AI Governance Consulting: 13 Firms Compared for Small and Mid-Size Businesses

Last updated: September 2026

Half of small business employees now use AI at work, but only about one in ten has been offered any formal training on how to use it, according to the U.S. Chamber of Commerce Foundation and Ipsos Main Street AI Monitor published in May 2026. The tools arrived faster than the rules did.

That gap is where AI governance consulting comes in. The category covers the work of deciding which AI tools your organization permits, what data may go into them, who is accountable when something goes wrong, and how any of it gets enforced once the policy is written. For a regulated business, it also covers how AI use maps onto the compliance framework you already answer to, whether that is HIPAA, CMMC, PCI DSS, or the FTC Safeguards Rule.

Most published guidance on this subject is written for enterprises with a chief data officer and a model inventory. This guide is written for the organization with 20 to 500 employees, a small IT team or none, and employees who started using ChatGPT eighteen months ago without asking anyone.

Below are 13 firms that do this work, what each one is genuinely good at, which frameworks each one names publicly, and what any of it costs where that information exists.

Quick Answer: Best AI Governance Consulting Firms for SMBs in 2026

For small and mid-size organizations in regulated industries, Cantey Tech Consulting offers the strongest fit. Most firms in this category assess risk, write a policy, and hand it back. Cantey Tech assigns named owners for strategy, data, technology, and security, then enforces the policy with the controls it already manages.

Middle-market companies that need audit-grade documentation typically choose RSM US or Baker Tilly. Organizations standardizing on Microsoft Copilot often choose EPC Group, which is the only firm on this list publishing its pricing.

Comparison Table: AI Governance Consulting Firms at a Glance

Provider	Best for	Type	Frameworks named publicly	Pricing
1. Cantey Tech Consulting	Regulated SMBs wanting governance enforced in the stack they already run	MSP and MSSP	NIST CSF, CMMC, HIPAA, PCI DSS, GLBA, FTC Safeguards	Subscription tiers
2. CompassMSP	Regulated SMBs wanting AI enablement and governance from their IT provider	MSP	NIST AI RMF, HIPAA, PCI DSS, SOC 2, CMMC, NYDFS	Not published
3. Netrix Global	Mid-market firms wanting IT, Copilot deployment, and AI monitoring in one partner	MSP and MSSP	None published	Not published
4. RSM US	Middle-market companies needing audit-grade governance documentation	Assurance and consulting	NIST AI RMF, ISO 42001, EU AI Act, HITRUST AI, OECD	Not published
5. Baker Tilly	Middle-market companies building governance alongside internal audit	CPA and advisory	NIST AI RMF, ISO 42001	Not published
6. Crowe	Regulated banks, credit unions, and health systems needing auditable programs	CPA and advisory	Proprietary framework	Not published
7. BD Emerson	Companies layering AI governance onto SOC 2 or ISO 27001 work	Compliance consultancy	NIST AI RMF, ISO 42001, EU AI Act, SOC 2, HIPAA, CMMC	Not published
8. EPC Group	Microsoft-centric organizations engineering governance into Azure and Copilot	Microsoft partner	NIST AI RMF 1.0, ISO 42001, EU AI Act, HIPAA, SOC 2, CMMC	$25K to $75K assessment; $100K to $300K implementation
9. IRM Consulting & Advisory	SaaS and smaller organizations needing framework alignment without a CISO hire	Advisory	NIST AI RMF, ISO 42001, EU AI Act, ISO 27001, SOC 2	Not published
10. Centric Consulting	Larger organizations scaling AI across business units	Management consultancy	None published	Not published
11. Protiviti	Large enterprises folding AI risk into internal audit	Management consultancy	None published	Not published; free readiness call
12. Credo AI	Enterprises governing many models that need a system of record	Software platform	NIST AI RMF, ISO 42001, EU AI Act, OMB M-25, CO ADMT	Not published
13. Holistic AI	Organizations using AI in hiring or regulated decisions	Software platform	NIST AI RMF, ISO 42001, EU AI Act, NYC LL 144, Colorado AI Act	Not published

The AI Governance Problems SMBs Have in 2026

Shadow AI is already in the building. UpGuard’s State of Shadow AI found that eight in ten employees use generative AI tools their employer has not approved, and that 68% of security leaders admitted to doing the same thing in their own workflows. The same research found only 52% of employees were familiar with their organization’s AI policy. The problem is rarely that no policy exists. The problem is that nobody can see what is happening and nothing enforces the rules.

Sensitive data is leaving through personal accounts. Cyberhaven’s 2026 AI Adoption and Risk Report, built from endpoint telemetry rather than survey responses, found that 39.7% of workplace AI interactions involve sensitive data, and that a majority of Claude and Perplexity usage at work flows through personal accounts rather than corporate ones. Data moving through a personal account is outside your retention, your logging, and your legal hold.

Policy exists on paper but the governance stack does not. SmarterX’s 2026 State of AI for Business Report found 48% of organizations have a generative AI policy, but only 13% have all four governance foundations in place: an AI roadmap, an AI council, a generative AI policy, and an ethics policy. Just under a third have none of the four.

