A welcome to LeadDev London 2025 from the host, David Yee.

Candid insights into the biggest misconceptions about leadership and what truly makes a team exceptional.

Jason is a former developer and current developer advocate, widely renowned for creating fantastic and fun content that engineers “actually want to watch”. As a big believer in personal growth being the result of playful exploration, active community and sharing stories, this talk promises to be a compelling account of his journey towards becoming an effective leader – with plenty of lessons learned along the way.

We asked 1,000+ engineering leaders how their roles are changing in response to a rapidly changing economic and technological landscape. This talk will give you a sneak peek into the results of our Engineering Leadership report 2025.

The Google Domains acquisition wasn’t just any domain migration – it was the largest in the history of the domains industry. High stakes, massive complexity, tight timelines, and pressure from every direction. But here’s what made it truly remarkable: we didn’t lose a single person along the way. In this talk, you’ll learn about what it took to lead a project of this scale while keeping the team fully intact, engaged, and motivated. From tough calls to small wins that mattered, you’ll hear the real story of how we stayed aligned, avoided burnout, and came out stronger than we started.

GenAI is fundamentally transforming software development, but the narrative that it will replace engineers or solve all our problems is misleading. In this talk, we’ll explore how leaders should approach the “70% Problem” – how AI excels at routine coding tasks but still requires significant human expertise for the critical remaining 30% that makes the difference between functional code and excellent software. How teams approach the “30% that matters” will be vital moving forward.

Drawing from real-world case studies at companies like Google, GitHub, and Microsoft, I’ll share practical strategies for engineering leaders navigating this new landscape. We’ll examine how the role of technical leadership is evolving from hands-on problem-solving to strategic guidance and ethical oversight.

You’ll learn:

• How to establish effective “trust but verify” processes for AI-generated code
• Strategies to prevent skill erosion and maintain code quality
• Approaches for upskilling both junior and senior developers to thrive alongside AI
• Techniques for measuring impact beyond speed to ensure long-term code quality
• Frameworks for responsible AI governance and usage in your organization

Whether you’re skeptical or enthusiastic about AI coding tools, this talk will provide a balanced, pragmatic guide to leading effective engineering teams in an era where the question isn’t whether to use AI, but how to use it to build better software while keeping humans at the center of the creative process.

Discover how modern data infrastructure pipelines optimize supply chain operations by enabling real-time insights, improving data quality, and supporting advanced analytics for demand forecasting, inventory optimization, and smarter decision-making across logistics networks.

I architected a scalable data integration solution that transformed our supply chain analytics, enabling ML-powered predictions from multi-vendor data streams. Drawing from hands-on implementation experience, I’ll share critical insights on designing robust data pipelines for enterprise-scale operations – from handling complex edge cases to ensuring data quality at scale. Join me to discover practical strategies for building resilient data architectures that can drive significant revenue impact through enhanced logistics and supply chain operations.

Key takeaways

• Learn how data moves through modern supply chain pipelines – from ingestion to actionable insights.
• Gain insights into designing resilient, scalable data infrastructure that supports supply chain complexity.
• Explore how real-time analytics drive faster, smarter decisions in inventory, demand, and logistics management
• Discover methods to improve data accuracy and consistency across systems for better operational outcomes.

Drawing from recent research of 250m+ lines of code: three risks of AI-authored code, with an idea or policy to mitigate each.

As AI continues to reduce the proportion of code guided by human experience, what is lost? This talk examines industry-scale code line data (including repos from Google, Microsoft, Meta, and 1,000+ others) to enumerate three main areas where AI-authored code bends toward incubating tech debt.

While the benefits of AI accumulate in the daylight of well-funded industry reports, the maintainability challenges of AI fester in dark corners. To illuminate how AI-authored code differs from its human counterpart, we take five years of “code operation” trends, and combine it with newly-released AI usage APIs.

The story emerging from the data suggests that historic “best practices” (e.g., DRY code) have loosened. The magnitude of this change appears unprecedented. Effective policies can mitigate the downside risk. Engineering leaders can sharpen their intuition for how to build smart policies by leveraging recently available data.

