AI for Engineering Leaders
A practical, team-level leadership programme for engineering managers and technology leaders.

At a glance
What you need to know
Over three days, participants learn to set the standards and guardrails that make AI adoption productive and safe, recognise the common failure modes, evidence real productivity gains, and apply AI to their own planning and delivery workload. The programme runs with extensive applied examples and case studies throughout.
- 3-day leadership programme
- 5 core sessions plus a leadership commitment close
- Practical and team-level, built around applied case studies
- Designed for engineering managers and technology leaders
Outcomes
What you'll gain
Lead AI-enabled engineering teams: set standards and guardrails, and coach prompt discipline and output validation across pods
Recognise and manage the common failure modes of AI-assisted engineering, including automation bias, hallucination risk and over-reliance
Define and track practical AI adoption metrics at team level, and evidence productivity gains and ROI to stakeholders
Understand the new dynamics of estimating, planning and costing engineering programmes where humans work alongside agents, and apply tokenomic best practice
Apply AI to their own management workload, including sprint planning, delivery reporting and decision support
About the programme
Key facts
Delivered by practitioners with hands-on engineering leadership experience, the programme combines short conceptual input with extensive applied examples and case studies, so leaders leave with a concrete plan for what changes for their team following the training.
About our training partner - Neueda
Neueda is the training delivery partner for this engineering leadership course. We work with organisations to turn AI strategy into practical, applied capability, pairing structured learning design with instructors who bring real hands-on delivery experience. Every programme is built to be hands-on and outcome-focused, grounded in the tools and challenges participants face in their own roles.
Course content
What you'll cover
Enabling productive, responsible adoption across pods.
Setting standards and guardrails for AI-assisted coding, testing and review
Coaching prompt discipline and output validation
Supporting consistent adoption across pods and squads
Balancing experimentation with risk awareness.
Automation bias, hallucination risk and over-reliance
What breaks in AI-assisted coding and review, and the signals to watch for
Embedding responsible AI behaviours day to day
Evidencing productivity gains and ROI.
Practical AI adoption metrics at team level
What “good” AI usage looks like in high-performing teams
Reporting productivity gains and ROI to stakeholders
Applying tokenomic best practice to programme planning and delivery costs.
The new dynamics of estimating, planning and costing engineering programmes where humans work alongside agents
Tokenomic best practices for scoping, budgeting and pricing AI-assisted work
Translating agent and token costs into estimates and business cases stakeholders can trust
Turning AI into a leadership multiplier.
AI-assisted sprint planning and backlog refinement
Drafting status reports, technical summaries and governance reports
Decision support and structured problem analysis
Each leader commits to one concrete change in how their team works with AI
Commitments are captured across the cohort as the basis for follow-up with engineering leadership
Delivery Partners
Led by

Neueda

Nick Todd
Our Partners

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