What does “innovation” really mean, and what are the conditions that enable it? The book “Collective Genius” highlights that Leaders of innovation need to make sure they collaborate, engage in discovery-driven learning, and make integrative decisions. Additionally, embracing “paradoxes” in every situation will help you approach problems with a “both/and” mentality. Learn from our struggle in Games Engineering to create this environment and how we embraced these tensions to unlock innovation for Netflix Games.

In the GenAI era, we’re outsourcing more than tasks – we’re outsourcing trust. This talk explores how to lead creatively and confidently when everything starts with a prompt.

This abstract has been created without relying on any GenAI tool. This was hard for me, I’m not a story teller, not a writer, I barely had conviction in what I had to say before GenAI came along. It’s been an incredible tool for me and countless others, It feels like my productivity shot through the roof, I had access to this wealth of knowledge, I felt like I had a voice – a seat on the table.

Over the last year, I’ve felt this voice get quieter.

In chasing speed, polish, accuracy, I’ve started to default to the tool – sometimes even before I’ve formed my own thought. “Hold on, let me prompt that”. For many of us, it’s become a way to delay decision-making, outsource confidence, and avoid the discomfort of trusting our own thinking. This talk is about exploring that tension: between the incredible power GenAI gives us, and the quiet erosion of the narrative conviction it can cause. For Engineering Leaders, this isn’t theoretical. We lead people. We make bets. We communicate vision. And when our sense of authority is blurred—by too many prompts, too much polish, or too much second-guessing – we risk leading from pre-defined templates instead of truth.

This talk invites us to take a pause, to sit with that moment where the answers aren’t clear yet, that thought isn’t fully refined yet. In a world where everyone has this tool, our advantage as engineering leaders is still the experience and perspectives we bring as individuals.

Key takeaways

• Recognize early signs of Gen-AI induced confidence erosion
• Rebuild your narrative and inner-clarity to enable high-stakes decisions as an Engineering Leader
• Discern when to prompt, and when to lead from conviction
• Apply simple techniques to model original thought and independent judgment

As leaders we have been tasked to go all-out with AI: both in terms of our products and also our productivity. This talk outlines what you should be thinking of doing for your team, your product, and, importantly, yourself with AI.

The hype surrounding Large Language Models (LLMs) has reached a fever pitch, making it challenging for technology leaders to discern actionable strategies from speculative promises. This talk cuts through the noise, offering a pragmatic operator’s perspective on how to genuinely leverage LLMs within your organisation.

We’ll cover specific, real-world applications for boosting your engineering team’s output, from automating mundane tasks to enhancing code quality and fostering rapid prototyping. You’ll learn how to identify high-impact product features powered by LLMs that deliver quick wins, along with practical considerations for data privacy, model selection, and deployment.

Crucially, we’ll discuss how you, as a leader, can personally leverage AI for better decision-making, thinking and strategic planning, ensuring you leave with concrete steps to implement and see immediate value from AI across your team, product, and leadership approach.

Key takeaways

• Develop a clear, actionable AI strategy for your team: Learn how to assess the readiness of your engineering function for LLM adoption and identify practical tools and workflows to boost productivity and innovation without overwhelming your team.
• Identify impactful LLM applications for your product: Understand methodologies for pinpointing genuine problems LLMs can solve within your product roadmap, moving beyond surface-level integrations to create meaningful user value.
• Navigate the personal leadership shift with AI: Gain insights into how your role as a technology leader evolves with the rise of AI, including strategies for continuous learning, ethical considerations, and fostering a culture of responsible AI experimentation.

Discover how KLM Royal Dutch Airlines transformed regulatory compliance using cloud platforms and Generative AI, reducing cycle times from weeks to hours, enhancing accuracy, and freeing up resources through automated data extraction and cloud workflows.

In the highly regulated aviation industry, KLM Royal Dutch Airlines faces the ongoing challenge of staying compliant with constantly evolving regulations. Traditionally, this process was manual, time-consuming, and prone to human error, with cycle times stretching from weeks to even months. The complexity of parsing regulatory documents, delivered in various formats, further compounded these challenges. The high stakes, including potential non-compliance costs of up to $15 million per incident, underscored the need for an efficient solution.