Key takeaways

• The scale of code quality changes are measurable and unprecedented
• There is learnable consistency in how AI can poison code quality
• Even though we often read about profound increases in code velocity, the incentives of the companies building the LLMs remain undocumented and likely imperfect
• Higher velocity carries new costs: better to understand them than to spike adoption without regard for long-term detriment

AI adoption usually starts in engineering, but what happens when it proves so useful that the entire company wants in? That’s exactly what happened with Goose, our autonomous AI agent. Initially built as an internal developer tool, Goose helped our engineers become 20% more efficient, streamlining workflows and automating tedious tasks. But what we didn’t expect? Non-engineering teams across the company started using it too, finding creative ways to boost their own productivity.

As adoption spread organically, leadership took notice. Seeing the measurable impact, we pushed to expand Goose as a company-wide initiative.

In this talk, Angie Jones will share the journey of how an AI tool went from an engineering experiment to a business-wide enabler, the roadblocks we hit along the way, and the strategies we used to turn skeptics into superusers.

Whether you’re introducing AI at a small scale or looking to drive enterprise-wide adoption, this session will give you a playbook for making AI indispensable across your organization.

You have recently switched from being a staff engineer/tech lead on the team to a manager. This talk covers common challenges that you may encounter during this transition and provide tools to succeed.

When you switch from being an individual contributor to an engineering manager, it can be a very lonely job as your peer group shrinks and due to confidential nature of the challenges, new managers often struggle alone and deal with imposter syndrome. In this talk, we will cover common challenges encountered in first 90 days to six months and how to build a roadmap to success.

Key takeaways

• Understand differences in expectation of both roles and build your transition plan for success
• Tips to win trust with direct reports and how to provide effective feedback.
• List of books/resources/tips that can help first time managers.
• Identify common challenges and share best practices on how to overcome them.
• Tips to effectively manage upwards.

As AI accelerates coding and fuels hype to “automate everything,” the hidden costs of test automation are becoming painfully clear. This talk helps leaders evaluate ROI, avoid common pitfalls, and build smarter, more sustainable strategies.

Test automation is often pitched as the answer to faster releases, fewer bugs, and happier developers. But what’s left out of that pitch are the hidden costs: maintenance overhead, brittle frameworks, misaligned tooling, and automation that looks impressive but delivers little real value. Too often, leaders approve automation efforts without a clear strategy, or the wrong one altogether.

In this talk, I’ll break down what engineering leaders need to know before investing in test automation at scale. I’ll share stories from organizations where automation efforts failed quietly or spectacularly – and what we did to turn things around. We’ll look beyond tool choices to the organizational patterns, architectural decisions, and incentive structures that make or break automation ROI.

Whether you’re assessing a current framework or planning a new investment, this talk will help you ask better questions: What does success look like? Who owns it? Is the automation solving real problems, or just creating a false sense of coverage? You’ll walk away with a sharper understanding of how to evaluate automation strategically, so you can build confidence without burning your team or your budget. Automation can absolutely accelerate delivery – but only when it’s paired with clarity, alignment, and the right level of investment. This talk gives you the playbook to get there.

Key takeaways

• How to spot the hidden cotsts of poorly planned automation initiatives
• Key questions to ask before scaling or pivoting your automation strategy
• How to align automation goals with business and team maturity
• Common failure modes (and success signals) of test automation at scale
• How to avoid “automation theater” and focus on meaningful outcomes

Sponsored by

An overview of the leadership strategies employed by top AI-assisted engineering organizations.

To realize meaningful returns on AI investments, leadership must take accountability and ownership of establishing best practices, enabling engineers, and ensuring proper guardrails are in place. When prompting practice and reflexive AI use is driven from the top down, engineers can align on the highest value use cases and experience peak productivity gains.In this session, Justin Reock, Deputy CTO at DX, will explain what the most effective leaders of AI enabled engineering organizations are doing to drive satisfactory utilization, augmentation, and psychological safety across their teams. Based on interviews, use cases, and data, leaders will walk away with an understanding of how to best lead their teams through mature AI rollouts.

