Building an AI Governance Framework: A Practical Guide for SMBs

Why AI Governance Is No Longer Optional

Every small and medium-sized business in Europe that uses AI — and that includes virtually every modern business — needs an AI governance framework. Not because it sounds impressive on a slide deck, but because the EU AI Act now demands it.

The word "governance" often triggers eye rolls in SMBs. It sounds like something built by committees for committees. But an AI governance framework is simply the answer to three questions: who is responsible for AI in our organization, what rules do we follow when using it, and how do we know those rules are being followed?

Without governance, compliance is guesswork. You might classify your AI systems correctly today, but six months from now someone adopts a new tool, nobody documents it, and you have a gap you do not know about. Governance is the structure that prevents drift and ensures your compliance posture holds over time.

The EU AI Act does not use the exact phrase "AI governance framework," but its requirements collectively demand one. Article 4 requires AI literacy across your organization. Article 9 requires risk management systems for high-risk AI. Article 17 requires quality management systems. Article 26 requires deployers to implement human oversight and monitoring. Taken together, these articles describe a governance framework — whether the regulation calls it that or not.

This guide walks through the five pillars of an effective AI governance framework, with practical steps that work for businesses with 5 employees or 500.

Pillar 1: Leadership Commitment and Accountability

Governance without leadership backing is theater. If the person at the top does not care about AI governance, nobody beneath them will either.

Why It Matters

The EU AI Act places legal responsibility on organizations, not individuals. But within your organization, someone needs to own AI governance. For an SMB, this does not mean hiring a Chief AI Officer. It means someone in the leadership team explicitly accepts responsibility for how your business uses AI.

Article 26 of the EU AI Act requires deployers to assign competent individuals to oversee high-risk AI systems. Article 4 requires that AI literacy training is provided "taking into account their technical knowledge, experience, education and training and the context the AI systems are to be used in." Both of these requirements imply leadership decisions about resource allocation and priorities.

Practical Steps

Designate an AI governance lead. In a small business, this is likely the founder or managing director. In a larger SMB, it might be the head of operations, IT, or legal. The key qualification is not technical expertise — it is authority to make decisions and allocate resources.

Add AI to board or leadership meeting agendas. Governance dies when it is treated as a one-off project. Put AI compliance on the regular agenda, even if it is just a five-minute update. Monthly is ideal; quarterly is the minimum.

Set the tone. Leadership must communicate that AI governance is a business priority, not an administrative burden. When employees see the CEO taking AI literacy training seriously, they follow suit.

Allocate a budget. AI governance costs money — whether for tools, training, or external advice. Even a modest budget signal makes the commitment real. If your business has high-risk AI systems, the investment needed is proportionally larger, but the EU AI Act explicitly states that obligations should be proportionate to the risk.

Document the governance structure. Write down who is responsible for what. This does not need to be elaborate — a one-page document listing the AI governance lead, their responsibilities, reporting lines, and escalation procedures is sufficient for most SMBs.

Our free assessment tool can help you identify where your current governance gaps are before you start building the framework.

Pillar 2: Policies and Procedures

Policies define what your organization will and will not do with AI. Procedures explain how to do it. Together, they turn leadership commitment into repeatable practice.

Why It Matters

Without written policies, your AI governance exists only in people's heads. When that person leaves, goes on holiday, or simply forgets, governance evaporates. Policies also serve as your first line of defense in a regulatory inquiry — they demonstrate intent and structure.

Article 17 of the EU AI Act requires providers of high-risk AI to establish quality management systems that include "policies, procedures and instructions" covering a range of topics from risk management to data governance. While this article technically targets AI providers, deployers who adopt similar practices are far better positioned for compliance and can demonstrate due diligence.

What Policies Do You Need?

Acceptable Use Policy. Define what AI tools are approved, what they can be used for, and what is off-limits. For example: "Employees may use ChatGPT for drafting marketing copy but must not input customer personal data, confidential business information, or employee data."