Copilot rollouts are stalling on permissions nobody cleaned up. CoreView’s 2026 State of Microsoft 365 Security Report found 66% of organizations delayed or cancelled a Microsoft 365 Copilot deployment over data exposure concerns, and 63% defer access reviews because they take too long. Copilot surfaces whatever SharePoint and OneDrive permissions already allow, which means a decade of inherited sharing links becomes a search feature the moment you turn it on.

Even well-resourced security teams are underwater. Proofpoint’s 2026 Voice of the CISO Report surveyed 1,600 CISOs at organizations with 1,000 or more employees. It found 78% now see generative AI as a security risk, up 18 points year over year, and 79% say they are expected to manage AI risk without proportional increases in budget or expertise. If that is the picture at a 1,000-person company with a full security team, a 60-person company without one is not going to solve this internally.

The regulatory floor is moving. Lawmakers in 45 states introduced 1,561 AI-related bills in the 2026 session alone, according to MultiState’s legislation tracker. Most will not pass. The direction of travel is the point. CISA, NSA, and the FBI issued joint guidance on securing data used to train and operate AI systems in May 2025, and sector regulators are working out how existing rules apply rather than waiting for new ones.

What an AI Governance Program Covers

Governance is a word that means different things to different vendors. For a small or mid-size organization, a working program has six parts, and most engagements you will be quoted cover some subset of them.

Policy and acceptable use. The written rules: which tools are approved, what data may go into them, who reviews output before it is used, and what happens when someone breaks them. This is the piece most firms deliver and the piece that changes least once written.

Data governance. What the AI can see. LLMs inherit whatever permissions already exist, so a model connected to a shared drive carrying a decade of inherited sharing links will surface exactly what those links allow. Cleaning up permissions and classifying data is unglamorous, and it usually decides whether a GenAI rollout is safe.

Risk assessment and regulatory mapping. How you manage risk against the regulatory obligations you already carry. For most SMBs that means mapping AI use onto HIPAA, CMMC, GDPR, or state privacy law rather than standing up a second program beside the regulatory compliance work already underway. Firms with a GRC practice tend to fold AI into existing enterprise risk management instead.

Guardrails and human oversight. The technical limits on what AI can do without a person in the loop. Guardrails cover blocked data types, restricted actions for agentic systems, and approval gates on consequential decisions. Where AI informs a decision about a person, explainability matters: you need to be able to say why the system produced the answer it did, and an explainable process is what a regulator asks for when something goes wrong.

AI ethics and responsible AI practices. Bias testing where AI touches hiring, lending, or care decisions, and transparency about where AI was used at all. Responsible AI governance gets dismissed as a large-company concern, but the legal exposure attaches to the decision rather than to the size of the company making it.

Continuous monitoring and audit trails. Governance is not a project with an end date. Model versions change, vendors change terms, and employees find new tools. Audit trails showing who used what and when are what let you answer a question six months later, and continuous monitoring is what tells you a new tool appeared before an auditor does. Pair both with an AI incident response path, so a bad paste reaches security the same day instead of surfacing in a review nine months on.

A program covering all six supports AI innovation rather than blocking it, because an organization can say yes to a new use case quickly when the framework for evaluating it already exists. The sustainability of the program depends on someone owning it after the consultants leave.

What to Look For in an AI Governance Partner

They name a framework. NIST AI RMF and ISO/IEC 42001 are the two reference points for this work. A firm that names neither is selling a proprietary approach you cannot benchmark, compare, or hand to an auditor. Five of the firms in this guide, including the one currently ranking first for this search, name no framework publicly at all.

They can enforce, not just recommend. A governance policy that lives in a PDF changes nothing. Ask specifically how a rule gets enforced: which conditional access policy blocks the unapproved tool, which DLP rule stops the paste, which log shows you it happened. If the answer is that your IT provider would handle that part, ask why you are paying two firms.

They map AI to the compliance regime you already have. You are not starting from zero. If you are a defense contractor, AI use touches CUI handling under CMMC. If you handle patient data, it touches the HIPAA Security Rule. A partner who treats AI governance as a separate program from your existing compliance work is creating a second set of documentation for the same auditors. If a GRC practice already runs your framework and audit work, that is the cheapest place to put this.

They can discover shadow AI, not just ask about it. Discovery should be technical: identity provider sign-in logs, cloud app security telemetry, DNS and firewall records, and expense data, which is the same evidence base a cyber risk assessment works from. A questionnaire circulated to department heads will not find it.

They size the engagement to your organization. Much of the published methodology in this category was built for companies with model inventories and data science teams. Ask what the deliverable looks like for an organization your size, and whether anything in their standard scope simply does not apply to you.

They tell you what you can skip. Most organizations under a few hundred seats do not need a dedicated AI governance platform, an AI council with a charter, or ISO 42001 certification. A partner who recommends all of it to everyone is selling a template.

They handle training, not just documentation. The Chamber of Commerce data on training is the relevant number here. Policy without training produces employees who violate rules they never read.

They will put their name on the ongoing part. Governance is not a project with an end date. Model versions change, vendors change terms, employees find new tools. Ask what the second year looks like and who is accountable for it.

Their references look like you. Case studies featuring global manufacturers and national banks tell you very little about how a firm handles a 75-person practice.

The 13 Best AI Governance Consulting Firms for SMBs in 2026

1. Cantey Tech Consulting

Best for: Small and mid-size organizations in regulated industries that want AI governance enforced inside the IT and security stack they already run.