Key takeaways

• Measuring and improving AI adoption and efficacy
• Nurturing a psychologically safe AI engineering culture
• Driving adoption through grassroots initiatives and experimentation

Transform product-engineering tensions into strategic partnerships by addressing prioritization conflicts, technical debt advocacy, decision-making processes, and business alignment through practical collaboration frameworks.

The product-engineering partnership forms the backbone of technical delivery, yet remains one of the most challenging relationships to navigate effectively. When this alliance falters, the consequences cascade throughout the organization: delivery timelines slip, technical quality suffers, and team morale deteriorates. Development leads and engineering managers frequently encounter specific pain points: priorities that seem to change without technical consideration, insufficient investment in system health, opaque decision-making processes, and difficulty connecting daily work to strategic objectives.

Drawing from extensive experience spanning startups to enterprise organizations, this session offers battle-tested approaches that engineering leaders can implement immediately. You’ll learn how to establish mutually respected prioritization frameworks that balance business momentum with technical sustainability, create structured processes for engineering input during product planning stages, and develop shared metrics that unite rather than divide these critical functions.

Beyond process improvements, we’ll address the critical human elements of successful collaboration: building psychological safety when advocating for technical needs, establishing confidence-building communication patterns between teams, and creating the conditions where product and engineering leaders become genuine strategic partners rather than occasional collaborators. This practical session delivers concrete tools for transforming product-engineering dynamics from a source of organizational friction to a catalyst for delivery excellence. You’ll walk away with implementation frameworks you can adapt to your specific context, regardless of your organization’s size or maturity level.

Key takeaways

• Implement a balanced prioritization system that creates visibility for both technical health and product initiatives
• Establish engineering’s strategic voice through structured participation models in the product development lifecycle
• Create shared metrics and success definitions that align technical and business perspectives
• Master effective communication strategies for technical advocacy across organizational boundaries

Promoted to Director during a company downsizing, I navigated leading managers and teams with clashing dynamics. This talk explores leadership growth, balancing process and discomfort, and driving outcomes in challenging times.

Being promoted during a downsizing can be a jarring experience when your role grows into suddenly managing managers. This talk explores the unique challenges of growing into your new role and trying to level-up an entire department in the wake of a significant reorganization.

Key takeaways

• How to navigate a sudden promotion during a down-sizing when morale is low and acknowledging how strange that can feel
• Navigating leading managers with clashing dynamics as the organization adopts new processes
• How to push a team into healthy levels of discomfort to facilitate growth, even admist all the turmoil
• Growing as a leader of leaders during tumolt is hard and you can’t rush change (the first mistake I made)
• The unique experience of managing the same person in different roles (IC -> manager) and the wildly different experience it was
• Aftermath of a challenging decision to move a manager back into IC and how I wish I’d acted sooner

Closing thoughts from the hosts.

Explore solutions for your technical issues.

Learn how real engineering teams turn ideas into practice.

As described in Accelerate: The Science of Lean Software and DevOps, organisations adopting principles and practices from the Lean Software and DevOps movements are seeing payoffs in terms of higher quality, better reliability, faster delivery, and reduced costs.

Leaders wanting to achieve these results will need to understand how to build a transparent, aligned metrics-driven team in order to successfully navigate their improvement journey.

Using material from Accelerate, Jez Humble will discuss:

• Important concepts in measurement
• Known predictors of success that any team can use
• A tool to apply them

As engineering leaders, we make big decisions every day—about people, projects, and our own careers. But when the stakes are high, it’s easy to get stuck, overthink, or second-guess ourselves.

This workshop offers a fresh approach: treat decisions like A/B tests. Instead of waiting for perfect clarity, you’ll learn how to break big choices into smaller experiments so you can move forward with confidence.

Whether you’re deciding how to lead your team, handle a tough situation, or plan your next move—you’ll walk away with a repeatable, low-pressure way to make decisions that feel aligned and proactive (not stressful or rushed).

This session is interactive, practical, and built for real-world leadership. Let’s take the pressure off decision-making—and make it something you can actually feel good at.

