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.

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.

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. 

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.