KLM embarked on a transformative journey by leveraging the cloud platforms, integrated with advanced Generative AI functionalities, to automate and streamline their compliance processes. By replacing complex code-based ETL pipelines with cloud native workflows, KLM was able to automate data extraction and interpretation from diverse regulatory texts. This approach not only standardized processes but also drastically reduced manual effort.

A key component of this transformation was the use of Generative AI models to interpret regulatory texts and extract critical dates, such as when regulations were passed and when they come into force. To ensure accuracy and prevent AI “hallucinations,” outputs were cross-referenced with verified sources and subject matter experts.

The results were remarkable. KLM freed up the equivalent of three full-time employees previously dedicated to manual tasks, reduced compliance update cycle times from weeks to hours, and saved four man-hours per compliance review. Additionally, the process became significantly more accurate, enhancing internal trust and satisfaction.

Join us to explore how KLM’s innovative use of cloud platforms and Generative AI not only improved compliance efficiency but also mitigated risks and reduced costs. This session will provide insights into the practical implementation of AI-driven automation in compliance and its transformative impact on organizational processes.

Key takeaways

• Efficiency Gains: Learn how Generative AI reduced KLM’s compliance cycle times from weeks to hours, significantly boosting operational efficiency.
• Resource Optimization: Discover how automation freed up three full-time employees, allowing KLM to reallocate resources to more strategic tasks.
• Accuracy Enhancement: Understand the role of AI in improving data accuracy and minimizing human error in regulatory compliance processes.
• Risk Reduction: Explore how faster compliance updates helped KLM mitigate the risks and costs associated with non-compliance.

Growth isn’t always vertical, and forcing people up the ladder often pushes them out. This talk challenges the frameworks we use to define success and shows how to build teams that grow without leaving.

We’ve convinced ourselves that growth = promotion. That career success means moving “up.” That to retain people, we must level them – whether they want to or not.

But what if someone doesn’t want to climb? What if their best work, their most fulfilled self, exists in a role without a title change?
In this lightning talk, I’ll expose the growth frameworks we use (and often weaponize):

• The Career Ladder = deeply hierarchical → Promotions-as-growth, limited mobility → can’t be redeemed Burn it
• The Dual Track Framework = rigid → Hard to switch, unequal prestige → good if we Redesign it
• Competency Matrix = oversimplifies → Used as gatekeeping, checklisted → Reclaim it with care

These systems were built for order, not for humans. And they often punish the curious, the multi-passionate, and the quiet high performers.
I’ll share real stories of team members who thrived only after we ditched the “climb” mindset, and started treating careers like something to explore, not just escalate.

You’ll walk away with permission to rethink what growth really looks like, and how to build systems that support it without forcing everyone up.

Key takeaways

• Not all growth is vertical, and that’s okay.
• Promotion isn’t always retention.
• Competency frameworks often punish real potential.
• You can support growth without offering a new title.
• Great teams make space for exploration, not just elevation.

Developing a clear target architecture will take your technical leadership to the next level by improving collaboration, increasing efficiency, speeding up execution and allowing you to make data-driven decisions while building to scale.

Can you imagine if construction workers built a skyscraper using notes scribbled on random bits of paper? Imagine how they would coordinate the plumbing, electricity, floor plans…Yikes! Yet, this is sadly what most tech systems look like – patched up to “make things work”, always fixing “the previous person’s code” and constantly bumping into chunks of code which make absolutely no sense but you’re too scared to remove because you’re pretty sure it does something important somehow.

As the founding engineer of the Cash Flow domain in Pleo, I was privileged enough to play a key role in architecting a whole subsection of the company’s backend ecosystem while building up a team that would take it to new heights. In this talk, we’ll walk through this case study which covers the wins and fails of our journey and ultimately highlights the importance of having a target architecture and how it has helped inform decision-making (technical and non-technical), increase efficiency, improve collaboration and speed up execution while building to scale.

Key takeaways

• What is a target architecture diagram and how to create it for different stakeholders
• How target architecture informs decision-making (technical and non-technical), increases efficiency and speeds up execution
• How to grow domains and teams based on target architecture

If you think managing managers is just a bigger version of managing individual contributors, think again.

