What is included in an AI strategy?

The six components of an AI strategy

A current state assessment maps every AI system already in use — including AI embedded in existing software platforms, not just standalone AI tools procured specifically for the purpose. This is typically the first surprise: most organisations are already using more AI than they realise. A prioritised use case list identifies where AI creates the most value relative to the effort and risk of deployment, sequenced so that high-value, low-risk applications come first. The governance foundation is the set of policies, oversight structures, and risk management processes that make AI deployment responsible. The implementation roadmap is a sequenced set of actions with owners, timescales, and dependencies. The skills and capability plan identifies what training is needed and for whom. Success metrics define what the strategy is supposed to achieve and how progress will be measured.

EU AI Act compliance as a strategic input

For Irish organisations, the EU AI Act is not a background consideration — it is an active constraint that shapes strategy. The Act classifies AI systems by risk tier. High-risk AI systems — those used in employment, credit assessment, healthcare, or other regulated contexts — require conformity assessments, technical documentation, human oversight mechanisms, and registration before deployment. High-risk obligations have a separate phased timetable. Assess the intended use, the organisation’s role and the applicable transition provisions before deciding what is required. An AI strategy that does not account for the Act is incomplete: it will recommend deployments that are not legally compliant or underestimate the implementation effort required.