AI Governance Consulting for Mid-Market Companies
Build the AI governance program your board, your regulator, and your enterprise customers will accept - without standing up a 30-person AI risk function.
Three pressures are creating the demand for AI governance consulting at mid-market organizations right now. They are not theoretical.
The governance program isn't a hypothetical. It's a gap that's already shaping deals, audits, and incident risk.
Defensibility is specific. Your program needs to map to recognizable frameworks so customers, auditors, and regulators can evaluate it without bespoke explanation. Logiciel's governance practice aligns to:
NIST AI Risk Management Framework (AI RMF 1.0). The US reference framework. Defines Govern / Map / Measure / Manage as the lifecycle structure.
ISO/IEC 42001:2023. The international AI Management System standard. Certifiable, increasingly requested by enterprise procurement.
NIST Generative AI Profile. Applied controls for generative and foundation models.
EU AI Act risk classifications. Required if you operate or sell into the EU.
Sector overlays. HIPAA, GLBA, SR 11-7 (financial model risk), FDA SaMD where AI touches clinical workflows.
SOC 2 + AI control mapping. For SaaS providers under SOC 2 Type II who are extending coverage to AI components.
Your governance program doesn't need to implement all of these. It needs to demonstrably map to the ones your customers and regulators reference.
Most engagements start here. The assessment is a fixed-scope, four-week diagnostic that produces three artifacts your risk committee can act on.
We catalog every AI system in use across your organization: shipped product features, internal tools, embedded vendor capabilities, and shadow usage. Each system gets a risk classification per NIST AI RMF.
We compare your existing controls (model documentation, access control, monitoring, bias and fairness testing, human oversight, incident response) against the standards in scope. Gaps are scored by severity and effort.
We deliver a prioritized 12-month governance roadmap and draft policies - AI Acceptable Use, Model Risk Management, AI Incident Response, and Third-Party AI Assessment.
A formal readout for your risk committee, board AI subcommittee, or executive team. The readout is the artifact your customers and auditors will reference.
A mid-market AI governance program does not require a 30-person team. It requires a small set of operational practices that run reliably. After Logiciel engagements, our clients operate on a pattern that looks like this:
That's the operating picture. Reachable in 90 to 180 days for most mid-market organizations.
AI Governance Assessment (4 weeks). Fixed-scope diagnostic, three artifacts, executive readout. The most common starting point.
Governance Program Build (12–24 weeks). We design and stand up the program - policies, inventory, classification standard, pre-deployment review, monitoring, third-party process, and reporting cadence. Operated by your team; designed and trained by ours.
Fractional AI Risk Office (ongoing). A senior Logiciel governance lead serves as your fractional Head of AI Risk. Right model when you need the function but cannot justify a full-time executive hire yet.
We build MVPs as the foundation of your future product because time, money, investor trust, and momentum are on the line.
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.
The AI governance assessment is the smallest credible engagement that produces an inventory, a gap analysis, a roadmap, and a board-ready readout. Most mid-market boards will approve it in one meeting because the scope, timeline, and deliverables are explicit and the cost is contained.