The strategies that launched your engineering career and got you into leadership won’t carry you to the next level. This talk challenges your assumptions and reveals what it truly takes to shift from doing it yourself to enabling others to do it better.

How I used AI tools to accelerate onboarding, understand team dynamics faster, and show up more confidently as a new engineering manager without drowning in docs or meetings.

Starting a new role as an engineering manager can be overwhelming. You’re expected to absorb technical context, understand team dynamics, and start delivering impact all within the first few months. In my most recent onboarding experience, I decided to do something different: I let AI help.

In this talk, I’ll share how I used AI tools to streamline my onboarding as a new manager. These tools didn’t just save time they helped me surface insights I might have missed, get clarity faster, and reduce the pressure of “figuring everything out” on my own.

I’ll walk through real examples of how I used AI to synthesize org charts and product documentation, explain complex systems, interpret unfamiliar code reviews and engineering standards, and improve team workflows. I’ll share how I mapped out my new engineers’ roadmap, identified gaps in our processes, and even prototyped onboarding plans all with AI’s support. I’ll also talk about what didn’t work, where human judgment was essential, and how I balanced automation with building authentic, trust-based relationships.

This isn’t about hype it’s about how AI can be a practical partner in the messy, high-context reality of engineering leadership. Whether you’re a new manager, hiring one, or just curious how AI is changing the way we work, you’ll walk away with practical ideas and honest reflections from the front lines.

Key takeaways

• How to use AI tools to accelerate understanding of org context and culture
• Pitfalls to avoid when over-relying on automation
• How AI can reduce early-stage overwhelm for new managers

A practical playbook for leading engineering teams through 2025’s budget pressures; aligning technical decisions with business goals, empowering teams, and driving impact despite constrained resources.

In 2025, engineering leaders are under pressure from all directions. Customers expect faster feature delivery and tools that deliver even greater value. Product owners and the C-suite push for innovation and speed. Meanwhile the CFO demands all this, but without increasing spend. Budgets are tighter, hiring is slower, and AI is reshaping how teams operate. And yet, expectations keep rising. This talk is a practical playbook for navigating that tension.

At StatusCake, a bootstrapped SaaS company, we’ve never had the luxury of excess. We’ve operated for over a decade with lean teams, constrained budgets, and no external funding. That’s taught us how to align teams around impact, prioritise ruthlessly, and make technical decisions that directly support the business; all without burning people out or compromising on quality.

I’m not an engineer myself, but I’ve led engineering teams for 15 years. That perspective has forced me to build trust through clarity, not code. And to translate between product, engineering, and commercial strategy with precision. In this session, I’ll share the practical lessons that emerged from leading through constant constraint, and how they apply to the challenges engineering leaders now face at scale.

This is not a just a story of surviving 2025, but it should help you create a framework for a thriving engineering team within your organisation. Helping you and your team stay focused, motivated, and strategic when resources are scarce but expectations are high.

Key takeaways

By the end of the session I hope you’ll have learned 5 things to take back to your businesses:

1. Why budget constraints are a leadership challenge, not just a financial one: Because leading through scarcity is about alignment, not spreadsheets.
2. How to empower engineers to think commercially and make smarter technical trade-offs: When engineers understand the “why,” they make better calls on the “how.”
3. Ways to align tech decisions with company-wide goals during moments of pressure: Strategy fails when tech and business drift out of sync; especially under stress.
4. How constraints can sharpen focus and increase team autonomy: Limits don’t have to reduce creativity, they can accelerate it.
5. How to reshape the role of engineering from a cost centre to a strategic driver of value: Engineering isn’t just delivery; it’s a key lever for business performance.

Think you’re stuck in your career?

You’re not.

You’re just following the wrong rules.

This talk will show you how to get noticed, grow your influence, and finally get the recognition you deserve.

Are you working hard, hitting your goals, and still not getting the recognition or growth you deserve?
You’re not alone – and you’re not broken.
You’re just playing by rules that were never designed for you.

In this talk, I’ll share the unspoken truths about career growth in tech, especially for women, introverts, and hidden leaders who feel like they’re doing everything “right” but still getting overlooked.

