TechLead Conf Amsterdam 2026: Adopting AI in Orgs Edition

The role of engineers is evolving in the AI era. As development tools become more powerful and accessible, the expectations for engineers are shifting from simply writing code to creating meaningful impact across teams and organizations.
In this talk, Gregor will share the concept of the engineering multiplier: an engineer who amplifies the effectiveness of the people around them, takes ownership beyond implementation, and proactively drives the most impactful work.

AI tools are writing more code than ever — but the engineers and leaders getting the most from them aren't the ones with the best prompts. They're the ones who think most clearly.Drawing on The 5 Elements of Effective Thinking by his former math professor Michael Starbird, Lindsey makes the case that a generalist's toolkit — deep understanding, productive failure, relentless questioning — is the real multiplier in an AI-augmented workflow. The same habits that make you a better thinker make you a better collaborator with machines.This talk also draws on Vercel's own AI adoption journey: rolling out AI tooling company-wide, developing shared vocabulary for working with coding agents, and building systems that connect AI-generated code to real production impact. Lindsey shares practical lessons on what's worked, what hasn't, and why "learning how to think" may be the most underrated leadership skill in tech right now.

The Hype vs. RealityTrillions of dollars invested in AI are fueling massive excitement. Yet amid the buzz, it's tough to separate fact from fiction. Science paints a sobering picture: MIT reports 95% of AI investments deliver no value, while METR finds AI slashing productivity by -20%. Sound familiar? In your organization, vocal AI advocates push big claims, often sidelining skeptical senior engineers.The Problem in Your TeamsThis dynamic risks wasting resources and alienating expertise. How do you cut through the noise and pinpoint where AI truly creates value?Lean Tech: The Scientific AntidoteEnter Lean Tech—an adaptation of Lean Thinking for tech, rooted in Toyota's methods and Deming's scientific approach to work. It's the ideal framework for navigating AI's revolution, focusing ruthlessly on value creation.Key Lean Tech Principles in Action (from Theodo's Experience): - Value for the Customer: Spot AI-solvable problems and measure improvements rigorously.- Tech-Enabled Network of Teams: Empower autonomous teams to experiment with AI tools.- Right-First-Time: Analyze every issue, no matter how small, to extract lessons.- Just-in-Time: Track lead-time gains for productivity, not just cycle time.
Building a Learning Organization: Use 6-step Kaizens, standards, skills matrices, and dojos to scale knowledge.Proven Outcomes: 3x faster legacy modernizations and 2x acceleration on projects where product decisions aren't the bottleneck.Attendees will walk away with a step-by-step playbook to inject science into AI adoption: identify high-impact opportunities, measure real ROI, foster autonomous experimentation, and build lasting learning systems—ensuring your teams deliver tangible wins without the hype.

AI is reshaping how engineers work, but many organizations are training teams in the wrong direction—optimizing for prompts instead of thinking. This leads to fast output, shallow understanding, and fragile systems. This talk focuses on how tech leaders and senior engineers can adopt AI while preserving engineering judgment, ownership, and long-term system quality. You’ll learn how to train engineers to use AI as a tool—not a crutch—without sacrificing craft or increasing complexity.

88% of organizations now report using AI, yet only 39% capture meaningful enterprise value from it (McKinsey State of AI Survey, Nov 2025). The gap between "we use AI" and "AI transformed our business" has never been wider. In this talk, I'll dissect why most AI adoption metrics are vanity metrics dressed in executive clothing, drawing on strategic AI research and my own experience rolling out AI-powered tools across IKEA's global supply chain. You'll walk away with a practical framework for measuring what actually matters: workflow redesign depth, decision-quality uplift, and compounding capability gains, not chatbot logins per month or tokens burned by your teams.

Most companies talk about becoming "AI-native". Very few actually do it.

In this talk, I’ll share how we’re approaching the upskilling of ~300 engineers to move beyond experimentation and into real, repeatable AI-native development.