In this workshop you’ll learn how to:

• How to turn big, overwhelming decisions into small, testable steps
• Simple tools to move forward—even when the “right” answer isn’t obvious
• Ways to apply product-thinking and feedback loops to your leadership style
• Ways to reduce overthinking and perfectionism when the stakes feel high

The integration of AI into engineering teams isn’t just changing how work gets done—it’s fundamentally shifting team dynamics, individual roles, and the emotional landscape of leadership.

In this interactive workshop, you’ll tackle the real challenges: creating clear agreements about AI use on your team, evolving your leadership approach in the age of AI, and identifying the skills your team needs to succeed in this new reality. You’ll leave with practical tools including a framework for building your team’s AI charter, strategies for navigating your evolving leadership role, and a clear approach for developing the human skills that become more valuable as AI handles routine work.

In this workshop you’ll learn how to:

• Create your team AI charter
  • Establish clear, shared agreements about how your team will use AI tools, what standards you’ll maintain, and where human judgment remains essential.
• Navigate your evolving role
  • Shift from managing tasks to developing capabilities, balancing team members at different AI adoption levels, and leading effectively in the new landscape.
• Develop the skills your team needs most
  • Identify and intentionally build the critical capabilities like complex problem framing, systems thinking, and cross-functional communication that become more valuable when AI handles routine work.

Sponsored by

How do we move beyond tracking surface-level AI metrics to truly understanding its impact on engineering performance?
High adoption rates and code completion percentages may look impressive on a dashboard—but do they really translate into better business outcomes?

In this immersive, hands-on workshop, you’ll go past the hype and into the reality of AI-assisted engineering. Blending cutting-edge research with practical tools you can use the very next day, the session is built for engineering leaders and DevProd teams tasked with measuring and maximizing the value of AI tools in engineering.

Through interactive exercises and simulated scenarios, you’ll apply the AI Measurement Framework to capture the full picture of AI’s effect on developer experience, team effectiveness, and business results. Participants will leave with concrete, actionable techniques: specific metrics to rethink, questions to ask their teams, and a framework for making smarter AI tool investments that truly drive developer productivity and organizational performance.

A welcome to LeadDev London 2025 from the host, David Yee.

Drawing on his experience working on internal engineering projects and with large clients, Dilip Krishna will help identify the best opportunities for AI to make an impact on your business beyond code generation and the implications this has on the way we value engineering time.

Prioritizing predictability over speed leads to faster, more sustainable software development. Learn how stable workflows, better planning, and reliable systems drive long-term efficiency and team success.

In the relentless pursuit of speed, many software teams sacrifice predictability – leading to brittle systems, frustrated developers, and tighter delivery timelines. But what if prioritizing predictability actually leads to faster outcomes in the long run?

In this talk, we’ll explore why predictability is a fundamental pillar of effective software development. We’ll discuss how predictable systems reduce cognitive load, enhance collaboration, and improve decision-making. Using real-world examples from engineering teams, we’ll contrast the hidden costs of “move fast and break things” with the long-term benefits of steady, reliable progress.

Attendees will leave with actionable insights on fostering predictability through better processes, tooling, and team culture – ultimately enabling them to deliver software that’s both stable and sustainably fast.

Key takeaways

• Why predictability leads to more efficient development cycles
• How to implement strategies for making teams and systems more predictable
• Real-world examples of teams that have benefited from prioritizing predictability

Not everyone on your team is chasing a promotion, and that’s okay. This talk gives you a practical framework for spotting what kind of growth each person needs and how to help them get there.

Every team is made up of individuals with different motivations, strengths, and growth trajectories. Some are hungry for the next big challenge. Others are steady, consistent, and quietly keeping things running. And then there are the folks who might be stuck or even holding your team back.

In this 25-minute talk, we’ll walk through a simple but powerful framework to help you identify who’s ready for more, who needs a different kind of challenge, and who may need direct support to improve. We’ll explore the differences between rock stars and superstars, how to recognize medium and low performers, and how to tailor your development conversations accordingly.