From my experience as a Director of Engineering and now an executive coach for women in tech, I’ll walk you through what actually moves the needle – how to build visibility, influence decisions, and shape your path without selling your soul or burning out.

You’ll get a practical and honest framework for driving your career forward on your terms – and stop waiting for permission to lead.

This isn’t another “just be confident” pep talk.
It’s a real conversation about what works, what doesn’t, and how you can start leading now – even if no one handed you the title yet.

A light, practical talk for engineering leaders on tackling self-doubt using a debugging mindset. Learn how to build real confidence with a simple framework that improves not only you, but also your team.

As engineering leaders, we’re expected to have answers, make decisions, and support our teams – even when we’re quietly questioning ourselves. Speaking up in high-stakes meetings, giving feedback, or navigating ambiguity can trigger self-doubt and quietly erode confidence over time.

In this talk, Natalia shares how imposter syndrome shows up in leadership – and how to handle it using a mindset we already know well: debugging. With humor, personal stories, and practical tools, she introduces the Confidence Debugging Framework (CDF) – a 5-step approach to identifying confidence “crashes” isolating their causes, applying targeted fixes, and building habits that stick.

You’ll learn:

• How to recognize your own confidence blockers and triggers
• Simple, proven strategies to build confidence intentionally
• Why your confidence directly impacts your team’s trust, performance, and growth

Expect a refreshing, actionable take on a topic that’s often overlooked in tech leadership – but deeply impactful. From private self-doubt to public team dynamics, this talk will leave you with tools to lead with more clarity, confidence, and self-awareness.

Because just like code, confidence isn’t perfect – but with the right mindset, you can always refactor it.

Key takeaways

• Recognize how self-doubt shows up in everyday leadership.
• Use a simple debugging mindset to approach confidence challenges.
• Discover practical habits for building confidence over time.
• Reflect on how your confidence affects your team’s trust and dynamics.

The definition of insanity is doing the same thing over and over again” – this quote attributed to Einstein warns us of the danger of magical thinking, hoping that trying something just one more time will achieve success when before we failed.

In this talk, I’ll argue that retrying things actually does make a lot of sense, and is in fact key to improving the resilience of a distributed system. Along the way, I’ll explain the importance of timeouts, retry limits and knowing when giving up does make sense. I’ll also show how retries can be made safe (and help avoid draining your bank account), and perhaps we’ll get to examine that Einstein quote in a bit more detail…

Explore solutions for your technical issues.

Learn how real engineering teams turn ideas into practice.

You spend a lot of your workday thinking, but how much time do you spend rethinking? Failure to challenge our own assumptions can lead us to miss big opportunities. Sometimes, failure to rethink can even have deadly consequences.

Through real-world examples from NASA’s Challenger explosion to Blackberry’s failure to thrive, we’ll examine how thinking like a scientist can make us a better leader and improve team dynamic.

You’ll learn how to challenge your assumptions, encouraging you to actively seek out information that challenges your views.
You’ll learn how to inspire a learning mindset in our teams through a strong foundation of psychological safety and promoting constructive conflict.
And you’ll learn why we should replace a culture of “best practices” with a learning culture.

We’re quick to update our possessions. When our clothes are out of style, we go shopping. When our phones are old, we replace them. So why shouldn’t we update our opinions and assumptions? It’s time we stop listening to views that make us feel good and seek out views that make us think hard.

Key takeaways

• Frequently updating our own views makes us a stronger leader.
• Challenging others to open their minds and potentially change their views strengthens team bonds and allows for healthy discussions.
• Replacing “best practice” culture with a learning culture at work improves psychological safety and efficiency.

Learn how to perform large-scale migrations and rewrites of complex systems safely. An experienced engineering leader will share his learnings from leading teams that have successfully performed multiple migrations over the past few years.

Performing large-scale migrations, rewrites, and sunsetting systems in software engineering is inevitable. They are an organic part of continuously growing, improving, and evolving the systems we work on. Teams and organizations that master these tasks successfully have a competitive advantage when it comes to adapting to changes and innovation.

This talk will share hard-earned lessons and insights on how to successfully migrate key functionalities safely and without negatively impacting users. It will first explore common reasons why teams usually need to undertake these projects. Then, it will present and assess different execution strategies, focusing in particular on different techniques that enable having incremental progress instead of a big bang switchover. Finally, it will cover how to effectively measure and communicate the progress of the migration, as well as how to build trust with stakeholders.

