Win accounts through governance.
AI governance is the easiest, most-wanted door into an enterprise right now — every board is asking for it, and we can actually deliver it. Open that door, and the discovery it forces hands us the whole account. This is the motion, the proof, the ecosystem we plug into, and the live pipeline.
Lead with governance. Win the account.
We don't sell AI governance as the prize. We use it as the wedge — the lowest-friction, highest-urgency way into an enterprise — and the discovery it forces earns us the much larger MCG/Kelly relationship.
The thesis in one line: governance is the cheapest, most credible door into an enterprise right now — and walking through it hands us their entire technology estate.
Why this is the best lead-in on the table.
- It's top of mind for everyone. Boards, regulators, and CISOs are all asking the same question — "is our AI governed?" — and most companies don't have an answer. It surfaces unprompted in account conversations across our book, week after week.
- They need it anyway. This isn't a manufactured need. Every company scaling AI has to govern it. We're selling something they already know they're missing.
- No one says no to "let's make sure your AI is safe." It's a low-threat, high-trust opener — far easier than leading with a big transformation pitch.
- We can actually deliver it. The method and the tooling are built (section 04). This isn't a slide — it's a machine.
Every figure in this document is tracked in the sources & evidence registry — verified wording, fetched URLs, dates.
The real prize isn't the governance fee. It's the discovery.
To govern an organization's AI, you have to inventory everything — and that inventory is the most valuable account-intelligence MCG can hold.
What a governance engagement uncovers
- Their full software stack + shadow IT and shadow AI
- Where systems duplicate, where they're brittle, where they're stuck
- Their security gaps and modernization debt
- The bottlenecks and pain points leadership actually feels
What that opens for MCG/Kelly
- Application & database rationalization
- Modernization + cloud + DevSecOps
- Managed services and the operate contract
- Staff augmentation against the gaps we just mapped
Governance is discovery you get paid to run — and the client thanks you for it. We walk in as the trusted party that made their AI safe, and walk out knowing their estate better than they do.
This isn't a deck. The machine is built.
The method and the tooling exist today — repeatable, standard-mapped, and demonstrable in a live room.
govrn.ai is the standard, the method, and the platform. MCG/Kelly is the delivery organization that brings it to clients. One offering, two components: the standard keeps the work rigorous and consistent; MCG/Kelly makes it real at enterprise scale.
The framework
Governance across three lenses — technology, security, and AI itself — every control mapped to a recognized standard (NIST AI RMF, ISO 42001, OWASP, NIST CSF, EU AI Act).
The repeatable engagement
A productized assessment instrument + a 6-stage delivery playbook with built-in human-in-the-loop gates. Run it the same way every time.
The platform
A live dashboard — inventory, three-lens posture, AI-BOM, cost & efficiency, and monitoring — that turns the assessment into a living system, not a one-time report.
See it live: a working three-lens dashboard, a full method library, and a real first-engagement mirror are all in the internal index — walk the proof.
Where we win — and where others stop.
Advisory-only firms
- Assess and recommend a framework
- Hand over a policy and a gap report
- Then the work goes back to the client's teams
MCG / Kelly
- Design the operating model and build it
- Stand up the registry, controls, and monitoring
- Run it as a managed service — engineering and delivery capacity at scale
Honesty, mapped to standards. We mark what's real (monitoring isn't claimed live until it's connected), every control traces to a recognized standard, and we never self-certify — where formal certification is required, it runs through accredited third-party bodies, with our work producing the audit-ready evidence they need. For regulated buyers, that's the difference that closes.
Governed AI is efficient AI.
Three lenses see the estate. The meter prices what they see. The angle no one else is putting on the table: governance and cost-efficiency are the same discipline from two ends. You can't right-size models you haven't inventoried, or cut waste you haven't measured. The same governance that de-risks an organization's AI is exactly what makes it cheaper and faster.
What disciplined AI looks like at scale: Cloudflare (the internet-infrastructure company) ran 131,000+ AI code reviews across ~5,200 repositories in a single month, at a median cost of $0.98 per review (their published engineering data) — right-sized model tiers, token-caching, context discipline, compute scaled to risk. That's their result on their own infrastructure — we cite it as the ceiling disciplined usage reaches, not as our delivery claim. Our job is moving clients toward it.
The meter we add
- Model right-sizing — frontier models only where they earn it; workhorse + lightweight tiers everywhere else.
- Token-spend visibility — cost per model and per workflow; know who's generating the value.
- Context & caching discipline — stop paying to re-send the same context.
- Risk-tiered compute — don't send the dream team to review a typo.
Why it changes the sale
- Governance stops being a cost and becomes a saver that pays for itself.
- It brings the CFO and CTO to the table — not just risk and compliance.
- The discovery finds the waste — immediate, quantifiable ROI that funds the rest.
