Govern AI before you scale it — then build and run it.
govrn.ai is an AI-governance engagement designed to go beyond the framework. It's built to design the operating model, implement the controls, and operate the system of record — the layer most advisory firms aren't structured to deliver. The governance foundation, plus the firm that builds against it.
What govrn.ai is.
?Why this is here
Every organization's first AI decision is governance — what AI it will use, what it won't, who decides, how risk is measured, what evidence it keeps. govrn.ai is the engagement that answers those questions and then operationalizes the answers.
Before a company uses AI widely, it has to decide what it will and won't use, who signs off, and how it proves the AI is safe. govrn.ai answers those questions — then builds and runs the system that keeps the answers true. Not a report that ages on a shelf: working controls, maintained over time.
govrn.ai runs in four phases — Assess → Design → Implement → Operate — the canonical market sequence. But the differentiator is the back half. Anyone can hand a client a policy and a gap assessment. govrn.ai's value is that MCG then builds the registry, the intake gate, the monitoring, and the GRC integration — and can run it as a managed service.
That matters because the market has moved past pure advisory. A one-time governance document ages out the moment a model drifts or a new tool shows up in procurement. The credible posture today is advisory to design the operating model, plus a system of record to operate it continuously. govrn.ai is built for that posture.
The deliverable isn't a document. It's the governance foundation plus the firm that builds and operates against it — assessed, framed, implemented, and monitored, with one accountable partner across the whole arc.
One-line positioning: govrn.ai governs AI before you scale it — and is the firm that builds and runs the governed AI afterward. Advisory firms stop at the framework; govrn.ai delivers the framework and the engineering.
The rationale — and why this shape wins.
?Why this is here
"Do you have AI governance?" is now a board-level question — one every technology and security leader has to answer to their board, their auditors, and their insurers. If a client has an answer, we assess it; if they don't, that gap tells us exactly what to build. And we stay honest about who MCG is: a firm that builds and operates technology, not an audit firm.
It's a discovery-led entry point. "Do you have an AI governance framework?" is a question every CIO, CISO, and CAIO now has to answer — to their board, their auditors, and their D&O carrier. If they have one, we assess it. If they don't, that gap is the discovery: it tells us exactly what to build, and it opens the door to the rest of MCG's practice (data, cloud, DevSecOps, managed services).
It's honest about what MCG is. MCG is an IT consulting and delivery firm, not an audit firm. We shouldn't position govrn.ai as pure governance advisory and try to out-credential the Big 4 or the assurance firms — we'd lose that bake-off. Our moat is that we build and operate. govrn.ai leads with governance because that's the entry point, but it's anchored to MCG's actual strength: turning the framework into shipped, monitored, governed AI.
It's built to survive a Risk/Compliance review. A standalone policy plus a one-time gap assessment — with no operating model, no intake gate, no monitoring, no framework crosswalk — is exactly what an internal AI risk lead flags as generic. govrn.ai is deliberately the opposite: a named, phased methodology with a real deliverable set, an explicit framework crosswalk, and a system-of-record posture.
We're entering a validated, growing market — with a differentiated posture.
?Why this is here
AI governance as a service is real, funded, and consolidating. We're not inventing a category; we're entering one with a delivery advantage.
Helping companies govern their AI is already a real, growing market — big consulting firms and software vendors are competing for it right now. We're not betting on a maybe; we're entering a proven market with one edge most of them lack: we can build and run the system, not just advise on it.
Who sells this today: Deloitte ("Trustworthy AI," 7 dimensions) · KPMG ("Trusted AI," 10 pillars — first Big 4 to ISO/IEC 42001: KPMG Australia Oct 2024, KPMG International Dec 2025) · PwC ("Responsible AI," assurance-led) · EY (AI Governance + ServiceNow alliance, CDO-as-a-Service style) · IBM (watsonx.governance + consulting) · Accenture (asset-led). Plus specialized platforms: Credo AI, Holistic AI, Monitaur, Saidot.
