Which AI use cases to fund. In what order. And how each one clears model risk, audit, and regulatory review — before you spend a dollar building. For executives at banks, insurers, capital markets and healthcare firms.
Every deliverable is written to be forwarded — to your board, your CFO, your model risk team, or your examiner — without you having to translate it first.
Your top AI opportunities — typically 8 to 12 — scored on expected value, feasibility, data readiness, and regulatory exposure. Ranked so the sequence is defensible, not political.
Where your current posture stands against model risk expectations (SR 11-7 / SR 21-8), audit requirements, and — where applicable — EU AI Act obligations. The gate that kills pilots, mapped before you hit it.
Sequenced first moves: what to build vs. buy, a vendor shortlist where relevant, owners, dependencies, and the measurement frame that connects the work to the P&L.
A live working session with your leadership team to walk the findings, pressure-test the sequence, and answer the questions your board will ask — so you present it with confidence, not hope.
Structured conversations with 3–5 of your leaders across business, technology, risk, and operations. I'm listening for where value hides and where governance will bite.
Workflow and cost-driver review, data and systems readiness, current AI initiatives (including the stalled ones — those are diagnostic gold), and your governance posture.
Use cases scored, sequence built, build-vs-buy calls made, governance requirements attached to each initiative so nothing is a surprise at review.
Live readout with your leadership team, revisions from the discussion, and final delivery of all four artifacts. You own everything.
Your time commitment: roughly 6–8 hours of leadership availability across the two weeks. Everything runs remote.
The Review runs on Enterprise Intelligence Capital Theory (EICT) — the framework I published through iProDecisions Research. It measures your organization's Intelligence Conversion Rate: how much of the intelligence you generate actually reaches an operationalized decision. Most large enterprises convert less than 10%. The Review baselines yours, identifies where the conversion breaks, and sequences the fixes by return on intelligence capital — the same logic a board applies to any other capital allocation.
I do — all of it. No leverage model, no analysts learning on your engagement. You get 27 years of enterprise judgment applied directly: 19 years closing $8B+ in transformation deals at TCS Americas, and four years building production agentic AI for regulated environments as Co-Founder & Chief AI Officer of CAIBots.
Access, not homework: 3–5 leadership interviews (an hour each), an inventory of current and stalled AI initiatives, and whatever documentation exists on your data landscape and governance posture. If documentation is thin, that's a finding, not a blocker.
Yes. Mutual NDA before kickoff — yours or mine. No client-specific information appears in any research, case study, or marketing without written approval. Founding-cohort case studies are approved by you before publication and can be anonymized.
A straight trade, stated plainly: the first five Reviews are $4,500 instead of $7,500 in exchange for a written case study and a reference call. You get the identical engagement at 40% off; I get documented proof. When the fifth closes, the rate reverts.
You own the roadmap and can execute it with anyone. Some clients continue into the Fractional Chief AI Officer retainer ($5,000/mo) to drive execution; some take it to their internal teams or an implementation partner. The readout includes my honest recommendation on which path fits — including the ones that don't involve me.
Sometimes — it depends on interview availability, since leadership calendars are the long pole. Raise it on the fit call and I'll tell you honestly whether your timeline is achievable.
Bring your hardest AI question. You'll leave with a recommendation either way — even if the recommendation is that you don't need the Review.
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