It’s already in the product: the meter — our cost & efficiency read — runs in our live dashboard today. In the first engagement mirror it models ~$34K/month of AI spend with ~$15K/month recoverable — a modeled baseline from our own instrument, shown live in the walkthrough, and the template for every client conversation about AI waste.
The tooling ecosystem is our sensor grid.
Governance delivery sits on top of telemetry — tools that see what AI is actually being used across an enterprise. We treat discovery tooling as interchangeable sensors, and our layer — the framework, the assessment, the audit-ready evidence, the build-and-run — is the part no tool can own. Tools observe. We assess, decide, and stand behind it.
Due diligence: Nexthink AI Drive. Kelly teams are in discussions with Nexthink — a Gartner-leading digital-employee-experience platform (1,500+ enterprise customers; Vista Equity Partners announced a majority investment in 2025 at a ~$3B valuation) whose endpoint agents already run on millions of enterprise devices. Their AI Drive product (included at no extra cost for their Workplace Experience customers) points that telemetry at AI: which AI tools employees actually use, where shadow AI is spreading, and what adoption is worth. We've completed a full due-diligence read of the product and the category — sources on file. The conclusion:
What AI Drive does well
- GenAI / Copilot adoption tracking — its core strength
- AI tool inventory with allow/prohibit classification
- Shadow-AI discovery via network-domain mapping
- Productivity and ROI dashboards for the CIO
Outside its published scope — as of our June 2026 review
- Regulatory framework mapping (NIST AI RMF, ISO 42001, EU AI Act)
- Attestation — no tool signs the audit or faces the board
- Policy authoring or enforcement in the request path
- Token / spend cost telemetry — it measures value created, not dollars spent
The read: complement, not competitor. Nexthink built the telescope; we're the observatory. Its telemetry is a first-class discovery feed for exactly the inventory our assessment needs — and everything above that layer (framework mapping, risk decisions, audit-ready evidence, build-and-run) is ours. A potential partnership here is on the table and worth exploring deliberately: best-in-class telemetry underneath, our governance and delivery on top.
In discussion — nothing formalized. The diligence is done and shared here so the room has it; how far to take the relationship is one of the decisions this group owns.
Live conversations, shown as reference points.
These are potential leads — real companies, real conversations, nothing closed. Each knows a proposal or an outline of what we can do is coming. They're here so the room has concrete examples of the wedge in motion, not as bookings.
| Company | Who they are | Status | The angle |
|---|---|---|---|
| Grace Hill | SaaS provider of training, policy, and compliance software for real-estate / multifamily property management — actively embedding AI into fair-housing and compliance workflows. | Warm — in conversation Direct CTO relationship; governance discussed openly; proposal forthcoming. |
A company-specific governance posture is already built in our platform. Ready to convert to a scoped paid baseline. |
| BCBS of Illinois | Blue Cross Blue Shield of Illinois — one of the largest health insurers in the U.S., part of the HCSC family. A heavily regulated enterprise scaling AI carefully. | Active — in conversation Multiple entry points into the account; outline expected. |
A member-first AI-governance point of view, in their brand, is built. A health plan governing AI before it scales is the textbook wedge. |
Why these two matter: one mid-market SaaS, one Fortune-500-scale regulated insurer — the wedge working at both ends of the market. Each is a governance entry into a much larger account relationship.
Assess → Design → Implement → Operate.
A clear path that starts small and scales into the account — and the back half (build + run) is where MCG is structured to win.
Assess
Inventory the AI estate across all three lenses; classify risk; map the stack, gaps, and pain. The wedge + the discovery.
Design
Operating model, decision rights, policy, intake gate, and the control set.
Implement
Stand up the registry, controls, and guardrails. Where MCG builds.
Operate
Continuous monitoring, board cadence, audit readiness — run as a managed service. Where MCG stays.
The commercial shape: a fixed-fee baseline opens the door · scoped design-and-implementation grows it · the managed-service run-rate is the annuity. Specific pricing and margin targets get sized with this group — that's part of what section 10 asks for.
What we're asking for.
- Green-light the governance-led motion as an MCG/Kelly go-to-market play.
- Backing to convert the reference leads — the SaaS conversation into a scoped paid baseline, and the health-plan conversation into a working session.
- A small delivery pod (advisory lead + SecOps + engineering) to run the first engagement well and prove the model.
- This group's input on the commercial model — deal shape, pricing, and margin targets for the three-phase motion. The mechanics are built; the sizing is a leadership decision we want made together.
Let's win accounts through governance. The method is built, the tooling is live, the ecosystem read is done, and the pipeline is warm. The green light makes it a business.
The program, versioned.
This program versions forward like a product. The full record stays available — every version, what it added, and who drove it. One ladder, two kinds of milestones: program builds (v0.8, v0.9) and the pitch itself (v1.0) — the earlier pitch draft is archived from the v0.9 era.