Key techniques discussed in this talk will include:

• Learning how to break down your problem and set up an incremental delivery plan
• Partial rollouts that provide faster feedback loops and avoid risky releases
• Dual-read approaches, on-the-fly migrations, and hard cuts
• Traffic splitting through feature flags and load balancing

The speaker will share his firsthand experience and learnings after having migrated several complex systems during the last years. Attend this session to gain valuable knowledge on ensuring your migrations are both safe and successful.

Key takeaways

• Understand common triggers for large-scale migrations and rewrites
• Learn strategies for executing migrations successfully and understand their trade-offs
• Discover techniques for planning and implementing incremental rollouts
• Learn how to build trust and effectively communicate the progress of the migration to your stakeholders
• Explore methods for traffic splitting at both application and infrastructure levels

How can senior ICs and managers strategically guide junior engineers to develop critical engineering skills amid the rise of AI coding assistants?

In a landscape where generative AI and coding assistants are increasingly prevalent, how do we ensure that junior engineers develop the critical thinking and problem-solving skills necessary to progress to senior roles?

This talk explores how senior ICs can foster a learning environment that leverages AI tools without allowing them to become crutches. We will discuss frameworks for evaluating when AI can accelerate learning versus when it might obscure skill gaps, and share case studies on effective mentorship strategies in this new paradigm.

Attendees will leave with actionable insights on balancing AI-assisted workflows with traditional engineering skill development, ensuring that emerging engineers are prepared to thrive as senior ICs in the future.

Key takeaways

• Identify key skill areas that AI tools may obscure and strategies for reinforcing them.
• Implement mentorship frameworks that balance AI use with traditional problem-solving techniques.
• Develop actionable plans to assess junior engineers’ readiness for senior roles amid AI-driven workflows.

The gap between AI demo and AI product is full of hard engineering work. I’m sharing 10 years of experience involving scaling, orchestration and cost optimisation.

How much does it take to go from a shiny AI demo to a deployed application? Everyone’s excited about new AI capabilities, but few talk about the engineering work required to use ML models in production. Getting this hidden part right is what separates successful AI projects from failed ones. In my talk I’ll show what it takes to turn ML experiments into robust, scalable products.

At Chattermill we’ve spent 10 years bringing ML to production. I’ll share hard-won lessons on orchestration of a dozen self-hosted ML services. We’ve had to tame the unpredictability of LLMs at scale, bridge the velocity gap between engineering and data science, and drive down both processing time and infrastructure costs. Have you ever thought that innocent retries could cost you hundreds of dollars?

You’ll walk away with methods to iterate faster and deliver more reliably in a heterogeneous environment of data scientists and software engineers. Whether you are a tech lead, architect or manager, this talk will prepare you to ship real-world ML applications, not just experiments.

Key takeaways

• Dealing with pace differences between data science and engineering
• Methods to iterate faster with ML products
• Awareness of the hidden effort required to bring ML to production and differences between regular software projects and ML ones

No matter what your org chart says, if you’re building a lasting product, you’re in a product and tech relationship. Make it a good one.

Good means effective and efficient, but also enjoyable. That kind of partnership takes time, care, and intention.

Here are the three pillars of a strong product and tech relationship:

1. Alignment – Are you actually on the same page? Leadership styles, ways of working, and long-term goals need to match before you can make real progress.
2. Date Night – Keep the spark alive with regular check-ins, syncs, and collaborative rituals to ensure ongoing alignment — especially at the leadership level.
3. Best Friend to Moan To – No relationship is without its challenges. Have a trusted partner to vent, troubleshoot, and work through friction before it turns into conflict.

Key takeaways

• Tools and best practices for alignment and collaboration
• How to create moments of being human to develop trust and make your relationship stronger
• A laugh or two as I share stories from the messy reality at the intersection between product and tech and what we can learn from them.

What does it really mean to support people who are different and create an inclusive working environment for all neurotypes? Learn the difference between lip service and real diversity.