The market's center of gravity is hybrid: advisory to design the operating model + a system of record to run it. Pure advisory reads as a one-time deliverable; pure platform reads as "a documentation tool." The hyperscalers are absorbing governance too — Azure AI Foundry now ships prebuilt integrations to Credo AI, Saidot, and Microsoft Purview.
Where govrn.ai fits: not as another framework vendor, but as the firm that designs the framework and engineers the system of record around it — registry, intake, monitoring, GRC integration — then operates it. That's the hybrid posture the market rewards, delivered by a firm whose core competency is building and running, not just advising.
Assess → Design → Implement → Operate.
?Why this is here
The canonical market sequence, with realistic durations. govrn.ai's front half mirrors what every credible firm does; the back half is where MCG's delivery muscle separates it.
Four steps: see where you stand (Assess), write the rules and decide who approves what (Design), build the actual tools and guardrails (Implement), and keep watch as things change (Operate). Most firms stop after the first two. MCG's strength is the last two — building it and running it — which is also where the steady, recurring revenue lives.
Assess
Gap analysis vs target framework, AI inventory discovery, risk classification, regulatory-exposure mapping.
Design
Operating model + RACI, AI policy, use-case intake/review process, risk taxonomy, decision rights.
Implement
Stand up the model registry, intake workflow, and controls; integrate into existing GRC; staff training. MCG's build muscle.
Operate
Continuous drift/bias/operational monitoring, dashboards, board reporting cadence, audit cadence. Recurring managed service.
Where the recurring value concentrates: the Big 4 and the platforms are strong on Phases 1–2. Phases 3–4 — implementation and run — are where MCG's engineering and managed-services heritage win, and where recurring revenue lives. govrn.ai should be priced and pitched so the assessment opens the engagement, not the whole of it.
The criteria govrn.ai has to satisfy.
?Why this is here
These are the recurring evaluation points across enterprise buyer guides. A Risk/Compliance reviewer will probe every one of them — govrn.ai is built to answer all seven.
When a large company shops for AI governance, buyers grade every vendor against the same checklist. Here's that checklist — and govrn.ai is built to tick all seven boxes.
1 · Multi-framework coverage in one place. EU AI Act, ISO/IEC 42001, NIST AI RMF, GDPR, sector regs — natively crosswalked, not siloed per-framework.
2 · Continuous monitoring, not one-time. Pre-deployment conformity is necessary but insufficient — models drift, data shifts. Runtime monitoring is a hard requirement.
3 · System of record, not a documentation tool. A registry + intake + evidence trail, not a static policy PDF.
4 · Integration with existing GRC. ISO 42001 is deliberately aligned to ISO 27001/9001 — governance plugs in, doesn't stand alone.
5 · Auditability & evidence. Model cards, AI bills of materials, approval records, monitoring reports, incident logs — auditor-defensible artifacts.
6 · Board-readiness. Even as oversight disclosure rises, only ~15% of boards rate the AI metrics they receive as adequate (NACD / McKinsey). govrn.ai produces the board reporting that closes that gap.
7 · Fit to AI estate & regulatory exposure. The right scope is driven by estate size and regulatory exposure — govrn.ai is sized per engagement, not one-size-fits-all.
Exactly what the client gets.
?Why this is here
The artifact set is table-stakes — covering all of it is what separates a credible offering from a generic one. Then govrn.ai adds the implementation and run layer that advisory firms don't.
Here's literally what the client receives. The first list — written policies, a master inventory of every AI system, a risk register, and an approval process — is what any credible firm provides. The green list — actually standing those up as working systems and running them — is what sets MCG apart.
