AI Governance Consulting for Mid-Market.

AI governance consulting is the advisory and program-design work that produces an organization's AI risk management posture: the inventory of AI systems in use, the standards they're classified against, the controls applied at each risk tier, the policies that govern human oversight and accountability, the monitoring and incident response practices, and the reporting cadence to leadership. For mid-market organizations, the engagement typically combines diagnostic, design, and build phases.

Yes - for three reasons that are now concrete rather than theoretical. Enterprise customers are sending AI risk questionnaires before contracting; regulators are codifying expectations at the sector and jurisdiction level; and internal AI usage at most mid-market organizations is outpacing the oversight applied to it. Companies without a documented governance program are losing deals, accumulating incident risk, and operating outside their own published security posture.

At minimum, the NIST AI Risk Management Framework (AI RMF 1.0) and - if you operate in the EU or sell to EU customers - the EU AI Act risk classifications. Many enterprise customers will also expect alignment to ISO/IEC 42001:2023. Sector overlays (HIPAA, GLBA, SR 11-7, FDA SaMD) apply if you operate in regulated industries. The exact set is determined in the assessment phase.

A 4-week assessment delivers an inventory, gap analysis, roadmap, and draft policies. A full operational program - policies in force, inventory current, pre-deployment review running, monitoring active, reporting cadence established - typically takes 12 to 24 weeks for a mid-market organization. The pace is determined by leadership decision velocity, not by Logiciel's capacity.

The 4-week assessment is a fixed-price engagement in the mid-five figures. A full program build typically runs in the low-to-mid six figures depending on the number of AI systems in scope and the sector overlay complexity. Fractional AI Risk Office engagements run on a monthly retainer sized to the organization's risk surface.

Governance defines what should be true (controls, oversight, risk classification). MLOps and AI reliability deliver the operational evidence that those controls are working (monitoring, drift detection, incident response, audit logging). The two need to be designed together. Logiciel's governance practice partners closely with our AI Reliability and MLOps practice so the policy and the operational implementation match.

AI ethics is the values layer - what your organization believes about fair, responsible, and trustworthy AI use. AI governance is the operating layer - the inventory, controls, policies, and reporting that make those values defensible. Ethics without governance is a statement. Governance without ethics is a checklist. Mature programs operationalize both, with governance making the ethics enforceable.