TechLead Conf Amsterdam 2026: Adopting AI in Orgs Edition

The speaker emphasizes the importance of engineers becoming multipliers in the AI era, highlighting the evolution of tools and methodologies in software development. The shift towards engineering leadership necessitates essential skills like feedback, delegation, and project leadership. The changing landscape of engineering roles reflects a rise in tech leads and product engineers. Adaptability and the ability to learn fast are crucial in the evolving demands of the industry. Strategies for career growth include showcasing skills, embracing AI adoption, and fostering a culture of continuous learning and adaptation.

Ilja founded Endgame to modernize systems, facing challenges in scaling practices and security. DevOx explores agentic experience, addressing client inquiries on technical and operational aspects. Developers encounter challenges in agent security and context management at scale. Efficient deployment with GitHub actions and contextual operations for improved efficiency. Cost optimization through LLM gateway and organizational enablement for effective team coordination. Adoption pockets and agentic AI best practices for organizational advancement. High-risk code creation for medical devices involves automation and compliance challenges. Importance of specialization in small teams for effective code review and skill-based reviews for expertise embedding.

Discussion on effective thinking in the age of augmented tooling, user interface development, AI's impact on software engineering, and the importance of saving time. Importance of identifying high cognitive load tasks and introducing a framework for tech leads to guide teams with AI. Leveraging existing knowledge in the era of AI, transformative learning framework, and recognizing one's identity in the craft. Aang's transformative decision, reimagining roles for AI engineers, effective communication with AI, and revival of liberal arts in tech education. Startups' agility, clarity in logic, and continuous adaptation in engineering. Constant evolution, adapting to changing technology, and problem-solving focus in tech roles. Loop engineering concept, autonomous problem-solving loops, importance of defining problems, utilizing data effectively, and adapting to changes in technology. AI-driven code review, agent loop framework, embracing being wrong for learning, and example of tool Dev 3000. AI-driven automation for browser control and improvement, focusing on cumulative layout shift optimization. Introduction to Agent Browser for browser automation and layout shift optimization. Agent Browser for continuous improvement through verification, repetition, and learning. Balancing busyness and sustainability in work and life, challenges in work-life balance for creative technology jobs, and measuring creative project success. Challenges in code reviews, automation, managing agent loop costs, and optimization.

Tech leads play a crucial role in AI value creation. Global AI spending in 2026 to reach $2.5 trillion. A Kinsey report reveals low impact on profits despite massive AI investments. More than 90% of organizations adopt AI, yet lack real value creation. AI initiatives often lack global impact due to local focus on metrics, not end value. Misunderstanding the value creation akin to Toyota's success. Freddy Ballet discovers Toyota's secret in Europe. Taichi Ono's unconventional methods for value creation in France lead to significant productivity gains and quality improvements. Realizing significant value through collective problem-solving and innovative strategies at Toyota. Focusing on Lean principles to create value through collective problem-solving and adapting learnings for AI integration. Lean Tech Manifesto emphasizing value for customers and creating a continuous learning system for AI transformation, driving value creation through customer-centricity and daily learning opportunities. Addressing bottlenecks in project delivery through AI, Achieving quality with one-shot prompting, Fostering a learning organization with Kaizen approach in AI environment. Utilizing blueprints to streamline code review processes, Embracing a holistic approach to AI value creation, Importance of metrics in evaluating organizational and product performance.

The Talk delves into the challenges faced with polyrepos, emphasizing issues with managing multiple apps and dependency hell. It highlights the benefits of monorepos in efficient code sharing and version management, advocating for their simplicity and effectiveness. The advantages of monorepos include atomic changes, large-scale refactoring, and strong code reuse culture. Monorepos offer benefits such as simplified dependencies, unified CI-CD, enhanced collaboration, and efficient refactoring. The impact of monorepos on legacy code bases includes reusability, traceability, early issue detection, and enhanced CICD processes. The discussion also touches on the challenges of context switching in a polyrepo environment, the limitations of AI in polyrepo versus monorepo settings, and the importance of building context layers for enhancing AI capabilities in monorepos.

Nihan, Gregor, Fabrice, and Lindsey discuss the impact of AI on their companies and the evolving skill requirements in the AI era. Engineers need people skills and good judgment in addition to technical skills. Hiring based on growth mindset and internships for learning evaluation are crucial. Developers should focus on being well-rounded and engaging in freelance projects for career growth. Senior engineers play a key role in architecture and AI control, with roles shifting towards tech leads. Evaluating performance and defining value in engineering roles are challenging tasks. Engineering managers are evolving towards enabling team improvement and interdisciplinary responsibilities, requiring continuous learning and adaptability.