Overview:

Cantey Tech Consulting is a managed IT and cybersecurity firm headquartered in North Charleston, South Carolina, founded in 2007 and named to the Inc. 5000 list of fastest-growing companies multiple times. It serves healthcare practices, manufacturers and defense contractors, law firms, financial institutions, school districts, and government agencies across the Southeast and nationally.

What separates Cantey Tech in this category is its operating model. Most firms in this guide assess risk, write policy, and hand the work back to the client. Cantey Tech combines AI strategy, data leadership, IT roadmapping, compliance guidance, cybersecurity controls, and ongoing operational support. The result is a governed AI program that can move from discovery and prioritization into implementation, user support, monitoring, maintenance, and continuous improvement.

The compliance and security practices provide the control structure behind that model. Cantey Tech maps AI use to the client’s existing obligations, develops acceptable use and data-handling standards, defines approved tools and access boundaries, and connects those decisions to identity, endpoint, email, firewall, logging, monitoring, and data-loss-prevention controls. The vISO and security team then review risk, evidence, exceptions, and remediation so governance remains active rather than becoming shelfware.

Cantey Tech assigns complementary executive roles instead of forcing one adviser to cover every discipline. The vCIO aligns technology architecture, budgets, vendors, and the broader IT roadmap. The virtual Chief Data Officer (vCDO) leads the ongoing data and AI operating program, including data readiness, governance, adoption, performance, and prioritization. The AI Strategist brings deeper business and AI advisory expertise to assessments, executive education, opportunity selection, operating-model design, and long-range guidance. The vISO and compliance team define and validate the security, privacy, regulatory, and acceptable-use controls that keep the program within the company’s risk tolerance.

Together, the vCIO, vCDO, AI Strategist, and vISO create a single governance and execution cadence. Business leadership defines the outcomes. The AI Strategist and vCDO translate those outcomes into a prioritized roadmap and operating model. The vCIO ensures the underlying systems and investments can support it. The vISO and security team establish and monitor the controls. This coordinated model allows the client to pursue efficiency and growth without separating AI strategy from data quality, IT reality, cybersecurity, or compliance.

The AI Readiness Assessment is the entry point. Cantey Tech spends time with executives, managers, functional leaders, and operating-line personnel to understand how the business actually works, where time and value are being lost, which decisions depend on trustworthy information, and where official process documentation differs from day-to-day reality. The assessment reviews business objectives, functional workflows, systems, data sources, data quality, permissions, current AI use, shadow AI, security, compliance, workforce readiness, and measurement requirements.

That discovery produces more than an inventory of tools. It establishes the truth of the business data and current process, identifies practical use cases by functional area, distinguishes attractive ideas from operationally viable opportunities, and creates a current-state view, prioritized opportunity map, risk and governance map, recommended ownership model, and phased roadmap. Those findings then guide pilots, implementation, employee training, and the ongoing AI Operations and Management service led by the vCDO.

Key features:

• AI governance policy development mapped to the client’s existing compliance framework
• Shadow AI discovery using identity, endpoint, DNS, and email telemetry already in place
• Enforcement through managed identity, endpoint security, managed firewall, and email security
• Fully managed cybersecurity: SOC, MDR, SIEM, endpoint security, managed firewall, email security
• Compliance consulting across CMMC, HIPAA, PCI DSS, SOX, GLBA, NCUA, NIST CSF, and the FTC Safeguards Rule
• Penetration testing and cyber risk assessments
• Dedicated vCIO with technology roadmaps and quarterly business reviews
• Managed IT support with 75.26% first-call resolution and 1.57-minute average response time
• Business-first AI Readiness Assessment with executive, manager, functional, and operating-line discovery
• Workflow, systems, and data mapping that tests whether documented processes match how work is performed today
• Virtual Chief Data Officer leadership for ongoing AI operations, data governance, adoption, performance, and roadmap management
• AI Strategist guidance for executive education, use-case prioritization, operating-model design, pilots, and long-range AI strategy
• Integrated vCIO, vCDO, and vISO governance cadence connecting technology, data, security, compliance, budgeting, and business outcomes
• Acceptable use, approved-tool, data-handling, human-validation, vendor-risk, and incident-response requirements
• Ongoing AI operations support, user guidance, monitoring, policy updates, lifecycle management, and continuous improvement

Pricing: Subscription tiers based on organization size and service package. Governance work is typically scoped into an existing managed services agreement rather than sold as a separate project.

Pros:

• Policy and enforcement come from the same team, which closes the gap most governance engagements leave open
• AI governance maps into an existing compliance program instead of creating a parallel one
• Consultative vCIO model keeps governance decisions under review as tools change
• 96% CSAT across 196 or more client organizations, with roughly 10 years of experience per consultant
• Combines business discovery, AI strategy, data leadership, IT planning, security, compliance, and ongoing operations in one accountable model
• Assessment methodology is built around how executives, managers, and operating personnel actually use systems, workflows, and data
• vCIO, vCDO, AI Strategist, and vISO responsibilities create clear ownership from strategy through control enforcement and continuous improvement

Cons:

• On-site support is strongest in the Carolinas and neighboring states, with national remote delivery
• Broader managed-services model rather than a standalone AI laboratory; organizations seeking only independent model testing may prefer a specialist
• Best fit assumes Cantey Tech manages or co-manages the environment; governance-only engagements are a narrower fit

2. CompassMSP

Best for: Regulated small and mid-size businesses that want AI enablement and governance delivered together by their IT provider.

