Development & technology · Tech Leads & Engineering Managers

AI training for Tech Leads and Engineering Managers

AI coding assistants and autonomous agents are changing how engineering teams work: tasks are sliced differently, effort shifts from writing code to reviewing it, and deciding which work can be delegated to agents becomes a leadership responsibility. Tech leads and engineering managers have to select tools, set policies, enable their teams and measure impact without losing sight of code quality, security and the growth of junior engineers. In this course you learn to plan the rollout of AI coding tools in a structured way, establish shared working practices and rule files for your team, choose meaningful metrics beyond raw speed and apply AI to leadership tasks such as planning, onboarding and technical communication. Deep programming skills are not required.

Who is this course for?

  • Tech leads and lead developers
  • Engineering managers and software team leads
  • Heads of engineering and CTOs in mid-sized companies
  • Delivery and project leads in IT organisations
  • Developer experience and enablement leads

Typical challenges we address

  • Unclear policies lead to a patchwork of tools and shadow AI within the team
  • More generated code increases review load and the risk of technical debt
  • Difficulty measuring the real benefit of AI tools in a credible way
  • Concerns about junior engineers’ skill development and team buy-in

Practical scenarios from your field

Writing a team AI policy

Draft a practical guideline that clearly defines approved tools, permitted data classes, review obligations for generated code, handling of secrets and accountabilities, illustrated with concrete examples.

Rule files and team conventions

Build shared context files for coding agents covering architectural principles, testing strategy, style rules and banned patterns, keep them under version control and ensure consistent output across the team.

Measuring impact

Collect metrics such as DORA measures, pull request cycle time, change failure rate and developer satisfaction before and after rollout, and prepare the analysis with AI support.

Slicing work for agents

Write and break down backlog items so they can be delegated to agents with clear acceptance criteria, and decide which tasks deliberately stay with people.

Faster onboarding

Prepare codebases, architecture decisions and operational knowledge for new team members, create guided exploration tasks and have questions about the codebase answered with source references.

Technical communication

Draft incident post-mortems, roadmap updates, decision papers for senior management and release notes from tickets and commits, structured and tailored to each audience. This frees up time for actual leadership.

Possible hands-on projects

Team metrics dashboard

A small web app that pulls data from your Git platform and shows cycle times, review duration and deployment frequency over time.

PR summariser

A bot that collects all merged pull requests each week and turns them into a readable status report for stakeholders.

Onboarding guide generator

A script that analyses repository structure, README files and ADRs and produces a structured onboarding plan with learning tasks. It adapts to role and seniority.

All 11 courses for Tech Leads and Engineering Managers

Vibe & agentic coding (11)

Data protection & compliance

As a leader you own the legal framework: you learn to address licensing and copyright questions around generated code, protection of source code and secrets, works council co-determination rights when measuring performance and obligations under the GDPR and the EU AI Act in your team policies.

Prerequisites

You lead a software team, either technically or as a line manager, and understand the development process; current hands-on coding is helpful but not required.

Frequently asked questions

How do we tell whether AI coding tools are worth it?

Don’t count generated lines of code; look at outcomes: lead time from idea to production, change failure rate, review effort, defect rates and team satisfaction. A baseline before rollout and accompanying qualitative surveys are essential. In the course you design a lean measurement approach that fits your organisation and involves employee representatives.

Do junior engineers still learn enough when AI writes the code?

The concern is valid and can be managed. Proven practices include having engineers explain every generated section in review, deliberate practice phases without an assistant and pairing with experienced colleagues where AI acts as a learning partner. You learn to adapt learning paths and review culture so that skill building and productivity go hand in hand.

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Total €949 plus VAT