impact and improve or remove the features that no longer serve the product. The Product Engineering Podcast explores how software teams can move beyond shipping features and take greater responsibility for measurable product outcomes.

    The Product Equivalent Of Technical Debt

  11. Jul 29

    Output Metrics vs Outcome Metrics

    Engineering teams have access to more delivery data than ever. Jira can measure cycle time and velocity. GitHub can track pull requests and code changes. Deployment tools can show how often software reaches production and how reliably those releases perform. These metrics are useful, but they only tell us how efficiently a team produces software. They do not tell us whether that software made the product better. In this episode of The Product Engineering Podcast, James explores the difference between output and outcome metrics, and why engineering leaders need both. The episode begins with the McNamara fallacy: the tendency to focus on what is easy to measure while overlooking information that may be more important. Engineering organisations can fall into the same trap when they treat delivery activity as the primary measure of success. James explains why output metrics work well for predictable work such as migrations and platform upgrades, where the intended destination is already understood. Product development is different. Teams are often working with uncertain assumptions about customers, behaviour and business impact. Efficient delivery cannot compensate for building the wrong thing. The episode also covers: The difference between output and outcome metricsWhy delivery metrics are easier to measure and attributeWhen metrics such as cycle time, deployment frequency and story points are usefulWhy product engineering cannot be managed like a manufacturing lineHow feature factories emerge when teams are rewarded mainly for shippingThe growing importance of outcome accountability as AI makes implementation fasterWhy teams should decide how they will measure success before delivery beginsThe difference between leading and lagging indicatorsHow outcome measurements can guide iteration and product decisionsJames shares an example involving two engineering teams. One completed a predictable React migration, while the other delivered a navigation redesign that users disliked. Both teams delivered their planned work successfully, but only one produced the intended result. The example demonstrates why delivery success and product success are not the same thing. The question engineering teams should ultimately be able to answer is simple: Did the product get better? Learn more about The Product Engineering Podcast at doproductengineering.com.

    Output Metrics vs Outcome Metrics

  12. Jul 21

    What Is Product Engineering?

    What does product engineering actually mean, and how is it different from traditional software delivery? In this first episode, James Charlesworth explores the shift from roadmap-driven engineering to an approach where engineers share responsibility for product outcomes, not just delivering requirements. Using an example from an email marketing platform, the episode compares two ways of working. In a roadmap-driven model, product managers define a feature and engineers are responsible for building it predictably. In a product engineering model, the whole team starts with the outcome they want to create, explores different solutions and uses evidence to decide what to build next. The episode also examines how product engineering changes the way teams think about prototypes, scalability, experimentation and technical quality. It explains why the first version of a feature does not always need to support the entire customer base, provided it is safe, measurable and capable of testing the underlying idea. James also discusses how AI is accelerating this shift by making it cheaper and faster to turn an idea into something that can be tested with real users. In this episode What product engineering meansRoadmap-driven engineering versus outcome-driven engineeringWhy engineers should help shape solutionsHow teams can test ideas before building for scaleWhy feature flags and limited releases matterWhere roadmap-driven engineering is still the right approachHow AI changes the economics of experimentationWhy delivering a feature is not the same as solving a problemThe central question is simple: after a team ships something significant, does anybody go back and find out whether it produced the intended result?

    What Is Product Engineering?
  • 12 Episodes

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The Product Engineering Podcast explores how software teams can move beyond shipping features and start delivering measurable outcomes. Hosted by engineering leader James Charlesworth, each episode covers practical ideas for product engineering, engineering leadership, team operating models and building products that create real impact.

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