AI Procurement and Adoption Policy. Before anyone in your organization starts using a new AI tool, what process must they follow? This prevents shadow AI — the growing problem of employees adopting AI tools without organizational awareness or approval. Your policy should require a risk assessment before adoption, with higher-risk tools requiring leadership approval.

Data Handling Policy for AI. What data can be fed into AI systems? What data is prohibited? How do you ensure personal data is handled in compliance with both GDPR and the AI Act? This policy bridges your existing data protection framework with your AI governance framework.

Incident Response Policy. What happens when an AI system produces an incorrect, harmful, or biased output? Who do you report it to? What steps are taken? Article 26(5) requires deployers of high-risk AI to inform the provider and the relevant authorities of serious incidents.

Documentation and Record-Keeping Policy. What records must be kept, by whom, and for how long? Article 12 requires automatic logging for high-risk systems, and Article 26(6) requires deployers to keep logs for at least six months.

Practical Steps

Start small. You do not need five separate policy documents on day one. Begin with a single AI Acceptable Use Policy. This addresses the most immediate risks — unauthorized tools, inappropriate data sharing, over-reliance on AI outputs. Expand from there as your governance framework matures.

Make policies accessible. A policy buried in a SharePoint folder nobody checks is not a policy. Put your AI policies somewhere employees actually look — your intranet homepage, your onboarding materials, your team Slack channel.

Review regularly. AI capabilities and the regulatory landscape change rapidly. Review your policies at least every six months, or whenever you adopt a significant new AI tool.

You can use our AI systems inventory to catalog every tool your organization uses — this inventory forms the foundation that your policies build upon.

Pillar 3: Risk Management

Risk management is the operational core of your governance framework. It answers the question: what could go wrong with our AI systems, and what are we doing about it?

Why It Matters

Article 9 of the EU AI Act requires a risk management system for high-risk AI that is "a continuous iterative process planned and run throughout the entire lifecycle of a high-risk AI system." Even for AI systems that are not classified as high-risk, basic risk management is good practice and demonstrates due diligence.

For SMBs, risk management is also about prioritization. You have limited resources. A risk-based approach ensures you spend compliance effort where it matters most rather than spreading it uniformly across all AI usage.

The Risk Management Process

Step 1: Identify risks. For each AI system in your inventory, consider what could go wrong. Common AI risks include:

• Accuracy failures — The AI produces incorrect outputs that lead to bad decisions
• Bias and discrimination — The AI treats different groups unfairly
• Privacy violations — The AI processes personal data inappropriately
• Security vulnerabilities — The AI system can be manipulated by adversarial inputs
• Over-reliance — Employees trust AI outputs without verification
• Regulatory non-compliance — The AI system's use violates the AI Act or other regulations

Step 2: Assess likelihood and impact. Not all risks are equal. A chatbot occasionally giving a slightly inaccurate product description is a lower-impact risk than an HR screening tool systematically discriminating against female candidates. Assess each risk on two dimensions: how likely it is to occur, and how severe the consequences would be.

Step 3: Mitigate. For each significant risk, define mitigation measures. Common mitigations include:

• Human review before AI outputs influence decisions
• Regular accuracy testing and performance monitoring
• Data quality controls and bias audits
• Access controls limiting who can use certain AI systems
• Training programs ensuring users understand AI limitations
• Contractual requirements with AI providers for transparency and performance standards

Step 4: Document everything. Record your risk assessments, the mitigations you have implemented, and any residual risks you have accepted. This documentation is critical both for internal governance and for demonstrating compliance to regulators.

Step 5: Review and update. Risks change as AI systems evolve, as your usage patterns change, and as new vulnerabilities are discovered. Schedule regular risk reviews — quarterly for high-risk systems, biannually for others.

Practical Steps for SMBs

Use a simple risk matrix. You do not need enterprise risk management software. A spreadsheet with columns for AI system, risk description, likelihood (high/medium/low), impact (high/medium/low), mitigation measures, and owner is perfectly adequate.

