AI governance
AI governance is the set of policies, roles, processes, and controls a company uses to decide which AI systems it builds or buys, how they are approved, how risks are assessed, and how they are monitored once running. It assigns accountability for AI decisions and makes sure use complies with law, contracts, and the company's own standards.
01why it matters for a business
As AI moves from experiments into operations, ad hoc decisions stop scaling. Someone has to decide which uses are allowed, which data may be used with which tools, who approves a new AI system, how it is tested, and who is accountable when it fails. Customers, auditors, insurers, and acquirers increasingly ask these questions, and a company with clear answers moves faster, not slower.
Established frameworks save you from starting from scratch. The NIST AI Risk Management Framework organizes the work into four functions: govern, map, measure, and manage. ISO/IEC 42001 defines a certifiable AI management system. The EU AI Act sets risk-based obligations for companies serving the EU market. A mid-sized company does not need all of this on day one, but it does need an inventory of AI in use, an owner, risk tiers, and review proportional to risk.
02what it looks like in practice
A logistics company sets up lightweight governance in a quarter. It inventories the AI tools and features in use, assigns an executive owner, and sorts uses into tiers. Low risk (drafting internal content) needs only approved tools. Medium risk (customer-facing answers) needs evals and a named business owner. High risk (decisions affecting pay, hiring, credit, or safety) needs legal review, human approval of each decision, and periodic audits. A short intake form routes each new request to the right tier.
03common mistakes
- Governance as a document instead of a process. If nothing changes when a new AI request arrives, it is not governance.
- One rule for everything. Proportional, risk-based review keeps low-risk work fast.
- No inventory. You cannot govern what you do not know is running.
- No owner with the authority to say no.
04related terms
- Shadow AIShadow AI is the use of AI tools by employees without the knowledge, approval, or oversight of the company's IT, security, or compliance teams, such as pasting customer data into a personal chatbot account or connecting an unvetted AI plugin to work email.
- AI guardrailsGuardrails are the controls around an AI system that keep its inputs, outputs, and actions within acceptable limits: input filtering, output checks for policy, format, and sensitive data, limits on which tools and data it can reach, spending caps, and rules that route risky cases to a person.
- Chief AI Officer (CAIO)A Chief AI Officer (CAIO) is the executive accountable for a company's AI strategy, adoption, and governance: deciding where AI should be applied, prioritizing and funding use cases, setting policy and risk controls, coordinating data and technology teams, and measuring results.
- Human in the loop (HITL)Human in the loop is a design pattern in which a person reviews, approves, or corrects an AI system's output at defined points before it takes effect.
- SOC 2SOC 2 is an attestation report, defined by the American Institute of Certified Public Accountants (AICPA), in which an independent CPA firm examines a service organization's controls against the Trust Services Criteria: security, availability, processing integrity, confidentiality, and privacy.
05where insomnia club fits
Insomnia Club's fractional AI officer engagement sets up practical governance for mid-sized companies: inventory, risk tiers, approval steps, and an accountable owner, without slowing down low-risk work.
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