Governance artifacts (Phases 1–2):
- AI Policy — enterprise acceptable-use + principles
- AI Inventory / Model Registry — living register of every tool, agent, vendor model, dataset
- Risk Register + classification — per-system risk tiering on a taxonomy
- Use-Case Intake / Review process — the approval gate before production
- RACI / decision rights — CAIO + CISO + CPO + Data Gov operating model
- Model cards + AI Bill of Materials — a spec sheet and parts-list for each AI system, plus approval records
- Audit cadence + Board reporting templates
- Vendor / third-party AI risk + training programs
The MCG layer (Phases 3–4) — the differentiator:
- Stand up the registry + intake workflow as working systems, not templates
- Build the controls + monitoring — drift, bias, operational risk
- Integrate into existing GRC stack
- DevSecOps guardrails for the AI delivery pipeline
- Managed service to run the monitoring + reporting on an ongoing basis
This is what EisnerAmper and the Big 4 advisory practices aren't structured to deliver — and it's MCG's core competency.
Not just generative AI.
?Why this is here
This isn't only about ChatGPT-style tools. It covers all the AI a company relies on — predictive models, automated decision systems, AI baked into vendor software, and the newer "agentic" systems (AI that takes actions on its own, not just answers questions).
The internal reviewer asked the right question: is this only generative AI, or does ML and deterministic automation factor in? govrn.ai's scope is deliberately broad, because the governance gap is broad:
- Generative AI / LLMs — chatbots, copilots, RAG systems, agentic workflows
- Machine learning broadly — predictive models, forecasting, classification, recommendation engines
- Deterministic / automated decision systems — rules engines that make consequential decisions (hiring, credit, eligibility)
- Vendor-embedded AI — AI features inside third-party SaaS where the client is the data controller
- Agentic AI — autonomous multi-step systems, the fastest-growing and least-governed category
Scope per engagement is set during Assess, tied to the client's actual AI estate and regulatory exposure. The framework crosswalk (next section) covers all of these.
ISO 42001 spine · NIST AI RMF method · EU AI Act overlay.
?Why this is here
The frameworks are complementary, not competing. govrn.ai's crosswalk: ISO/IEC 42001 as the management-system spine, NIST AI RMF as the risk methodology, the EU AI Act as the product/legal overlay, plus sector regs for regulated clients.
There are several AI "rulebooks" — an international standard, a US risk method, and EU law — and they stack rather than compete. We use the international standard (ISO 42001) as the backbone, the US method (NIST) to measure risk, and EU law as the legal layer — then map every deliverable to the exact rule it satisfies. That mapping (the "crosswalk") is what separates a real offering from a generic one.
| Framework | What it is | Status |
|---|---|---|
| ISO/IEC 42001:2023 | Certifiable AI Management System; aligned to ISO 27001/9001 — the umbrella | DE FACTO via RFPs |
| NIST AI RMF | Govern / Map / Measure / Manage — risk methodology | VOLUNTARY · US baseline |
| EU AI Act | Risk-tiered product law; extraterritorial. Prohibited practices since Feb 2025; high-risk obligations Aug 2, 2026 (phased to 2027–28) | MANDATORY |
| SR 26-2 (Fed/OCC/FDIC) | Banking model-risk mgmt (Apr 2026; supersedes SR 11-7). Explicitly excludes GenAI/agentic — a live gap (RFI forthcoming) | SUPERVISORY GUIDANCE (banks) |
| HIPAA / FDA CDS / 21 CFR Part 11 | Health data + clinical AI | MANDATORY (health) |
| Colorado AI Act | First US state AI law; rewritten by SB 189 (signed May 2026) — effective Jan 1, 2027; algorithmic-discrimination duty repealed, narrowed to disclosure/transparency | EVOLVING (CO) |
Why this matters for credibility: a values-statement framework with no regulatory crosswalk is the #1 generic tell. govrn.ai maps every deliverable to a named clause/function across these frameworks — and flags live gaps (like SR 26-2 putting GenAI/agentic out of scope) that show genuine currency.
Implement vs. not — the business case in hard numbers.
?Why this is here
govrn.ai is a D&O and balance-sheet de-risking instrument, not a compliance cost center. The case rests on four pillars: regulatory fines, litigation, insurance, and personal officer/board liability.