Focus on high-risk systems first. If your risk assessment identifies any high-risk AI systems under Annex III of the EU AI Act, prioritize those. The compliance requirements and potential penalties are significantly higher. Our high-risk classification guide explains exactly how to determine whether your systems qualify.

Involve the people who actually use the AI. The person using the recruitment AI daily knows its quirks and failure modes better than anyone. Include frontline users in your risk identification process.

Pillar 4: Monitoring and Continuous Improvement

Building an AI governance framework is not a one-time project. AI systems change, your usage evolves, regulations get updated, and new risks emerge. Monitoring is how you keep your governance framework current and effective.

Why It Matters

Article 26(5) of the EU AI Act requires deployers of high-risk AI systems to "monitor the operation of the high-risk AI system on the basis of the instructions of use." Article 72 requires providers to establish post-market monitoring systems. These are ongoing obligations, not box-ticking exercises.

Beyond legal requirements, monitoring catches problems early. A high-risk AI system that was performing accurately six months ago may have degraded due to data drift, model updates by the provider, or changes in how your team uses it. Without monitoring, you discover problems only when they cause harm — which is too late.

What to Monitor

System performance. Are your AI systems producing accurate, reliable outputs? Track error rates, user complaints about AI outputs, and any cases where AI recommendations were overridden by human reviewers.

Usage patterns. Are AI systems being used as intended, or have employees found creative new uses that might change the risk profile? Shadow AI is particularly insidious — employees adopting new AI tools without going through your procurement process.

Compliance status. Are all your documentation requirements up to date? Are training records current? Have any new regulatory guidance documents or enforcement decisions changed how you should interpret your obligations?

Incident tracking. Log every AI-related incident, from minor inaccuracies to serious failures. Look for patterns — a single chatbot error is a data point, but a pattern of similar errors signals a systemic problem.

Provider changes. AI tool providers frequently update their models, terms of service, and data processing practices. Monitor these changes, as they can affect your risk profile and compliance status.

Practical Steps

Create a monitoring calendar. Define what gets checked, how often, and by whom. High-risk systems need more frequent monitoring — monthly at minimum. Lower-risk systems can be reviewed quarterly.

Use simple metrics. Track a small number of meaningful indicators rather than trying to monitor everything. For a customer service chatbot, you might track escalation rate (how often the chatbot fails to resolve an issue), customer satisfaction with AI interactions, and instances of incorrect information. For an HR screening tool, track the demographic distribution of candidates progressed versus rejected.

Establish a feedback loop. Create a simple process for employees to report AI issues — an email alias, a Slack channel, or even a form. Make it easy and non-punitive. The faster you hear about problems, the faster you can address them.

Conduct periodic governance reviews. At least annually, step back and assess whether your entire governance framework is working. Are policies being followed? Are risk assessments up to date? Has your AI inventory changed? Are there new regulatory requirements to address?

Keep audit trails. Document your monitoring activities and findings. If a regulator asks how you oversee your AI systems, you need to show a trail of continuous, structured monitoring — not just a policy document that has not been touched since it was written.

Pillar 5: Training and AI Literacy

The final pillar is arguably the most immediately urgent, because the obligation is already in force. Article 4 of the EU AI Act requires that all staff with AI-related responsibilities have sufficient AI literacy. This obligation has been active since February 2, 2025.

Why It Matters

A governance framework only works if the people within it understand their roles. Policies are useless if employees do not know they exist. Risk management fails if the people using AI systems cannot recognize when something goes wrong. Monitoring is impossible if nobody knows what to look for.

AI literacy is not just a compliance checkbox. It is the human infrastructure that makes everything else function. An employee who understands how AI works, what it can and cannot do, and what the regulatory requirements are, will naturally make better decisions about AI usage — reducing risk and improving compliance outcomes even without constant oversight.