And because we don’t all have an unlimited budget for promotions, raises, or new roles, we’ll also cover creative, cost-effective ways to offer meaningful growth opportunities – like stretch assignments, mentorship, and skill-building projects—that help your team grow without breaking the budget.

You’ll leave with a clear lens to evaluate performance beyond just outputs and learn how to create growth plans that actually stick.

Key takeaways

• A framework for identifying performance and growth profiles across your team
• How to differentiate between rock stars and superstars (and why that matters)
• Approaches to keep steady contributors engaged without burning them out
• Tactics for coaching low and medium performers toward improvement
• Why personalized development is the key to long-term team health and retention

We’ve all worked at that place where the air felt thick. Or so-and-so wouldn’t talk to so-and-so. How does that environment come to be, and how do we influence our organization to embrace a culture of feedback?

Unspoken tension. Silent resentment. That decision you let slide, even though it was the wrong one. These aren’t just interpersonal hiccups—they’re conflict debt, and over time, they accrue interest in the form of bottlenecks, misalignment, and team dysfunction.

In this talk, I’ll make the case for addressing conflict early and often – not as a side skill, but as a core leadership competency. Drawing from Liane Davey’s concept of conflict debt and years of firsthand engineering leadership experience, I’ll explore how avoiding hard conversations quietly erodes team trust and productivity. More importantly, I’ll offer frameworks and practical language for navigating conflict directly, constructively, and without fear.

If you’ve ever walked out of a meeting thinking, “We didn’t really agree on that,” or let silence pass as consensus – this talk is for you.

Key takeaways

• What is conflict debt, and how does it show up in technical and cross-functional teams?
• How does unresolved conflict silently slow down delivery, trust, and morale over time?
• What stops us from addressing conflict directly—and how can we move through that discomfort?
• How can we create a culture that normalizes healthy conflict without descending into dysfunction?

As AI use grows, so does the need for transparency and interpretability to mitigate bias, racism, and discrimination. This talk covers building trustworthy, human-oriented AI.

The need for interpretable artificial intelligence systems grows along with the prevalence of artificial intelligence applications in everyday life. Despite the successes, these systems have their limitations and drawbacks. The most significant ones are the lack of transparency and interpretability behind their behaviors, which leaves users with little understanding of how particular decisions are made by these models. Every day, new cases of bias, racism, and discrimination are reported by users of these systems. In this talk, I discuss bias in AI, how to mitigate them to build trustworthy AI products using a human-oriented and critical approach.

Key takeaways

• Importance of Transparency: Understand the critical need for transparent AI systems to build user trust and ensure accountability in decision-making processes.
• Mitigating Bias: Learn strategies to identify and mitigate bias in AI models to prevent discrimination and promote fairness.
• Human-Centered Approach: Emphasize the importance of a human-oriented approach in AI development, ensuring that ethical considerations are prioritized.
• Interdisciplinary Collaboration: Recognize the role of various tech professionals, including software engineers, data engineers, and data scientists, in creating ethical AI.
• Real-World Impact: Reflect on the real-world implications of AI products and the responsibility of tech professionals to create systems that positively impact society.

Building AI products sounds exciting, until reality hits. I’ll share honest lessons from a year of scaling AI at a FinTech startup: what broke, what burned, and what we would absolutely do differently next time.

When I set out to build an AI engineering org at GoodLeap, a high-growth FinTech startup, I thought it would be straightforward: hire smart engineers, pick good models, set clear goals. I couldn’t have been more wrong. Turns out, AI isn’t just another layer you slap onto a tech stack. It changes everything: how you hire, how you build, and how you operationalize and support production systems. If you underestimate those shifts (we did), you’ll pay for it later (we did).