The business case is money. Skipping governance exposes the company to regulatory fines, lawsuits, worse insurance terms, and personal liability for executives and board members ("D&O" = Directors & Officers, the insurance that protects them personally). The cards below put real numbers and real court cases behind each.
Regulatory penalties (real, current):
- EU AI Act: up to €35M or 7% of global turnover for prohibited practices — vs GDPR's 4% / €20M ceiling
- Clearview AI: >$30M fine (2024, Netherlands DPA)
- Colorado AI Act: rewritten by SB 189 (May 2026) — effective Jan 1, 2027, now disclosure-focused; a live example of fast-shifting state AI law
Litigation precedent:
- Air Canada (Moffatt v. Air Canada, Feb 2024): held liable for its chatbot's misinformation; the tribunal rejected the argument that the chatbot was a separate entity responsible for its own answers
- iTutor (EEOC): $365K settlement — recruiting AI auto-rejected by age
- Mobley v. Workday: AI hiring-discrimination claims proceeding under agency theory — deployer liability is live
Insurance & board pressure:
- >90% of businesses now want insurance cover for generative-AI risks (Geneva Association, 2025); D&O underwriting increasingly probes AI-governance maturity — stronger governance supports cleaner terms + more capacity
- Board AI-risk oversight disclosure tripled 2024→2025 (16% → 48%, Fortune 100; EY)
Shareholder exposure:
- 53 AI-related securities class actions (Mar 2020 – Jun 2025, Stanford SCAC) — one of the fastest-growing event-driven categories
- SEC enforcement against "AI-washing" — false AI claims
The pitch in one line: A CAIO/CISO buys govrn.ai to (a) avoid 7%-of-revenue exposure, (b) defend against the fastest-growing class of shareholder suits, (c) earn favorable D&O terms, and (d) give the board the AI oversight metrics most boards still lack. Not implementing governance is the expensive choice.
Governance that pays for itself.
?Why this is here
Governance isn't only insurance against bad outcomes — done right, it makes AI cheaper and faster to run. Clear rules mean fewer stalled pilots, less duplicated tooling spend, and lower day-to-day running costs.
Governed AI isn't slower AI — it's AI that actually ships and scales. The same controls that reduce risk also remove the friction that strands most AI initiatives before they reach production.
Faster to production. A single intake gate plus reusable, pre-approved controls turn one-off approvals into a repeatable pipeline — so pilots stop dying in committee. Analysts have reported that a large share of enterprise AI pilots never reach production; a governed path attacks that directly.
Lower run cost — FinOps for AI. A model registry plus monitoring surface duplicated tools, idle models, and runaway token/compute spend. Optimizing usage and cost is a natural Phase-4 add-on — and a direct answer to the reviewer's token-usage / ROI question.
Less rework, fewer incidents. Catching a risky use case at the intake gate is far cheaper than remediating a model already in production — or unwinding a public failure after the fact.
Faster sales & procurement. Demonstrable governance shortens the security and procurement reviews that gate enterprise deals — governed vendors clear the gate faster.
The two-sided case: the prior section shows what governance prevents you from losing; this shows what it helps you create — faster delivery and materially lower operating cost. That "optimize production while reducing operational cost" framing is how enterprise leadership funds a program, not just files a policy.
The operating model it leaves behind.
?Why this is here
What the client is left running: a small cross-functional team that approves new AI before it goes live, one master list of every AI system in use, and a regular reporting rhythm to the board — all plugged into the risk processes the company already has, not bolted on beside them.
govrn.ai doesn't just produce documents — it stands up a working governance operating model that plugs into the client's existing risk infrastructure:
- A joint review body — CAIO + CISO + CPO + Data Governance jointly reviewing production-intent AI, with documented decision rights (RACI).
- An intake gate — the critical governance primitive: a structured approval workflow capturing use case, data sources, intended users, potential harms, and risk class before anything reaches production.
- A model registry as system of record — every AI system inventoried, tiered, owned, and tracked through its lifecycle.
- GRC integration — because ISO 42001 is aligned to ISO 27001/9001, govrn.ai slots into existing risk management rather than creating a parallel stack.
