Responsible AI consulting · AI governance

What is responsible AI consulting?

Responsible AI consulting turns policy language into operating practice. It defines which tasks staff may hand to AI, which require human review, and which are prohibited. The work produces written boundaries, review owners, escalation paths, and documentation a team of fifteen can actually follow and defend later.

Discuss governance support 678.397.8411

Four professionals reviewing a workflow map, documents, and a tablet together at a meeting table. — Governance holds when a named person reviews the work, not when a policy exists on paper.

Readiness

Where AI use is already happening, and where it is not ready.

Boundaries and policy

Written rules staff can actually follow.

Implementation

Workflows, named owners, and human review.

Measurement

Whether the change held after the training ended.

The difference between an AI policy and AI governance

A policy states intent. Governance decides who does what, when review happens, and what gets recorded. Most organizations have the first and not the second, so staff improvise and the audit trail becomes a log nobody reads.

A three-level task boundary

Tier	Meaning	Control
Tier 1	Safe and low risk. No sensitive information, low consequence if wrong.	Approved use list. Spot checks.
Tier 2	Useful but consequential. Public-facing output, records, or judgment calls.	Named human review before release.
Tier 3	Prohibited or specialist only. Sensitive, legal, clinical, or identifying information.	Blocked, or routed to a designated owner.

How an approved task moves

Each stage has a named owner and a date. An engagement covers readiness, written boundaries, human review, escalation, and the review calendar that keeps them current.

Approved use
Human review
Escalation
Documentation and ownership

Evidence boundary

This work is educational and operational. It does not replace legal, compliance, or clinical advice.

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Questions buyers ask

Direct answers

What is human-in-the-loop AI governance?

It is governance that names a person, not a principle. A specific role reviews defined output before it reaches the public or the record, and that review is documented.

Who should review AI-generated work?

The person already accountable for the output. Governance should follow existing responsibility rather than create a new committee.

When should an employee escalate an AI task?

When the task involves sensitive or identifying information, a legal or clinical question, an unfamiliar tool, or an output they cannot verify.

How should an organization classify AI use by risk?

By consequence, not by tool. Sort tasks by what happens if the output is wrong, then apply Tier 1, Tier 2, or Tier 3 controls.

Next step

Discuss governance support.

A twenty-minute scoping call confirms audience, current tool use, and whether a bounded governance engagement is the right first step.

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