Most companies don’t have an AI strategy problem. They have an AI ownership problem. We fix that.
Your AI Initiative Doesn’t Need Another Strategy Deck
Every executive team we talk to has an AI strategy. Slides, pilots, a vendor shortlist, maybe a Chief AI Officer title bolted onto someone’s existing job. What almost none of them have is a single person accountable for turning that strategy into a governed, auditable, revenue-
generating operation.
That gap is why most enterprise AI initiatives stall between pilot and production. Not because the technology fails, because no one owns execution, no one owns risk, and no one is answerable when the board asks what actually shipped.
The Cost of AI Without Governance
Ungoverned AI adoption doesn’t fail quietly. It shows up as an enterprise customer’s security questionnaire you can’t answer, a regulator’s request you weren’t prepared for, or a board member asking who signed off on a model your team put into production eight
months ago and no one can now.
For SaaS, FinTech, and InsurTech companies specifically, the exposure compounds fast:
Procurement risk — enterprise buyers increasingly require documented AI governance before they’ll sign, and “we’re working on it” doesn’t close deals
Regulatory exposure — frameworks like Canada’s AIDA, the EU AI Act, and SR 11-7 are moving from guidance to enforcement, and retrofitting governance after the fact is far more expensive than building it in
Board and audit liability — an AI initiative with no named owner and no audit trail is a liability the moment anything goes wrong, technically or reputationally
Wasted technical spend — pilots that can’t get sign-off to reach production because no one built the governance case for them to
None of this requires a governance failure to become a problem. The absence of governance is the problem.
Embedded AI Leadership, Not Advisory
We don’t sit outside your organization and recommend. We embed inside it operator, transformation leader, and board advisor in one seat and take direct responsibility for how AI gets adopted, governed, and scaled across the business. The same model we apply as your Fractional COO, applied to AI: we don’t advise on outcomes. We take responsibility
for them.
This is delivered through the Velocity Framework™, in 90-day execution cycles the same methodology built and pressure-tested across enterprise transformation engagements, now applied to AI adoption specifically: governance first, execution second, scale third. No cycle
moves forward until the one before it is provably done.
How It Works: The Velocity™ Framework Applied to AI
Every engagement runs in 90-day cycles. Each cycle has a single gate: it doesn’t move forward until the deliverable before it is provably done not “mostly done,” not “in progress.” This is the same discipline we apply to every transformation engagement, sequenced for AI specifically.
Cycle 1: Governance Baseline
We run the AI Governance Command Center assessment across all seven dimensions to establish where you actually stand against NIST AI RMF, the EU AI Act, ISO 42001, SR 11-7, and the frameworks relevant to your sector. You leave this cycle with a documented baseline, a prioritized gap list, and a board-ready report not a slide deck of recommendations with no owner attached.
Cycle 2: Execution and Controls
We close the highest-priority gaps identified in Cycle 1; policy, approval workflows, model documentation, vendor risk controls and get stalled pilots unstuck by building the governance case they need to reach production. This is embedded operator work, not advisory: we’re accountable for the controls existing, not for recommending that they
should.
Cycle 3: Scale and Audit-Readiness
With governance and controls in place, we extend the operating model across additional AI use cases and prepare the organization to withstand an actual audit, regulatory inquiry, or enterprise procurement review not just look good in a self-assessment.
Engagements typically run multiple consecutive cycles. Some companies exit after Cycle 1 with a governance baseline in hand; most continue into ongoing fractional leadership once they see what actually surfaces.
How Engagements Are Structured
AI Governance Command Center
The AI Governance Command Center is our structured, board-ready AI governance assessment tool built to show you where your AI governance actually stands, not where you assume it stands. Thirty-five questions across seven dimensions, mapped directly to the frameworks your auditors, regulators, and enterprise customers will eventually ask
about: NIST AI RMF, the EU AI Act, ISO 42001, SR 11-7, NYC Local Law 144, the OECD AI Principles, and Canada’s AIDA.
You get a defensible governance baseline in weeks, not the multi-quarter engagement a Big Four firm will sell you to arrive at the same answer.
Fractional AI & Transformation Consulting
Ongoing embedded leadership for companies that need AI adoption run like an operating discipline, not a side project. This is the same retainer model as our Fractional COO engagements; one accountable operator, integrated with your existing leadership team, running 90-day cycles against a governance-first roadmap.
Board & Executive Advisory on AI Risk and Strategy
For boards and executive teams who need a credible, operator-grade perspective on AI risk, governance exposure, and realistic execution timelines without a vendor’s product to sell you at the end of it.
Why Embedded Beats the Marketplace Model
Most “AI consulting” in this market is a bench a firm sells you access to a pool of consultants, none of whom carry the outcome personally. We built TopFractionalExecs specifically as the opposite of that. Every engagement is delivered directly, by one operator, who is still in the room in week twelve when the governance report is due to the board.
For SaaS, FinTech, InsurTech, and Financial Services companies in particular, that distinction isn’t cosmetic. Your regulators and enterprise customers don’t accept “the AI team handled it” as an answer. They want a name attached to the decision.
Who This Is For
- Scaling B2B SaaS companies whose AI pilots haven’t survived contact with production, compliance, or the board
- FinTech and InsurTech companies that need AI governance mapped to regulatory frameworks before their next audit or enterprise sales cycle
- Financial Services organizations under SR 11-7 or equivalent model-risk expectations who need AI initiatives run with the same discipline as any other regulated change program
- Boards that want an operator’s assessment of AI risk exposure not a vendor’s pitch
Frequently Asked Questions
What’s the difference between AI management consulting and AI strategy consulting?
Strategy consulting ends at the recommendation. AI management consulting, as we run it, ends when the governance structure is in place, the initiative is in production, and someone is accountable for how it got there.
Do you implement the AI itself, or just the governance and leadership around it?
We own execution and governance, the operating discipline that determines whether AI adoption succeeds. We work alongside your technical teams and vendors rather than replacing them; our job is making sure their work ships inside a governed, accountable
structure.
How is this different from hiring a Chief AI Officer?
A fractional model gives you the same seniority and accountability without a full-time executive hire, and without the ramp-up time. It’s the same logic as a Fractional COO engagement, applied to AI leadership specifically.
What frameworks does the AI Governance Command Center assess against?
NIST AI RMF, the EU AI Act, ISO 42001, SR 11-7, NYC Local Law 144, the OECD AI Principles, and Canada’s AIDA; the frameworks most likely to come up in a regulatory review, audit, or enterprise procurement process.
How long does an engagement take?
Work is structured in 90-day Velocity™ Framework cycles. The governance assessment itself can be completed in weeks; ongoing fractional leadership engagements typically run in successive 90-day cycles for as long as active transformation is underway.
We already have a data science or ML team. Why would we need this?
Technical teams build models. They’re rarely mandated, resourced, or positioned to own governance, regulatory mapping, or the accountability structure a board or enterprise customer expects. This engagement sits above and alongside your technical team it
doesn’t replace their work, it gives it a governed structure to ship inside.
What does the AI Governance Command Center assessment actually produce?
A documented governance baseline scored across all seven dimensions, a prioritized gap list, and a board-ready report mapped to the specific regulatory frameworks your sector faces something you can hand to an auditor, regulator, or enterprise procurement team as evidence of due diligence.
Is this only for companies already using AI in production?
No. Companies at the pilot stage benefit the most building governance in before scaling is significantly cheaper than retrofitting it after a regulator, auditor, or enterprise customer
asks for it.
Start With an Honest Assessment of Where You Stand
Book a call to walk through your current AI governance posture and where the gaps actually are not where a vendor wants them to be.