Overview:

CompassMSP is a managed services provider with an AI enablement practice aimed explicitly at regulated organizations in the 50 to 500 employee range. It is the closest structural match to Cantey Tech on this list and the most direct competitor for the same buyer.

The offering covers an AI enablement assessment, policy development, employee training, staged Microsoft Copilot rollout, ongoing shadow AI monitoring, and vCISO advisory. CompassMSP names NIST AI RMF, HIPAA, PCI DSS, SOC 2, CMMC, and NYDFS, which puts it ahead of most firms in this guide on framework specificity.

Key features:

• AI enablement assessment and policy development
• Staged Microsoft Copilot rollout with permissions remediation
• Ongoing shadow AI monitoring
• vCISO advisory
• Employee AI training

Pricing: Not published. Contact required.

Pros:

• Explicit SMB and mid-market positioning rather than enterprise methodology scaled down
• Names NIST AI RMF alongside HIPAA, PCI DSS, SOC 2, and CMMC
• Combines enablement and governance, which suits organizations still deciding what to adopt

Cons:

• Geographic coverage differs from a regionally concentrated provider, so on-site expectations should be confirmed
• Enablement-first framing may be a looser fit for organizations whose immediate problem is containment rather than adoption

3. Netrix Global

Best for: Mid-market organizations that want one partner to run IT, deploy Copilot, and monitor AI in production.

Overview:

Netrix Global is an engineering-led managed services and security provider operating since 1989, with deep Microsoft specialization and vertical practices in financial services, legal, education, manufacturing, and professional services.

Its AI practice covers strategy and roadmap work, Copilot and AI agent implementation, managed AI operations monitoring, and a fractional Chief AI Officer arrangement for organizations that cannot justify the role full time. Netrix has also published the most visible SMB-facing writing on shadow AI in this category, which is a fair signal that the firm takes the mid-market seriously rather than treating it as a downmarket afterthought.

The gap worth knowing about is framework specificity. Netrix’s public AI pages reference an internal AI readiness checklist rather than NIST AI RMF, ISO 42001, or the EU AI Act. For an organization that needs to show an auditor which standard its program follows, that is a question to ask on the first call.

Key features:

• AI strategy and roadmap development
• Microsoft Copilot and AI agent implementation
• Managed AI operations monitoring
• Fractional Chief AI Officer
• Full managed IT and security services alongside the AI practice

Pricing: Not published. Contact required.

Pros:

• Genuine depth in Microsoft environments and AI agent deployment
• Strong vertical practices in five industries
• Ongoing monitoring rather than a one-time engagement

Cons:

• No public framework alignment to NIST AI RMF, ISO 42001, or the EU AI Act
• Scale and vertical focus point more toward the mid-market than toward organizations under 100 employees

4. RSM US

Best for: Middle-market companies that want audit-grade AI governance documentation from an established assurance firm.

Overview:

RSM US is a national assurance, tax, and consulting firm that positions explicitly around the middle market, and it has the most thorough public framework alignment of any firm in this guide. Its AI governance practice names NIST AI RMF, ISO/IEC 42001:2023, the EU AI Act, the Microsoft Responsible AI Standard, Google SAIF, HITRUST AI, and the OECD AI Principles.

The work is advisory: governance strategy, policy development, internal audits of AI practices, third-party and vendor risk assessment, training, and adversarial AI penetration testing. RSM does not implement or operate the controls, which means the output is documentation and recommendations that someone else has to put into effect.

Key features:

• AI governance strategy and policy development
• Internal audit of AI practices
• Third-party and vendor AI risk assessment
• Adversarial AI penetration testing
• Training programs

Pricing: Not published. Contact required.

Pros:

• Broadest public framework alignment in this guide
• Explicit middle-market positioning rather than enterprise-only
• Assurance-firm credibility carries weight with boards, insurers, and auditors

Cons:

• Advisory only, so implementation and enforcement fall to your IT provider or internal team
• Engagement structure and cost are typically higher than an MSP-delivered equivalent

5. Baker Tilly

Best for: Middle-market companies building AI governance alongside existing internal audit and risk work.

Overview:

Baker Tilly is a CPA and advisory firm serving the middle market and larger enterprises. Its AI governance practice covers governance planning, responsible AI program design, operating model work, and internal audit over AI controls.

Baker Tilly names NIST AI RMF and ISO 42001, though the references sit on its broader AI consulting page rather than the governance page itself. The natural fit is an organization that already uses Baker Tilly for audit or risk work and wants AI folded into that relationship rather than opened as a new vendor conversation.

Key features:

• AI governance planning and operating model design
• Responsible AI program development
• Internal audit over AI controls
• Risk assessment

Pricing: Not published. Contact required.

Pros:

• Names NIST AI RMF and ISO 42001
• Integrates cleanly with existing audit and risk engagements
• Middle-market experience rather than enterprise-only

Cons:

• Advisory only, with no implementation or ongoing enforcement
• Governance page itself is lighter on framework specifics than the broader AI page

6. Crowe

Best for: Regulated banks, credit unions, healthcare systems, and insurers that need auditable AI governance programs.