We’ll cover how we’re introducing new paradigms like AI-Native Engineering (AINE) and Spec-Driven Development, how we’re structuring the individual contributor journey, and how we’re driving adoption across teams with very different levels of maturity.

More importantly, we’ll dive into what doesn’t work: resistance patterns, false starts, over-reliance on tools, and the gap between perceived and actual productivity gains.

This talk will give you a concrete blueprint along with the trade-offs and lessons learned along the way.

Agents are the next distributed system: non-deterministic, autonomous, and tool-connected. Some patterns we rely on to run distributed systems reliably transfer directly; others break down and need rethinking.

This talk applies lessons from DevOps, platform engineering, and SRE to the agent era: enablement structures, hybrid pipelines mixing deterministic and probabilistic steps, SLOs and error budgets for agent reliability, context engineering as the new dependency management, golden paths for non-deterministic actors, and where human judgment belongs in agent pipelines.

Building blocks, patterns, tactics, and hard learnings from running agents in production on enterprise client engagements.

While most of the conversation around AI in software engineering is about using it to pump out new features at a rate we haven't seen before, one of the most interesting use cases for AI is ensuring the quality of your product. From PR reviews to bug fixes to code cleanup, AI can help engineering teams focus on what they enjoy working on, while helping them create a better product. 

In this talk, Kseniia Korostelova shares the results of a real experiment where she attempted to delegate parts of her engineering leadership work to AI. From architecture decisions and feature planning to design reviews and performance feedback, some tasks worked surprisingly well while others failed completely.
These experiences led to the concept of Organic Leadership: a leadership approach that uses AI to remove noise while preserving the human judgment, trust, and context that teams rely on.
You will learn where AI can genuinely augment engineering leadership today, where it cannot, and why the rise of AI may actually increase the value of truly human leadership.

This talk is about what gets unlocked when engineers pair with each other and an agent, instead of disappearing into silos with one human and one machine. We never paired to type. We paired to stay aligned, challenge assumptions, and make better decisions together. When code gets cheap to produce, the human collaboration layer matters more, not less. We'll explore what effective human+agent collaboration actually looks like in practice and you'll leave with a framework for keeping teams aligned without slowing them down. 

Every cloud platform was designed for developers: humans who read docs, click dashboards, and push to git. But AI agents are already using these platforms, and they experience them very differently. At Render, we've watched agents parse our marketing pages, struggle with our APIs, and surface (or not surface) our platform in LLM recommendations. Building our MCP server, CLI, and agent skills meant designing for two users at once, and rethinking what "developer experience" even means when the developer isn't human. This talk distills what we learned: where our assumptions failed, what we changed in response, and the concrete principles engineering leaders can apply to build tools, docs, and APIs that serve both humans and AI agents.

Manual coding as the primary activity in software development is ending.

Developers will not spend most of their time writing code. They will review, steer and constrain what AI systems produce. The bottleneck shifts from typing syntax to defining context, boundaries and intent.

When code can be generated instantly, the leverage moves to the system around it. Your internal developer platform, your golden paths, your compliance controls, your deployment standards and your observability become the real foundation of product development.

Without a strong platform, AI accelerates fragmentation. Different tools, inconsistent environments and ad hoc processes will compound into chaos at machine speed. With standardized workflows and paved roads, AI becomes a multiplier for quality and consistency instead of risk.

At the same time, each developer will likely be responsible for more software than ever before. As output increases, cognitive load must decrease. That means less variability, fewer bespoke setups and stronger defaults. The complexity of the systems must go down even if the volume of code goes up.

Internal developer platforms are no longer a productivity initiative. They are a prerequisite for succeeding in the AI era of software development. If you are rethinking your Content experience platform for this AI-driven era, partnering with an engineering-led headless CMS agency like FocusReactive turn these principles into a practical, scalable content platform.

An eval platform is not just a test runner. You are building shared definitions of “good,” reliable data pipelines, labelling workflows, versioning, and trust in results across many teams and model changes. This session breaks down the hidden complexity, the common failure modes, and the design principles that make evals credible and usable in day-to-day engineering.