This talk is a field guide to the real, messy work of building AI systems in a fast-growing company. I’ll share:

• Hiring AI talent: How hiring brilliant ML specialists without strong engineering instincts led to major gaps, and what an ideal hiring framework should have looked like. You’ll leave with practical frameworks you can use to hire AI engineers who can build and own systems, not just models.
• Operationalizing Proofs of Concept (POCs): How launching a promising POC without real production planning caused model drift, hallucinations, and a lot of late-night firefighting. You’ll learn how to operationalize POCs the right way, and when to say “not yet” to production.
• Integrating AI as infrastructure: Why treating AI like a “feature” instead of an infrastructure change set us back, and what engineering leaders should do instead. This includes how to prep your engineering org for AI ops challenges before they hit.
• Driving AI adoption: How our best AI tech demos failed to win buy-in until we embedded AI inside real product teams (and stopped trying to “sell” AI separately). You’ll discover how to create AI adoption by solving real user and business problems first.

If you’re leading teams through AI adoption or thinking about it, this is the talk I wish someone had given me a year ago.

Key takeaways

• AI initiatives need different hiring strategies: Focus on systems builders over pure researchers.
• Moving POCs into production demands discipline, not just technical optimism.
• Operational resilience matters more than flashy model performance.
• Winning business buy-in requires embedding AI inside real workflows, not running it as a parallel innovation track.
• Mistakes are inevitable, but they can become the engine for faster, more sustainable AI scaling if you learn deliberately.

An inter-generational (mother-daughter) conversation about the history of repeating trends in the tech and software industry and how they have surfaced once more in our modern AI climate.

“AI will mean no one needs software engineers anymore!”

“We can just write the spec and the LLM will do all the coding!”

“What happens if we hire software engineers who turn out not to be able to code?”

We’ve all heard versions of these statements (and others) as AI trends seize the industry. What many of us don’t know is that none of these are actually new claims or new fears. As a child I was privileged to hear about trends, blind alleys, failures, and successes in the software industry at large from my mother, who worked as an engineer for 50 years (from the mid 1960s to the mid 2010s).

I’d like to invite the audience to join me for an interview with my mother about our industry’s history. We’ll discuss past trends, including COBOL and “4GL” languages, which were supposed to replace the need for skilled engineers; the industry’s earlier forays into offshoring software implementation; and some historical struggles to hire good engineers. Learning from our history can give us all significant insight into likely outcomes of current trends, and point us to the most productive ways to use new technology without getting lost in hype. Even if it’s different this time, our history can show us the traps and pitfalls to avoid in a brave new world.

Key takeaways

• The software industry has, over and over, insisted that this new thing will mean we “don’t need programmers/SWEs”
• This promise has never materialized, because “writing code” is not the hard part of software engineering
• Understanding the history of the industry is incredibly valuable for predicting its future

Sooner or later, tech debt accumulates. Much like financial debt, it’s not universally a bad thing. After all, sometimes we need to build something fast to test & learn, or meet an external deadline, or have to accept something imperfect in order to make progress. But the problem comes when you start paying your mortgage on your credit card.

In this talk, join a seasoned CTO talking through their real-life experiences of categorizing, understanding, evangelizing about, and tackling tech debt across a range of company types and sectors.

You don’t need to be a technical expert to be a great manager – learn how to lead, support, and challenge teams working across unfamiliar tech stacks.

Drawing from experience as a former iOS engineer turned multi-stack engineering manager, the speaker explores how strong leadership isn’t about knowing every line of code but about creating an environment where engineers can thrive. Attendees will learn how to build trust and credibility without being the most technically knowledgeable person in the room – through better team processes, advocating for systems improvements, and enabling peer-driven growth.

The session also covers concrete tactics for supporting technical development: sourcing quality feedback, fostering mentorship structures, and knowing when to bring in outside expertise. With insights from both sides of the reporting line – as a manager and as an engineer reporting to non-experts – this talk highlights how leadership grounded in empowerment, not micromanagement, drives lasting team success.

Key takeaways

• How to quickly build technical trust without being a deep expert
• Creative strategies to support engineers’ growth beyond hands-on code reviews
• Why effective leadership means empowering, not micromanaging
• Lessons learned from managing – and being managed – outside the tech stack

Interviewing needs to evolve in an AI (enhanced) world. This talk explores what traditional interviews now miss, which skills matter more than ever, and how we can design pipelines that truly identify engineering potential.