Overview:

Crowe is a public accounting, consulting, and technology firm with concentrated expertise in banking, financial services, healthcare, and life sciences. Its AI governance practice covers risk and maturity assessment, custom governance program design, ongoing oversight, internal audit, training, and vendor risk.

Crowe works from a proprietary framework rather than naming NIST AI RMF or ISO 42001 on its governance page. For a bank or health system already working with Crowe on regulatory matters, that is a reasonable trade. For an organization that wants to benchmark its program against a public standard, it is a limitation worth raising.

Key features:

• AI risk and maturity assessment
• Custom governance program design
• Ongoing oversight and internal audit
• Vendor and third-party AI risk
• Training

Pricing: Not published. Contact required.

Pros:

• Deep regulatory fluency in banking, credit unions, and healthcare
• Program design plus ongoing oversight rather than a one-time assessment
• Established relationships with examiners and regulators in those sectors

Cons:

• Proprietary framework rather than a public standard you can benchmark against
• Oriented toward larger regulated institutions than toward a 50-person practice

7. BD Emerson

Best for: Companies layering AI governance onto an existing SOC 2, ISO 27001, or HIPAA compliance program.

Overview:

BD Emerson is a compliance and security consultancy that treats AI governance as an extension of the certification work it already does. It names EU AI Act, ISO/IEC 42001, and NIST AI RMF alongside SOC 2, ISO 27001, HIPAA, CMMC, FedRAMP, GDPR, PCI DSS, NIS 2, and DORA.

The delivery model includes advisory, framework implementation, internal audits, and an AI governance as a service arrangement with fractional CISO and data protection officer coverage. That subscription structure fits organizations that need ongoing accountability but cannot staff the role.

Key features:

• AI governance advisory and framework implementation
• Internal audit against ISO 42001 and NIST AI RMF
• AI governance as a service with fractional CISO and DPO
• Integration with SOC 2, ISO 27001, HIPAA, and CMMC programs

Pricing: Not published. Contact required.

Pros:

• Strong framework coverage across both AI and traditional security standards
• Subscription model provides ongoing accountability rather than a one-time deliverable
• Natural fit for companies already pursuing SOC 2 or ISO 27001

Cons:

• Advisory and audit rather than technical enforcement
• Client base skews toward technology companies and startups more than traditional regulated SMBs

8. EPC Group

Best for: Microsoft-centric organizations that want governance engineered directly into Azure, Copilot, and Purview.

Overview:

EPC Group is a Microsoft Solutions Partner consultancy founded in 1997, holding all six Microsoft solution designations. Its AI governance practice is the most developed in this guide on the technical side, covering framework design, implementation across the Microsoft stack, ongoing governance monitoring as a managed service, and a virtual Chief AI Officer retainer.

EPC Group names NIST AI RMF 1.0, ISO 42001, the EU AI Act, HIPAA, SOC 2, FedRAMP, and CMMC. It is also the only firm on this list that publishes pricing, which is worth noting on its own: an AI readiness assessment runs $25,000 to $75,000 over four to six weeks, and a full governance implementation runs $100,000 to $300,000 over 12 to 24 weeks.

Those numbers are useful as a market reference point even if they are out of range for most SMBs. They tell you what a full enterprise-methodology engagement costs, which is the right benchmark for deciding whether you need one.

Key features:

• AI governance framework design and Microsoft stack implementation
• Azure AI, Copilot, and Microsoft Purview configuration
• Ongoing governance monitoring as a managed service
• Virtual Chief AI Officer retainer
• Agentic AI governance

Pricing: Published. AI readiness assessment $25,000 to $75,000 over four to six weeks. Full governance implementation $100,000 to $300,000 over 12 to 24 weeks. vCAIO retainer priced after scoping.

Pros:

• Only firm in this guide publishing engagement pricing
• Deep technical implementation in Microsoft environments, not advisory alone
• Strong framework coverage including NIST AI RMF and ISO 42001

Cons:

• Client base is heavily Fortune 500 and federal, and pricing reflects that
• Assessment minimum alone exceeds the annual IT budget of many organizations in the 20 to 100 employee range

9. IRM Consulting & Advisory

Best for: SaaS companies and smaller organizations that need framework-aligned governance without a full-time security hire.

Overview:

IRM Consulting & Advisory positions explicitly against the cost of a full-time CISO and markets to SaaS companies and smaller organizations. It names NIST AI RMF, ISO 42001, the EU AI Act, ISO 27001, SOC 2, and GDPR.

The firm is smaller than most on this list and Toronto-based, with a client lean toward SaaS and startups rather than traditional regulated industries. For an organization that fits that profile, the framework coverage relative to firm size is unusual and worth a conversation.

Key features:

• Framework-aligned AI governance program development
• Fractional security leadership
• ISO 42001 and NIST AI RMF alignment
• SOC 2 and ISO 27001 support

Pricing: Not published. Contact required.

Pros:

• Framework coverage well beyond what firm size would suggest
• Explicitly priced and scoped against smaller organizations
• Fractional model suits companies that cannot staff the role

Cons:

• SaaS and startup client lean rather than healthcare, manufacturing, or legal
• Smaller team means less bench depth than a national firm
• Canadian base may complicate US sector-specific regulatory work

10. Centric Consulting

Best for: Larger organizations scaling AI across multiple business units that need governance and change management together.