- An audit + board cadence — what's measured quarterly, re-certified annually, and reported to the board.
Customer Zero — the credibility move: KPMG's own ISO 42001 certification is the model. If MCG/Kelly adopts govrn.ai's framework internally — "we govern our own AI exactly this way" — that's the single strongest proof point we can offer a prospect. Recommendation: run govrn.ai on ourselves first. It also directly answers the question "if we get asked about our own AI governance, can we speak to it?"
Where govrn.ai wins — and where it teams.
?Why this is here
The Big 4 and assurance firms are strong on governance advisory. MCG should not fight them there. MCG wins on what they structurally can't do: build and run.
The big audit/accounting firms are strong at advising on AI and independently certifying it — but their independence legally bars them from building the thing they certify. MCG builds and runs. So a firm like EisnerAmper is a partner, not a rival: they assess and attest, we implement and operate.
Audit / advisory firms (Big 4, EisnerAmper)
- Assess, govern, and independently ATTEST
- Audit-grade frameworks, certifications, case studies
- Independence is their product — they can't build the AI they assess
- Strong on Phases 1–2 (assess + design)
MCG / govrn.ai
- Design, IMPLEMENT, and OPERATE the governed AI
- Engineering + managed-services heritage + scaled talent bench
- Can't self-attest our own builds — but that's fine, that's the partner's job
- Strong on Phases 3–4 (build + run), where the moat and the recurring revenue are
EisnerAmper — PARTNER, not competitor (both at LEC 2026). EisnerAmper is a Top-15 accounting firm whose AI offering runs on audit/assurance DNA (third-party assessment and attestation, not engineering). Their independence structurally prevents them from building and running the AI they assess; MCG can't credibly self-attest its own builds. Each firm's strength is the other's blind spot — textbook teaming. Partnership shape: EisnerAmper assesses + attests; MCG implements + operates; bidirectional referral. At LEC, lead with "you assess and attest, we build and run."
The one contested sliver: pure "governance advisory." If govrn.ai pitches head-to-head as governance advisory, audit firms out-credential us. The move is to absorb that sliver into the build/run story — govrn.ai governs so that MCG can build and operate — rather than fight on advisory turf.
For review by Kelly/MCG's AI governance stakeholders.
?Why this is here
AI governance here is owned across InfoSec, Digital Worker Experience, and Risk & Compliance. We've cleaned up the obvious items before bringing this forward — these are the substantive questions where that group's input shapes the offering before it goes anywhere near a client.
These are the open decisions we'd like the governance team to weigh in on — the substantive calls that shape the offering before anything goes near a client.
- Deliverable boundary. We've drawn the line at: governance artifacts (Phases 1–2) + implementation + managed-service operation (Phases 3–4). Is that the right scope, or should attestation/assurance be explicitly excluded and routed to a partner?
- AI scope confirmation. We've scoped govrn.ai to cover generative, ML, deterministic decision systems, vendor-embedded, and agentic AI. Anything to add or carve out?
- Customer Zero. Do MCG/Kelly have an internal AI governance framework today? If yes, govrn.ai should mirror it ("we run this on ourselves"). If no, should standing one up be a prerequisite to going to market?
- Framework crosswalk depth. Is the ISO 42001 / NIST RMF / EU AI Act / sector mapping sufficient, or do specific regulated verticals (financial SR 11-7, health HIPAA) need dedicated treatment before we sell into them?
- Partnership strategy. Do we formalize an assurance partnership (EisnerAmper or similar) so we can offer independent attestation we structurally can't self-provide?
- Token-usage / ROI consulting (raised in the first review) — worth folding in as a Phase-4 add-on, given tightening AI-tool margins?
- Brand & IP. The offering is branded govrn.ai (domain owned). Any IP or brand considerations for a Kelly public-company offering — including the domain-ownership structure — to address before external use?
How it fits together. govrn.ai is the standard, the method, and the platform; Motion Consulting Group (a Kelly Services company) 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. 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.