How long does it take to develop an AI strategy?

What happens during strategy development

The first phase is diagnostic: understanding what AI is already in use, what the organisation’s business objectives are, where capability gaps exist, and what the regulatory landscape looks like for this specific organisation. This typically involves structured interviews with the leadership team, a review of existing technology and process documentation, and an assessment of data quality and governance maturity. The second phase is strategy development: working through the diagnostic findings to identify the priority use cases, governance requirements, and capability investments. The third phase produces the deliverables: a written AI strategy document, a governance framework, and an implementation roadmap with sequenced actions and timescales. With an experienced external advisor, this process does not require the organisation to pause its operations — it is designed to fit around the leadership team’s existing commitments.

Why rushing AI strategy development is a false economy

Organisations that rush AI strategy development — or skip it entirely in favour of immediate tool deployment — consistently produce the same problems: AI tools that do not address real business problems, governance gaps that create regulatory exposure, and AI investments that cannot be evaluated because success was never defined. The cost of recovering from a rushed or absent AI strategy is almost always higher than the cost of developing one properly. For Irish organisations, the August 2026 EU AI Act enforcement deadline adds urgency — but that urgency argues for starting strategy development now, not for compressing the process. A four-week strategy engagement that produces a governance-ready roadmap is better than a two-week exercise that misses the compliance dimension.