Strategy that survives board and legal review
A credible AI strategy names the decisions you are improving, the data you trust, and what you will measure in production — not a vague “transformation” pledge.
Diagnostics before roadmaps
We start with shadow-AI inventory, data-flow reality, and sponsor alignment. That yields a ranked backlog where each item has an owner, an evaluation plan, and an explicit risk class — the same ingredients regulators and customers ask for when models touch material workflows.
Buying vs building in regulated contexts
Enterprise platforms, open models, and specialist vendors all have a role. The strategic question is where you need a model of record, where retrieval is enough, and where human-in-the-loop is non-negotiable. We map those decisions to governance and retrieval architecture.
Frequently asked questions
How long until we have a usable strategy artifact?
Most teams need two to four weeks of structured interviews and stack review before a board-ready narrative is honest. Shorter decks usually hide open questions about data rights and operational ownership.
Should AI strategy be enterprise-wide or domain-specific?
Define enterprise-wide principles (logging, access, vendor due diligence) once, then prioritize domain roadmaps where P&L or risk concentration is highest.
How do we align business cases with model risk expectations?
Tie each initiative to failure modes, monitoring signals, and rollback paths. If you cannot state what “wrong” looks like numerically, the business case is incomplete.
Next step: a fixed-fee diagnostic.
Three weeks. Board-ready brief. Ranked opportunities. No discovery theatre.
Book a diagnostic →