Overview:

Centric Consulting is a management and technology consultancy with an AI governance practice covering policy development, governance framework design, and tool implementation. Its client base includes large utilities, technology integrators, and construction firms, with Fortune 500 references.

Centric’s strength is organizational change management alongside governance, which matters when AI adoption crosses many teams with different needs. Its public pages do not name NIST AI RMF, ISO 42001, or other standards, using generic governance language instead.

Key features:

• AI governance policy and framework development
• Governance tool selection and implementation
• Organizational change management
• AI strategy alongside governance

Pricing: Not published. Contact required.

Pros:

• Change management depth that pure compliance firms lack
• Handles governance and adoption strategy as one program
• Experience scaling programs across large, distributed organizations

Cons:

• No public framework alignment
• Enterprise client base and engagement structure
• Limited fit for organizations whose main need is containing tools already in use

11. Protiviti

Best for: Large enterprises folding AI risk into an existing internal audit function.

Overview:

Protiviti is a global management consultancy and a subsidiary of Robert Half, with an AI services practice that includes governance as one component rather than a standalone offering. The work covers AI strategy, an initial governance framework, internal audit, security reviews, model testing, and lifecycle management.

Protiviti serves large enterprises, national banks, and global manufacturers. Its AI pages do not name NIST AI RMF or ISO 42001, which is unexpected for a firm of this type. It offers a free 30-minute AI readiness call, which is a low-cost way to benchmark your thinking even if the engagement itself is out of range.

Key features:

• AI strategy with an initial governance framework deliverable
• Internal audit over AI
• AI security reviews and model testing
• AI lifecycle management

Pricing: Not published. Free 30-minute AI readiness call offered.

Pros:

• Deep internal audit and enterprise risk capability
• Global bench across regulatory environments
• Free readiness call gives smaller organizations a useful reference point

Cons:

• No standalone AI governance offering, and governance is one pillar inside a larger strategy engagement
• No public framework alignment
• Enterprise scale and pricing put it out of reach for most SMBs

12. Credo AI

Best for: Enterprises governing many AI models at scale that need a system of record rather than advice.

Overview:

Credo AI is a software platform, not a consulting firm, and it is included here because it appears in the same search results and buyers regularly confuse the two categories. The platform provides model registries, policy packs, risk assessment workflows, and compliance evidence generation, with advisory and forward-deployed experts available alongside it.

Credo AI names the EU AI Act, NIST AI RMF, ISO 42001, OMB M-25, Colorado’s ADMT rules, and NAIC AI standards. Its customer base is large enterprise, including Mastercard, Autodesk, Northrop Grumman, and Booz Allen.

A platform like this earns its cost when you have dozens of models to track and evidence to produce continuously. If you have four approved AI tools and 80 employees, it is the wrong purchase.

Key features:

• AI model registry and system of record
• Policy packs mapped to named regulations
• Risk assessment workflows
• Compliance evidence generation
• Optional forward-deployed advisory

Pricing: Not published. Third-party estimates circulate but are not confirmed by the vendor.

Pros:

• Strong regulatory mapping, including US state-level rules
• Purpose-built for organizations with many models under governance
• Enterprise reference customers in regulated sectors

Cons:

• Software rather than consulting, so it does not write your policy or enforce it on your endpoints
• Built for scale most SMBs do not have
• No published pricing makes budget planning difficult

13. Holistic AI

Best for: Organizations using AI in hiring or other regulated decisions that need continuous bias testing and audit evidence.

Overview:

Holistic AI is a second software platform in this category, covering AI system discovery, technical testing, and compliance evidence generation. It is the strongest option on this list for organizations exposed to algorithmic decision-making rules, naming NYC Local Law 144, the Colorado AI Act, the EU AI Act, NIST AI RMF, and ISO 42001.

If you use AI anywhere in hiring, lending, insurance underwriting, or tenant screening, this category of tooling becomes relevant at a much smaller company size than general AI governance software does, because the legal exposure attaches to the decision rather than to the scale of your AI program.

Key features:

• AI system discovery and inventory
• Bias and technical testing
• Audit-ready evidence generation
• Mapping to NYC Local Law 144 and the Colorado AI Act

Pricing: Not published. Contact required.

Pros:

• Best coverage of algorithmic decision-making regulation in this guide
• Technical testing rather than documentation alone
• Relevant at smaller scale than general governance platforms if you use AI in hiring

Cons:

• Software rather than consulting
• Narrower relevance for organizations not using AI in regulated decisions
• No named customer references published on the platform page

Enterprise Firms Worth Knowing About

Accenture, Deloitte, PwC, EY, and IBM Consulting all run substantial AI governance practices, and any list that omitted them would be incomplete. They are not ranked here because their engagement minimums put them outside the range of the organizations this guide is written for.

Deloitte publishes the most widely referenced material of the group, and a fair number of smaller providers quietly build their own methodology from it. Reading what Deloitte puts out costs nothing and will tell you what a mature program is supposed to contain even if you never hire them. The same goes for IBM’s governance writing and PwC’s responsible AI work.