This talk is about the difference between paying superficial support to diversity and inclusion, versus truly understanding how different colleagues, team members, and employees have varying challenges and needs. From the small, everyday details to the bigger picture, this talk includes practical ideas and examples of how your behaviour and the support you offer can have a major impact on whether you and those around you are able to thrive.

Clare draws on years of experience supporting people with a wide range of needs as a mentor, tutor, interviewer, and line manager. She also shares her own perspective as a middle-aged autistic woman with ADHD navigating menopause. Through personal stories, she highlights the challenges she has faced and what has made a real difference.

Small actions can have a big impact.

This is a grounded and honest look at what it really means to be different, with practical advice and insights on how greater awareness can lead to meaningful change.

Key takeaways

• How to identify the needs and strengths of all your team members
• How to create a supportive working environment for people with differing needs
• What neurodivergence means
• The strengths of neurodivergent team members

A real-world story of how we integrated AI into our product development workflow, what broke, what worked, and how it’s changing our sprints, our quality, and our team habits.

How do you bring AI into your product development workflow without slowing your team down or making things worse?
At Learnn, an italian EdTech startup with over 200,000 users, we set out to do just that.

In this talk, I’ll walk you through how we introduced AI into our engineering process, not as a shiny tool, but as part of how we build, test, and deliver product. You’ll see what worked, what failed, and how AI is now helping us write code, deliver features, generate tests, improve documentation, and even speed up product discovery.

We’ll look at the impact on sprint velocity, product quality, and team dynamics with honest lessons from the trenches.

Whether you’re leading a small team or scaling a large org, this talk will give you practical ideas (and cautionary tales) on how to adopt AI in a way that’s sustainable, measurable, and team-friendly.

Key takeaways

• A practical framework to start integrating AI into your workflow.
• Key insights into what AI is good at and where it breaks.
• How AI adoption changed our sprint dynamics and team mindset.
• Strategies to introduce AI without losing control or quality.
• A real-world use case of AI’s impact on our product delivery

AI is reshaping what’s easy to solve in technical interviews, prompting us to revisit what we ask and why. This talk explores how more thoughtful, human interviews can lead to deeper signal and better hiring.AI is reshaping what’s easy to solve in technical interviews, prompting us to revisit what we ask and why. This talk explores how more thoughtful, human interviews can lead to deeper signal and better hiring.

AI is changing the landscape of technical interviews. With tools that can generate code, explanations, and solutions in seconds, many traditional formats are losing their ability to surface meaningful signal. At the same time, AI is becoming a natural part of how engineers work – so why design interviews that ignore it?

This talk explores how we can adapt interviews to this new reality. Rather than relying on questions that are easy to offload, we’ll focus on ways to uncover deeper qualities: how candidates think through ambiguity, weigh trade-offs, and communicate their decisions. It’s not about avoiding AI—it’s about designing interviews that reveal what it can’t answer for someone.

We’ll dig into practical strategies for structuring interviews around your team’s actual workflows, crafting questions that invite thoughtful discussion, and creating an environment where candidates feel comfortable showing how they approach real problems. You’ll also learn how interviewer tone and feedback can elevate – not dilute – evaluation.

More humane interviews don’t just feel better for candidates – they give your team a clearer signal, more honest conversations, and ultimately,
stronger hires.

Discover a system that monitors your team’s stress signals, staves off burnout, and evolves leadership into a dynamic and adaptive framework.

Engineering teams face challenges that traditional KPIs often overlook. In this talk, I share practical lessons learned from creating systems to detect early signs of burnout, stress, and misalignment across teams. I compare how we monitor code performance with the way we notice subtle signals in team behavior, such as variations in energy, mood, or engagement that typically remain unseen.

I will outline straightforward methods for implementing regular check-ins and feedback loops that capture early warning signs. You’ll learn simple practices that encourage transparent communication and prompt the reporting of issues before they escalate into burnout or operational disarray.

This talk is geared toward anyone who leads engineering teams, whether managing a small group or an entire department. The techniques I share are practical and straightforward, focusing on establishing routine assessments that serve as health-checks for your organization. By shifting from a reactive approach to proactive management, leaders can maintain steady progress while ensuring team well-being.

