Job descriptions
Every agent gets a defined role: what it does, what it reports to, and where it stops. No agent runs without one.
§ 0 AI governance consulting
Governance isn’t a document you buy. It’s a system you operate.
Most AI governance consulting ends with a policy in a shared drive. Ours ends with a working operating model: defined agent roles, permissions, approval gates, and the metrics that prove AI is under control in your firm.
§ 1 The premise
What is AI governance consulting?
AI governance consulting helps a firm put rules, oversight, and accountability around how it uses AI: who can use which tools, what data they touch, how outputs get reviewed, and who answers for it when something goes wrong.
The trouble is that most of it stops at a document. A policy governs nothing if the work doesn’t route through it. The version worth paying for ends in a system your people run every day, with the gates and metrics built into the work itself. That’s the only kind we do.
§ 2 Paper vs. operated
A policy on paper vs. governance you operate
The risk isn’t that your firm misses AI. It’s that AI enters your firm without an operating model. Here’s the gap between what most governance engagements deliver and what actually keeps AI in line.
Governance · the gapWhat ships vs. what governs
- A policy document and a committee An operating model your people run every day
- A risk framework that sits in a shared drive Permissions, approval gates, and review rules wired into the work
- A maturity score and a slide deck An edit-rate metric and an audit trail you can show a client
- Governance as a project that ends Governance as a system someone owns and keeps tuning
§ 3 The controls
What governed AI actually looks like
We govern AI agents the way you’d manage junior staff: clear roles, limited permissions, review before anything goes out, and metrics on the work. Six controls turn “responsible AI” from a value into a system.
Permissions
A clear matrix per agent — what it may read, what it may draft, what needs approval, and what it may never touch.
Approval gates
AI prepares the work. A human approves anything that leaves the firm or changes a record. The gate is the point of the system, not a slowdown.
Review cadence
Outputs flow through a review queue with states you can see: pending, approved, edited, escalated. Nothing ships unseen.
Edit-rate metrics
We measure how often a human has to correct the AI. It tells you whether the system is earning trust or burning it.
Escalation paths
When an agent hits something outside its bounds, it stops and routes to a named person. Exceptions are handled, not hidden.
§ 4 Maturity model
Find where your firm sits — and the next move
A maturity model is only useful if it points somewhere concrete. This one ends in a single decision: the first workflow to govern.
Stage 0 Ungoverned AI is already in the firm without rules. Confidential work may be flowing through unmanaged tools. This is the exposure most firms are in and don’t name.
Stage 1 Policy on paper You have an acceptable-use document and good intentions. It governs nothing in practice, because the work doesn’t route through it.
Stage 2 Governed in one workflow One real workflow runs through permissions, an approval gate, and a review queue, with a metric on it. Governance you can actually see.
Stage 3 Operated at scale The governed model extends across workflows, with an owner, an audit trail, and tuning over time. AI runs the way a well-managed team runs.
§ 5 Questions
AI governance consulting FAQ
What is AI governance consulting?
AI governance consulting helps a business put rules, oversight, and accountability around how it uses AI — who can use which tools, what data they touch, how outputs get reviewed, and who is responsible when something goes wrong. The useful kind ends in a system your people operate, not just a policy document.
What does an AI governance specialist do?
They define the operating model for AI in a firm: agent roles and permissions, approval gates for anything client-facing, review and escalation rules, and the metrics that show whether the system is trustworthy. At LanternOps, we don’t stop at advising — we build and operate that governed system.
How is AI governance different from responsible AI?
Responsible AI is the principle: humans stay accountable, sensitive work stays protected, judgment stays human. Governance is how you make that real in daily work — the permissions, gates, reviews, and metrics that turn the principle into something a firm actually runs. See our responsible AI approach for the principles behind it.
Do we need AI governance if we’re a small firm?
Especially if you’re small. AI usually enters a firm before anyone decides it should, and a 20-person firm carries the same client-confidentiality and quality obligations as a large one with none of the compliance staff. A light operating model beats a heavy policy nobody follows.
What is an AI governance maturity model?
It’s a way to place where your firm sits — from ungoverned, to policy-on-paper, to governed in one workflow, to operated at scale. It’s useful only if it points to the next concrete move. Ours does: it tells you the one workflow to govern first.
How much does AI governance consulting cost?
It varies by scope, but the LanternOps model is built to be legible: we price an engagement the way you’d price a hire, not an open-ended advisory retainer. The fastest way to a real number is a workflow diagnosis, where we scope the first governed workflow with you.
§ 6 Start here
Put AI under an operating model.
Start with a workflow diagnosis. We’ll find the one workflow worth governing first and scope what it takes to run it safely — no policy binder required.
Proof over promises: the Operator’s Log publishes real numbers from running supervised AI Teams across five ventures. What shipped, what it cost, what we had to fix.