What you buy from any of these firms is enterprise risk management applied to AI, delivered by a team operating at a scale most SMBs never will. If your organization is above roughly 1,000 employees or your AI program carries board-level scrutiny, they belong on your list. Below that, their AI consulting services are priced for a different buyer, and a Deloitte engagement does not come with anyone to administer your endpoints afterward.

Consulting vs. Platform vs. Your Existing MSP

Searching for AI governance returns three different kinds of vendor, and most buyers do not realize they are looking at three different purchases until the second or third sales call. Here is the distinction.

A governance consultancy sells you judgment and documentation. You get an assessment, a policy set, a framework mapping, and a roadmap. This is the right purchase when you need something defensible to show an auditor, a board, an insurer, or a customer’s procurement team, and when nobody internally has done this work before. What it does not do is change anything in your environment. The consultancy leaves, and someone else has to implement what the document says.

A governance platform sells you a system of record. It inventories models, tracks policies against named regulations, runs testing, and produces evidence continuously. The economics work when you have enough AI in production that tracking it manually has become a job. Below that threshold you are paying enterprise software prices to manage a list of four approved tools, and the platform still does not write your policy or block anything on an employee’s laptop.

Your managed IT or security provider sells you enforcement. They already run your identity platform, your endpoints, your email filtering, and your network. Every control that turns an AI policy into something real lives in systems they administer. The historical weakness of this option was that MSPs did not have governance expertise. That has changed quickly, and the providers who added a compliance practice can now do both halves.

The practical answer for most organizations under about 300 employees is the third option, with a consultancy engaged once if you have a specific external requirement such as a certification, a contract clause, or a regulator asking questions. The reason is not cost. It is that a governance program nobody can enforce produces documentation, not safety, and enforcement is the part that requires ongoing access to your systems.

How to Choose the Right AI Governance Partner for You

Three questions settle this faster than a vendor comparison matrix.

Is your problem documentation or enforcement?

If a customer, auditor, insurer, or regulator is asking for evidence of an AI governance program, you need documentation and an advisory firm delivers that fastest. If your problem is that you do not know what your employees are using and nothing stops them, you need enforcement, which means the provider who administers your systems. Most organizations discover they have the second problem while shopping for the first.

What compliance regime are you already in?

AI governance is much cheaper to build inside an existing compliance program than beside one. If you already do CMMC, HIPAA, PCI DSS, or FTC Safeguards work, the partner running that program should extend it to cover AI. Opening a separate AI governance workstream creates two sets of documentation, two assessments, and two vendors answering to the same auditor.

How much AI do you have in production, honestly?

Count the AI systems your organization has deployed and depends on, not the tools employees experiment with. If the number is under ten, you need a policy, discovery, training, and enforcement, and you do not need a governance platform. If it is in the dozens and includes models making decisions about people, the platform category becomes relevant and the conversation changes.

Frequently Asked Questions

What is AI governance consulting?

AI governance consulting is advisory work that helps an organization decide which AI tools it permits, what data can go into them, who is accountable for AI decisions, and how those rules map onto existing compliance obligations. A typical engagement produces an assessment of current AI use, a written policy, a framework mapping to a standard such as NIST AI RMF or ISO/IEC 42001, and a roadmap. Some firms stop there, and others carry the work into implementation and ongoing monitoring.

How much does an AI consultant cost?

Most firms do not publish pricing. EPC Group, the exception among the firms in this guide, publishes $25,000 to $75,000 for an AI readiness assessment over four to six weeks and $100,000 to $300,000 for a full governance implementation over 12 to 24 weeks. Those figures reflect enterprise methodology applied to Microsoft-heavy environments. Organizations that add AI governance to an existing managed services or compliance relationship generally pay substantially less, because the discovery and much of the control work is already being done.

What should be in an AI governance policy?

At minimum: which AI tools are approved and how new ones get reviewed, what categories of data may never be entered into an AI tool, rules for verifying AI output before it is used in client or patient work, disclosure requirements for AI-assisted work product, who owns AI decisions and who to contact with questions, vendor and third-party AI requirements, and the consequences for violations. For regulated organizations, add a section mapping each rule to the specific compliance obligation behind it.

What are the key principles of AI governance?

Most frameworks converge on a similar set: accountability for who owns AI decisions, transparency about where AI is used, data protection covering what goes in and where it goes, human oversight of consequential decisions, fairness and bias testing where AI affects people, security of the AI systems themselves, and ongoing monitoring rather than one-time approval. The NIST AI Risk Management Framework organizes these into four functions: govern, map, measure, and manage.

Do small businesses need AI governance?

Yes, though not the enterprise version of it. Half of small business employees use AI at work, and roughly one in ten has had any training on it. If your organization handles patient records, payment data, controlled unclassified information, or client confidences, employees are making decisions every day about what goes into a chatbot. A written policy, a discovery pass, training, and enforcement in your existing security tools covers most of the risk. An AI council, a model inventory, and ISO 42001 certification do not apply to most organizations this size.

Can our MSP handle AI governance, or do we need a separate consultant?

It depends on whether your MSP has a compliance practice. An MSP that already consults on HIPAA, CMMC, PCI DSS, or the NIST Cybersecurity Framework has the methodology to extend into AI governance, and it has something no consultancy has: administrative access to the systems that enforce the policy. An MSP without a compliance practice can enforce rules but should not be the one writing them. Ask which frameworks your provider consults on today and who on the team holds those credentials.