In a world where AI can help candidates solve coding problems, craft “perfect” answers, and even simulate system design conversations, current interviewing practices are struggling to keep up. If your process is optimized for by-the-book answers, clever tricks, or perfect resumes, it’s likely measuring the wrong things.

In this talk, we’ll explore how AI is reshaping the landscape of technical interviewing and where it leaves critical gaps. We’ll discuss which timeless skills AI can’t replace, like debugging unexpected issues, evaluating trade-offs, or exploring ambiguous problems. We’ll look at why some traditional signals are now weaker signs of success, and share practical ideas for designing interviews that better surface the skills that matter to us.

Whether you’re responsible for hiring one engineer or scaling an entire organization, rethinking your interview process isn’t just important, it’s necessary. This talk will provide you and your team with frameworks and strategies to build a hiring process that’s resilient, fair, and aligned with the realities of 2025 and beyond.

Key takeaways

• Understand how AI tools are impacting traditional technical interviews.
• Identify critical engineering skills that remain and benefit of being human-driven.
• Learn practical strategies to design AI-resilient interview processes, being closer with real-world engineering challenges.
• Explore ideas to improve candidate experience and fairness in hiring.

Tired of inaccurate estimations that become commitments? Learn how we transitioned from spending endless hours providing (faulty) estimates to getting to probabilistic forecasting, backed by historical data and risk of failure.

Engineering leaders constantly battle inaccurate estimates that morph into rigid deadlines. We wasted countless hours trying to find the ‘perfect’ number, yet projects consistently missed targets. So we decided to try something radically different.

This talk shares our company’s transformative journey away from traditional estimation toward data-driven, probabilistic forecasting. I’ll present actual metrics and visualizations from our engineering organization that demonstrate how this shift dramatically improved our delivery predictability and stakeholder relationships. You’ll see the before-and-after comparisons, including charts contrasting our story point estimates against actual cycle times.

Our case studies reveal why focusing on individual task estimates failed to account for the inherent randomness and dependencies in our systems, factors that repeatedly derailed our carefully planned projects. I’ll share how we implemented Flow Metrics and Monte Carlo Simulations across our engineering teams, the resistance we encountered, and how we now spend 60% less time on estimation activities while delivering more reliable forecasts. Most importantly, you’ll learn how this approach empowered our Engineering Managers to communicate delivery risks effectively to stakeholders.

You will discover:

• Why traditional estimation methods fall short in unpredictable environments – with real data showing the gap between estimates and actuals
• Why we need to distinguish work analysis from estimation exercise
• Our three-tiered estimation approach based on planning horizons (annual roadmap, quarterly planning, sprint commitments)
• Simple techniques we use to visualize and communicate forecast confidence and risk
• Leave equipped with actionable methods to move beyond guesswork and answer ‘When will it be done?’ with data-backed insights and appropriate confidence levels.

Key takeaways

• See real data showing how our team’s estimates compared to actual cycle times and why this gap existed.
• Learn why historical data beats (guess)estimates
• Work analysis is not the same as a ballpark estimation
• Discover our practical three levels of forecasting for different planning horizons (annual roadmaps, quarterly planning, sprint commitments)
• Implement basic probabilistic forecasting techniques that engineering managers can use without specialized tools (AI can do this for you!)
• Learn practical methods, using your existing data, to predict delivery ranges and communicate forecast confidence.

Discover the power of becoming an “invisible” engineering manager – one whose team thrives independently, enabling you to step back from the weeds and focus on high-leverage work that drives growth for everyone.

In many engineering leadership roles, visibility is often mistaken for value. Managers are expected to be involved in everything: decisions, delivery, communication. But what if true impact comes from building teams that don’t need constant oversight?

In this talk, Chris Class explores the counterintuitive, high-trust approach of being an “invisible” engineering manager – someone who enables self-sufficient teams through systems, culture, and clarity rather than control. Instead of acting as the single point of decision-making, this leadership style focuses on becoming a multiplier: creating the space for teams to thrive while focusing on the bigger picture.

