AI TRAINING
AI for CTOs: Engineering Organisation Design
Leave with a concrete AI-augmented engineering strategy covering talent, tooling, and platform investment decisions.
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Run the diagnostic →What it covers
This practitioner-level programme equips CTOs and VP Engineering with the frameworks and decision-making tools to redesign their engineering organisations around AI capabilities. Participants evaluate developer productivity tooling, build AI platform investment theses, and define talent strategies that blend AI-native and traditional engineering roles. The format combines peer cohort sessions, case studies from scaled engineering orgs, and a capstone organisation design exercise. Participants leave with a board-ready roadmap for AI integration across their engineering function.
What you'll be able to do
- Build a scored vendor evaluation matrix for AI developer tooling and present a justified buy/build/partner recommendation
- Design an AI-augmented engineering org chart with defined team topologies, responsibilities, and headcount implications
- Define a 12-month talent strategy that identifies roles to retrain, roles to hire, and roles impacted by AI automation
- Construct a platform investment roadmap distinguishing foundational infrastructure from quick-win productivity tools
- Produce a board-ready business case quantifying productivity gains, cost impacts, and risk factors of the proposed AI strategy
Topics covered
- AI-assisted developer productivity: Copilot, Cursor, and beyond
- Build vs. buy vs. partner: AI vendor stack evaluation frameworks
- Platform engineering investments for AI workloads (MLOps, LLMOps)
- Talent strategy: hiring, upskilling, and role redefinition in AI-augmented teams
- Engineering org design models: platform teams, AI guilds, embedded AI squads
- Measuring engineering effectiveness with AI tooling (DORA, SPACE metrics)
- AI governance and security posture for engineering organisations
- Budget modelling and board-level business cases for AI platform investment
Delivery
Delivered as a 4-week cohort programme with two 3-hour live virtual sessions per week, supplemented by async case study reading and a group capstone project. In-person intensive formats (2-day offsite) are available for executive leadership teams. Hands-on exercises account for approximately 50% of learning time. Participants receive a facilitated peer network of engineering leaders across non-competing industries. All materials, frameworks, and templates are provided digitally.
What makes it work
- Executive sponsorship: CTO champions the AI engineering vision explicitly, rather than delegating to a single AI team
- Pilot-then-scale discipline: one high-visibility team adopts AI tooling with defined success metrics before org-wide rollout
- Role clarity: new AI-adjacent responsibilities (prompt engineers, AI platform engineers) are formally defined in job architecture
- Regular feedback loops: quarterly engineering effectiveness reviews that tie AI tool adoption to DORA and SPACE metric trends
Common mistakes
- Treating AI tooling adoption as a bottom-up engineering choice rather than a strategic org-design decision with budget and governance implications
- Deploying AI developer tools without updating engineering metrics, leading to misleading productivity signals
- Underestimating talent strategy change, assuming existing engineers will self-organise around new AI workflows without structured upskilling
- Selecting AI infrastructure vendors based on technical demos alone, without evaluating data residency, compliance, and long-term TCO
When NOT to take this
This programme is not the right fit if the organisation has fewer than 10 engineers or has not yet shipped a production product, foundational engineering practices should be established before redesigning the org around AI augmentation.
Providers to consider
Sources
Use cases this training unlocks
- AI-Assisted Code Generation and ReviewAccelerate software delivery by automating code suggestions, boilerplate generation, and PR security reviews.
- AIOps Infrastructure Monitoring and RemediationAutomatically correlate alerts, predict incidents, and trigger remediation for IT infrastructure teams.
- ML-Driven Infrastructure Capacity PlanningPredict resource utilisation trends and automate scaling decisions to cut infrastructure waste.
- Technical Debt Scoring EngineQuantify and prioritize technical debt across codebases so engineering teams can act on what matters most.
- Automated Vulnerability Prioritization with MLAutomatically score and rank security vulnerabilities so teams fix what matters most, faster.
- Digital Twin Simulation PlatformMirror physical assets in software to simulate, predict, and optimise operations before changes happen.
Other trainings at this level
This training is part of a Data & AI catalog built for leaders serious about execution. Take the free diagnostic to see which trainings your team needs.