What is the difference between AI governance and AI security?

AI governance covers the decisions: which tools are allowed, what data may be used, who is accountable, and how AI use aligns with regulation. AI security covers the technical controls: preventing data leakage into AI tools, securing AI systems against attack, managing identity and access for AI agents, and monitoring for misuse. Governance decides the rules and security enforces them. Programs fail most often at the seam between the two, where a policy exists but no control implements it.

Does HIPAA apply to AI tools?

Yes. If protected health information goes into an AI tool, the HIPAA Security Rule and Privacy Rule apply to that processing the same way they apply to any other system handling PHI. In practice this means the AI vendor generally needs a business associate agreement, the data flow needs to be documented in your risk analysis, and access controls and audit logging must meet the same standard as the rest of your environment. Consumer versions of most chatbots do not offer a BAA, which is why entering PHI into them is a violation regardless of intent. Cantey Tech covers this under its HIPAA compliance practice and its AI work in healthcare.

Does CMMC cover employee AI use?

CMMC does not have AI-specific requirements, but its underlying NIST SP 800-171 controls apply to controlled unclassified information wherever it goes, including into an AI tool. Entering CUI into a public AI service is a spillage event. Defense contractors should treat AI tool approval as part of their existing CUI handling procedures, document approved tools in the system security plan, and confirm that any AI service touching CUI meets the same requirements as any other external system. Cantey Tech covers this under CMMC compliance and its AI and CMMC practice.

How long does it take to put AI governance in place?

For an organization of 20 to 500 employees working with a provider who already manages its environment, a first pass typically takes four to eight weeks: a discovery period to find what is in use, policy drafting, review with leadership, employee training, and configuration of the controls that enforce it. Full enterprise engagements run 12 to 24 weeks. The part with no end date is maintenance, because tools change, vendors change terms, and employees find new things.

What is ISO 42001 and do we need it?

ISO/IEC 42001 is the international management system standard for artificial intelligence, published in 2023, structured like ISO 27001 but scoped to AI. It is a certifiable standard, which means an accredited auditor can assess you against it. Most small and mid-size organizations do not need certification. It becomes relevant when a customer’s procurement process requires it, when you sell AI-enabled products to enterprises, or when you operate in a market where regulators are pointing at it. Aligning your program to the standard is useful long before certifying against it.

What happens if employees use AI tools we never approved?

The exposure depends on what they put in. Data entered into a consumer AI account leaves your retention policy, your logging, your legal hold capability, and often your jurisdiction. For a regulated organization that can constitute a reportable incident, a compliance violation, or a breach of a client confidentiality obligation, independent of whether anything bad happened to the data. The practical response is discovery first, then an approved-tool list that gives employees a sanctioned way to do what they were already doing, then enforcement. Bans without approved alternatives push usage onto personal devices where you have no visibility at all.

Key Takeaways

• AI arrived in most small and mid-size organizations before any policy did. The gap between adoption and governance is the risk, not AI itself.
• The most common failure is not a missing policy. It is a policy nobody can enforce. Ask any prospective partner which specific control implements each rule.
• Framework alignment is a fast filter. NIST AI RMF and ISO/IEC 42001 are the reference standards, and several firms in this category, including some large ones, name neither publicly.
• Most organizations under a few hundred employees need a policy, discovery, training, and enforcement. They do not need a governance platform, an AI council, or certification.
• AI governance is cheaper and more durable when it extends an existing compliance program rather than running beside it.
• Pricing in this category is largely unpublished. The one firm that does publish puts a full enterprise engagement at $100,000 to $300,000, which is a useful benchmark for deciding whether you need one.

See Cantey Tech in Action

If employees are already experimenting with AI, the right starting point is not another software purchase. It is an honest assessment of the business, its workflows, its people, its systems, and the data that reflects how work is actually performed. Cantey Tech uses that discovery to identify where AI can create measurable value, where governance must come first, and what the organization is ready to operate responsibly.

From there, Cantey Tech provides the leadership and operating structure to keep the program moving. The AI Strategist shapes the opportunity and roadmap. The vCDO runs the data and AI operating program. The vCIO aligns technology and investment priorities. The vISO and compliance and security teams establish acceptable-use requirements, protect company data, validate controls, and monitor risk. Together, they turn AI from disconnected experiments into a more efficient, secure, governed, and continuously improving business capability.

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Sources: U.S. Chamber of Commerce Foundation and Ipsos Main Street AI Monitor (May 2026); U.S. Census Bureau Business Trends and Outlook Survey (May 2026); SmarterX 2026 State of AI for Business Report; UpGuard State of Shadow AI (November 2025); Cyberhaven 2026 AI Adoption and Risk Report; Proofpoint 2026 Voice of the CISO Report; CoreView 2026 State of Microsoft 365 Security Report; Varonis 2025 State of Data Security Report; Stanford HAI 2026 AI Index Report; MultiState State AI Legislation Tracker (March 2026); CISA, NSA and FBI joint guidance, AI Data Security: Best Practices for Securing Data Used to Train and Operate AI Systems (May 2025); NIST AI Risk Management Framework (AI 100-1); ISO/IEC 42001:2023.