Enterprise AI Governance & Execution
A scattered set of AI initiatives, owned by no one in particular, needed to become a governed, prioritized program.
Who it's for
You're an executive team that has real AI activity happening across the business but no shared view of what's mature, what's risky, and what to do next.
Timeline
Several weeks, run in structured rounds of evidence-gathering
Format
Structured assessment + operating model engagement, remote and on-site discovery
The situation
The engagement started with evidence scattered across the business — different teams, different levels of AI maturity, no shared inventory or shared language for talking about it. The difficulty wasn't a lack of activity; it was that no one had a structured way to compare initiatives against each other, understand the dependencies between them, or know which ones actually carried risk worth escalating.
What I did
I ran the business through a defined prioritization model, layered with market research specific to their industry and business model, and a close look at the operational and technical dependencies underneath each initiative. That took several rounds of artifact requests, discovery calls, and validation — each round adding another layer of evidence rather than taking the first answer at face value.
What they walked away with
Executives and department leaders walked away with a maturity assessment and an opportunity analysis broken down to the department and team level, with owners and concrete next steps assigned against each priority. The headline value: a short list of things they could act on immediately to mature their highest-priority AI use cases, alongside a clear picture of where the business was carrying AI risk it hadn't previously named.
At a glance
- Maturity assessment broken down to the department and team level — not just an enterprise score
- Every priority leaves with a named owner and a next step
- Surfaces where AI risk was carried but never named, alongside where to mature fastest
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