Drawing from real-world experience transitioning from hands-on involvement to sustainable autonomy, Chris shares how engineers can be empowered to lead initiatives, solve problems, and make decisions independently. The session will cover how to establish shared context, build psychological safety, and foster decision-making confidence.

It will also explore how this shift creates space for managers to grow and contribute more broadly across the organization. Ideal for leaders looking to scale their impact without becoming a bottleneck, this talk makes the case that the most effective leaders aren’t always front and center.

Key takeaways

• How to create the conditions for team autonomy without stepping away entirely
• Techniques for reducing team dependency while increasing alignment and trust
• The hidden cost of being too hands-on, and how to break the cycle
• Why letting go is essential for your team’s growth – and your own
• Frameworks to help your team make decisions confidently without you

The tech industry is in a period of workforce downsizing. Companies are hiring fewer and laying off more, compared with one decade ago. For many workers, it is harder than ever to “vote with your feet” and seek out a new role with better compensation, growth opportunities, and values alignment. Engineering leaders are facing the challenge of delivering more business impact while offering their teams less than ever before.

This talk is about the mechanics of leading through times of uncertainty and chaos by strategically building and spending organizational capital. I’ll share hard-won lessons learned while managing in different types of organizations, and discuss how leaders who care about their teams need to reorient their mental models for an era of surviving, not thriving.

Key takeaways

• Learn how to navigate periods of widespread uncertainty, at the organizational or industry level
• Understand common anti-patterns and mistakes that managers make in low-autonomy environments
• Gain tools to engage ethically with organizational systems, in service of more authentic leadership

Closing thoughts from the hosts.

Explore solutions for your technical issues.

Learn how real engineering teams turn ideas into practice.

As described in Accelerate: The Science of Lean Software and DevOps, organisations adopting principles and practices from the Lean Software and DevOps movements are seeing payoffs in terms of higher quality, better reliability, faster delivery, and reduced costs.

Leaders wanting to achieve these results will need to understand how to build a transparent, aligned metrics-driven team in order to successfully navigate their improvement journey.

Using material from Accelerate, Jez Humble will discuss:

• Important concepts in measurement
• Known predictors of success that any team can use
• A tool to apply them

As engineering leaders, we make big decisions every day—about people, projects, and our own careers. But when the stakes are high, it’s easy to get stuck, overthink, or second-guess ourselves.

This workshop offers a fresh approach: treat decisions like A/B tests. Instead of waiting for perfect clarity, you’ll learn how to break big choices into smaller experiments so you can move forward with confidence.

Whether you’re deciding how to lead your team, handle a tough situation, or plan your next move—you’ll walk away with a repeatable, low-pressure way to make decisions that feel aligned and proactive (not stressful or rushed).

This session is interactive, practical, and built for real-world leadership. Let’s take the pressure off decision-making—and make it something you can actually feel good at.

In this workshop you’ll learn how to:

• How to turn big, overwhelming decisions into small, testable steps
• Simple tools to move forward—even when the “right” answer isn’t obvious
• Ways to apply product-thinking and feedback loops to your leadership style
• Ways to reduce overthinking and perfectionism when the stakes feel high

The integration of AI into engineering teams isn’t just changing how work gets done—it’s fundamentally shifting team dynamics, individual roles, and the emotional landscape of leadership.

In this interactive workshop, you’ll tackle the real challenges: creating clear agreements about AI use on your team, evolving your leadership approach in the age of AI, and identifying the skills your team needs to succeed in this new reality. You’ll leave with practical tools including a framework for building your team’s AI charter, strategies for navigating your evolving leadership role, and a clear approach for developing the human skills that become more valuable as AI handles routine work.

In this workshop you’ll learn how to:

• Create your team AI charter
  • Establish clear, shared agreements about how your team will use AI tools, what standards you’ll maintain, and where human judgment remains essential.
• Navigate your evolving role
  • Shift from managing tasks to developing capabilities, balancing team members at different AI adoption levels, and leading effectively in the new landscape.
• Develop the skills your team needs most
  • Identify and intentionally build the critical capabilities like complex problem framing, systems thinking, and cross-functional communication that become more valuable when AI handles routine work.