AI governance consulting
AI governance consulting that decides what governance should exist
Sentient diagnoses where AI is creating obligations, risk, and reliance, then designs the governance architecture the company should run, with roles, decision rights, control requirements, and a sequenced roadmap.
For leadership teams that need to decide what governance to put in place before they invest in building it.
01
- Diagnose
- Design
- Decide
The core question
What governance system should this company have?
Most companies start AI governance from a policy template or a framework checklist. Neither says who decides, what must be controlled, or what to build first.
Consulting answers those questions for your systems, your obligations, and your stage. The result is a decision-grade design that leadership can approve, fund, and hand to implementation.
Decide what should exist. Then build what the decision requires.
What leadership leaves with
- An AI governance operating model
- Roles, ownership, and accountability
- Decision rights and authority limits
- Control requirements matched to risk
- A prioritized implementation roadmap
02
- Unclear
- Unowned
- Unsequenced
When to start with consulting
Start here while the answers are still open.
Consulting fits when no one can yet say who owns AI risk, which obligations apply, or which controls matter most. Building before those answers exist produces records nobody uses and controls in the wrong places.
What it often looks like
- A governance gap an investor will ask about
- An AI vendor handling your data before terms are in place
- A product claim your evidence can't yet back up
- No agreed owner for AI risk or approval decisions
- Frameworks and obligations not yet interpreted for your systems
03
- Generic
- Specific
- Decided
Template versus architecture
A template describes what others have. Architecture decides what you need.
Governance design starts from your systems, obligations, and stage, not from a generic document.
Where teams often start
What the architecture decides
Where teams often start
An AI acceptable-use policy template.
What the architecture decides
A governance operating model sized to your systems, obligations, and stage.
Where teams often start
A framework checklist.
What the architecture decides
The framework requirements that apply to you, interpreted into specific control requirements.
Where teams often start
Everyone owns AI risk.
What the architecture decides
A named owner for each system and a clear line of accountability above them.
Where teams often start
Escalate concerns to leadership.
What the architecture decides
Decision rights that set who decides what, at which threshold, and who can stop a system.
Where teams often start
Do everything the framework lists.
What the architecture decides
A sequenced roadmap that says what to build first, what can wait, and why.
04
- Structure
- Authority
- Risk
- Sequence
What the design settles
The governance architecture, decision by decision.
Each area ends in a decision leadership can approve, with the reasoning recorded so implementation builds against it.
01
Operating model
How governance is organized, which decisions sit with which forum, and how AI questions reach the people who can resolve them.
Governance structure
02
Roles and ownership
Who is accountable for each AI system, vendor, and use case, and where accountability passes between teams.
Accountability
03
Decision rights
Who can approve, intervene, accept risk, grant exceptions, or stop a system, and at what thresholds.
Authority
04
Risk structure
How AI uses are classified by consequence and AI vendor risk is assessed, so scrutiny scales with risk instead of applying evenly to everything.
Risk tiers
05
Control requirements
Which controls each level of risk requires, stated as requirements implementation can build and test against.
What must be controlled
06
Obligations
What customer agreements, product claims, security commitments, and applicable regulation require the company to do and prove.
Contracts, claims, and regulation
07
Evidence requirements
What the company must later be able to demonstrate, decided up front so evidence is designed in rather than reconstructed.
What must be provable
08
Prioritization and roadmap
The minimum structure the company needs now, what to build first, and what can wait until scale or scrutiny requires it.
Sequence
05
- Interpret
- Apply
- Prioritize
Framework interpretation
Frameworks, interpreted for your systems.
Customers, auditors, and insurers often ask how AI governance maps to recognized frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001.
Frameworks define what governance must achieve. They do not say who decides or what to build first. Consulting determines which requirements apply, what they mean for your systems, and which control requirements they become.
Interpretation comes before implementation.
Common reference points
- NIST AI Risk Management Framework
- ISO/IEC 42001 AI management systems
- EU AI Act obligations, where they apply
- California SB 813 and AB 1405 standards for third-party AI audits
- Deployment AI Governance Standard (DAGS) execution-time conditions, where appropriate
Requirements mapped to decisions
06
- Design
- Implement
- Operate
Engagement models
Governance Architecture comes first.
Governance Architecture is the consulting engagement. It can stand on its own or lead into implementation and ongoing operations.
01
Governance Architecture
Diagnosis and design
We map the systems, dependencies, claims, obligations, risks, and authority gaps, then deliver a decision-grade design for the minimum governance structure the business needs, with a prioritized roadmap.
02
Governance Activation Sprint
Build and activate
Implementation turns the approved design into working records, controls, and workflows, tested on real work.
03
Ongoing Governance Operations
Operate
Sentient administers the operating structure and keeps it current as systems, people, and vendors change.
07
- Design
- Interpret
- Decide
Questions, answered
AI governance consulting, answered.
What does an AI governance consultant do?
An AI governance consultant decides what governance a company should run: the operating model, ownership, decision rights, risk tiers, control requirements, and a sequenced roadmap. Sentient's consulting ends in a design leadership can approve and hand straight to implementation.
What AI governance includesWhen should a company start with consulting rather than implementation?
Start with consulting when no one can yet say who owns AI risk, which obligations apply, or which controls matter most. If those answers already exist, go straight to implementation.
AI governance implementationHow does AI governance consulting use the NIST AI RMF?
The NIST AI RMF says what AI risk management should achieve but leaves the operating design to each company. Consulting decides which of its outcomes apply to your systems, who owns them, and which control requirements they become.
NIST AI RMF implementationDoes Sentient give legal advice on AI regulation?
No. Sentient is not a law firm and gives no legal opinions. Consulting works alongside your counsel to translate obligations, such as those under the EU AI Act, into ownership, controls, and evidence requirements.
EU AI Act readiness
08
- Discuss
- Design
- Implement
- Operate
Be ready before they ask
Build the operating structure before the next customer, investor, insurer, partner, or buyer demands proof.
Sentient builds the governance structure companies need to show how AI is owned, controlled, reviewed, evidenced, and operated. Talk to Sentient about what your customers, investors, insurers, partners, or buyers will expect to see.