Ultimately, you’ll come away with actionable insights that help you build a self-regulating team system: one that detects early stress signals and enables timely intervention. This approach not only boosts productivity, but also helps create a sustainable, supportive work environment where innovation can thrive.

Key takeaways

• How to track subtle signals of stress and low energy in your team.
• Practical steps for setting up regular feedback and check-in processes.
• Methods for addressing issues before they escalate into burnout.
• How to balance productivity with the ongoing well-being of your team.

Discover how the Financial Times used AI to personalise paywalls and reduce churn – along with the architecture, trade-offs, and unexpected side effects of applying machine learning to real business-critical decisions.

Over the past year, the FT has partnered with two external AI providers to deliver large-scale machine learning projects focused on subscriber acquisition and retention. By using predictive models to personalise user journeys on FT.com, we’ve more than doubled our projected lifetime revenue per customer.

In this talk, I’ll walk through the real-world implementation of AI at a media organisation – what worked, what didn’t, and what we’d do differently. You’ll get a behind-the-scenes look at our architecture for integrating Sub(X) into the paywall, and how we navigated unintended consequences, like a risk to advertising revenue we initially handled the wrong way. I’ll also share how we tackled churn reduction with a different AI model built by VectorLabs, which runs on our own infrastructure – and the unique technical and operational challenges that came with that.

Finally, I’ll offer hard-earned lessons on evaluating and maintaining AI in production: how to adapt to disruptions like price changes, what to monitor during model training, and how we revisited our approach to testing to cope with the explosion of possible user journeys AI introduced. Whether you’re considering an AI initiative or already running one, this talk will give you a clear-eyed look at the trade-offs, stakeholder dynamics, and surprising complexity that come with applying machine learning in a real business context.

Key takeaways

• Strategies to minimise risk and build stakeholder confidence when integrating AI into critical user journeys
• Practical techniques for evaluating models that continuously adapt and evolve
• Why we chose different AI providers and model architectures for acquisition vs. retention – and what we learned from that decision

Promotion denial stings. Discover strategies for bouncing back, effectively managing expectations, and reigniting your career growth and fulfillment, with insights on effective communication and managerial support.

Rejection stings. Especially when it’s a promotion a person was aiming for and putting significant effort in. So what happens when that ‘next step’ doesn’t happen?

This presentation dives into the different aspects of navigating this challenging situation, offering valuable insights for both employees and managers. Understanding the emotional impact of rejection, the potential loss of trust, and the various ways disappointment can manifest are crucial first steps in effectively addressing the aftermath of a denied promotion.

This session will dive into the critical questions and decision-making process an employee faces after a promotion denial:
“Who am I without this promotion?” Having a promotion denied often feels like a personal rejection instead of a professional one. We will look into how to reframe success beyond the title.

“Is it me or the company?” It’s important to get clarity why the promotion was denied. We will examine common reasons, such as personal factors, role preparedness, or simply unfair treatment.

“Should I stay or should I go?” Should the person remain with the organization (double down on any growth areas or pursue alternative paths internally) or to seek new challenges elsewhere? We will analyze the factors that might influence this decision and provide frameworks for evaluating different options.

We will also explore the importance of well-defined promotion pathways. Transparent communication throughout the promotion process is crucial, ensuring that employees understand the criteria of performance and promotion, and have clarity on their progress. The role of managerial support in providing constructive feedback, guidance, plans, and ongoing development opportunities is key for navigating the situation successfully.

Furthermore, we will focus on the essential skill of resilience in case of a career setback. We will discover practical strategies for bouncing back from a missed promotion, effectively managing expectations and adapting to unexpected turns in their career trajectory. We will explore how to transform disappointment into renewed focus on personal and professional development, ultimately reigniting career growth and fostering a sense of fulfillment.

Key takeaways

• Techniques to transform promotion denial to an opportunity of personal and career growth
• 30-day comeback plan after promotion denial
• Mini-resilience self assessment

Charity’s new talk will address the incredible variance in performance among engineering teams in 2025. While the ceiling for engineering performance has increased dramatically over the past decade, only a tiny fraction of teams are actually taking advantage of this shift. Hear how your observability team can help unlock the next level in performance.

Explore solutions for your technical issues.

Learn how real engineering teams turn ideas into practice.