What AI Literacy Must Cover

The EU AI Act does not prescribe a specific curriculum. Article 4 requires that training be "proportionate" to the role. In practice, this means a tiered approach:

All staff should understand:

• What AI is and how it broadly works (without requiring technical depth)
• That the EU AI Act exists and applies to your organization
• Your organization's AI Acceptable Use Policy
• How to report AI-related concerns or incidents
• The limitations of AI systems — that AI can be wrong, biased, or manipulated

Staff who regularly use AI tools should additionally understand:

• The specific AI tools they use and their intended purpose
• How to interpret AI outputs critically
• When to override or escalate AI decisions
• The data handling rules for AI systems (what can and cannot be input)
• Their monitoring responsibilities

AI governance leads and decision-makers should additionally understand:

• The EU AI Act's risk classification system
• The obligations applicable to your organization's AI systems
• The risk management and documentation requirements
• The consequences of non-compliance
• How to conduct or commission risk assessments and audits

Practical Steps

Start now. The AI literacy deadline has already passed. If you have not conducted any training, you are technically non-compliant today. Even a basic introductory session gets you moving in the right direction.

Make it practical, not theoretical. Employees do not need a lecture on transformer architectures. They need to know: what AI tools does our company use, what are the rules for using them, what should I do if something seems wrong, and why does this matter for our business and our customers?

Document completion. Keep records of who has completed training and when. This is your evidence of compliance with Article 4 and is essential for audit readiness.

Refresh regularly. AI literacy is not a one-and-done event. As your AI tools change, as the regulatory landscape evolves, and as employees join or change roles, training must be updated.

Use our platform. Our AI literacy training module is designed specifically for EU AI Act compliance, with role-appropriate content that satisfies Article 4 requirements while being practical and engaging.

Putting It All Together

An AI governance framework for an SMB does not need to be a 200-page document or require a dedicated department. Here is a realistic timeline for getting all five pillars in place.

Month 1: Foundation

• Designate an AI governance lead
• Build your AI systems inventory using our systems registry
• Run a compliance assessment to identify gaps
• Draft an AI Acceptable Use Policy
• Begin AI literacy training for all staff through our training module

Month 2: Risk and Documentation

• Classify all AI systems by risk level
• Conduct risk assessments for high-risk systems
• Draft additional policies (procurement, data handling, incident response)
• Document classification decisions and risk assessments

Month 3: Monitoring and Review

• Establish monitoring procedures and calendars
• Set up incident reporting channels
• Create a feedback loop for employees
• Conduct the first governance review and adjust the framework based on findings

Ongoing: Continuous Improvement

• Monthly monitoring reviews for high-risk systems
• Quarterly governance reviews
• Biannual policy reviews
• Annual comprehensive framework assessment
• Ongoing training as tools, roles, and regulations change

The Cost of Waiting

The August 2, 2026 enforcement deadline for high-risk AI systems is now less than six months away. Building a governance framework takes time — not because the work is impossibly complex, but because it involves organizational change, which cannot be rushed.

Businesses that start now have time to build something solid. Businesses that wait until July 2026 will be scrambling, making mistakes, and likely ending up with a paper framework that does not actually govern anything.

Fines for non-compliance with high-risk AI requirements reach EUR 15 million or 3% of global turnover. But the real cost is more subtle: reputational damage, loss of customer trust, and the operational disruption of trying to retrofit governance after a regulatory inquiry.

How AktAI Helps You Build Your Framework

AktAI is designed to operationalize every pillar of your AI governance framework:

• Assessment — Our compliance assessment identifies exactly where your governance gaps are, prioritized by risk
• Inventory and Classification — Our AI systems registry catalogs every tool and automatically classifies them against the EU AI Act's risk tiers
• Training — Our AI literacy training satisfies Article 4 requirements with role-appropriate, practical content
• Documentation — Audit-ready compliance documentation generated and stored in one place
• Monitoring — Continuous compliance tracking with alerts when your status changes

AI governance is not about perfection on day one. It is about building a structure that improves over time, keeps your organization compliant, and turns AI regulation from a burden into a competitive advantage. Businesses that govern their AI well do not just avoid fines — they use AI more effectively, build more customer trust, and make better decisions.

Start building your AI governance framework today. Take our free compliance assessment to see exactly where you stand and what to do next.