Training engineers for proper AI usage without prompt AI dependency. Challenges in ensuring code quality with AI development. Importance of DRY principle in optimizing AI usage. Advocating for effective AI practices implementation with project-specific rules. Establishing AI rules and standards for seamless collaboration. Maximizing AI efficiency with agents mimicking human roles. Encouraging self-education, setting standards, and focusing on quality for AI proficiency. Emphasizing context, validation processes, and specialized agents for maximizing AI efficiency.

AI dashboard utilization challenges include lack of clear metrics for effectiveness and low adoption rates. Companies struggle with AI implementation leading to delivery improvements and financial gains. AI transformation hurdles stem from a focus on fluency over workflow redesign. Achieving true AI integration requires deep integration for transformative change. Organizational challenges in AI involve balancing code production with product outcomes. Key steps for AI transformation include measuring real changes in production and prioritizing killing ineffective pilots. Managing cloud costs and addressing unused resources are key concerns. Measuring AI impact on teams, business, people growth, and skill development is crucial for successful implementation.

Richard Rodenkemper, senior software engineer at Sentry, discusses ensuring quality with AI. GitHub data shows exponential growth in coding. Concerns arise about the reliability of coding agents versus human engineers. Impact of AI and Cloud on code production and app quality is questioned. Challenges in code reliability despite increased production are highlighted. AI as a quality tool in software development. Importance of reliability for product success highlighted. AI's strengths in handling data and searching code base discussed. Examples of AI usage in code reviews and quality assurance at Sentry shared. AI efficiency in endpoint deprecation and system updates highlighted. AI's assistance in migrating design systems and reducing notifications using Cloud Code emphasized.

The speaker delves into code editor development, emphasizing collaboration between humans and AI. Discussions revolve around adapting to technological changes while facing persistent cognitive limitations. Balancing cognitive load in software development is crucial for optimal task completion. Focus and alignment in software development are essential for effective problem-solving. Addressing challenges of team alignment in AI-driven environments is crucial to avoid creating legacy code bases. Pair programming enhances collaboration, accountability, and learning within development teams. Valuing collaboration, trust, and autonomy fosters speed and efficiency in software development. Leadership strategies focus on promoting autonomy, mastery, and purpose while addressing burnout. AI impact on productivity and collaborative coding practices are reflected upon, emphasizing the benefits of pair programming. Effective onboarding and encouraging pair programming adoption contribute to better problem-solving and team collaboration.

AI-powered software development is rapidly evolving, leading to pressure for more AI implementation. Developers are transitioning from code completion to orchestrating change, managing increased output. Challenges arise from neglecting code relevance and adapting to accelerated workflows. Internal developer platforms reduce cognitive load and system complexity, emphasizing adaptable strategies and communication. Organizational learning and intentional system strategies are crucial for acceleration. Engineering leaders must consider deterministic controls and generative AI impact. AI integration for non-developer contributions requires scalability and security considerations. Standardization in development pipelines is crucial, balancing with flexibility for experimentation and evaluation.

Reflecting on the integration of AI in software development and the implications for leadership and decision-making. AI integration in leadership: embracing context, judgment, and accountability. Principles: Context before output, Intent before optimization, Awareness before efficiency, Accountability before automation. Leadership as an ecosystem with roots, stem, and fruit; AI's role in each part. Using AI at different levels of leadership: fruit, stem, and roots. Decision-making needs context. Leadership bridges information and context gaps. AI for efficiency but human touch for depth and understanding.

Shifra, founding developer relations engineer at Render. Render is the cloud for builders. How to relate to users who are not people? Company growth challenges with AI recommendations affecting signups. The challenges of AI recommendations in contrast to traditional SEO. Impact on team operations and product development. Need for a strategic shift towards agent-centered developer experience. Developing interface design for agents, content portfolio importance, and human gate validation. The evolving role of agents in product consumption and the necessity for a fundamental shift in development focus. Facing challenges head-on, emphasizing agentic experience, and prioritizing system self-correction for productive agent and human interactions at Render.

The talk explores scaling AI adoption in a large engineering company, emphasizing mindset shifts and upscaling. It discusses achieving AI-native team transformation through AI fluency and standardization. The importance of upskilling individuals and teams, overcoming resistance, and addressing objections to AI tools is highlighted. Transitioning to AI-native engineering focuses on intent, teamwork, and problem-solving. Emphasizing co-learning, project context, and verification in AI engineering and engaging engineers in practical AI projects are key points. Exploring AI engineering fundamentals, optimizing project iterations, and addressing project bottlenecks are also discussed.