AI Governance for EU SMEs - A Practical Setup Before AI Usage Scales
AI, GDPR
AI is now used across almost every team. Support uses writing assistants. Sales uses call and meeting summaries. Developers use coding copilots. Marketing uses AI for text and image drafts.
That is normal.
The challenge starts when usage scales: there is no shared view of what is allowed, what is risky, and who is accountable.
For small and medium-sized companies, this is where governance matters most. Not as bureaucracy, but as a practical way to prevent predictable mistakes.
This article outlines a practical AI governance baseline for SMEs operating in the EU.
Why SMEs need AI governance now
Most SMEs are not building high-risk AI systems from scratch. They are adopting third-party tools quickly to save time.
The risk is not ambition. The risk is unstructured adoption:
- personal data is pasted into external tools without review
- teams automate decision steps without legal or privacy checks
- no one can clearly explain which AI tools are in use
- legal, privacy, and engineering work from different assumptions
If a customer asks how AI is used in your operations, “we are still figuring it out” is not a strong answer.
What “good enough” AI governance looks like
You do not need a 90-page policy to start. You need a minimum operating model that teams can actually follow.
A practical baseline has five parts:
- Ownership
- AI inventory
- Risk tiers and usage rules
- Data handling controls
- Review cadence
1) Assign ownership first
If everyone is “sort of responsible,” no one is.
Assign at least these roles:
- Business owner: defines purpose and expected business outcome
- Privacy/legal owner: validates GDPR and contractual requirements
- Technical owner: manages integrations, access, and security controls
In many SMEs, one person can hold multiple roles. That is fine. What matters is explicit accountability.
2) Build a simple AI inventory
Start with a spreadsheet if needed, then move to a structured system. For each tool or use case, track:
- tool and vendor
- purpose (which process it supports)
- data inputs (especially personal data categories)
- whether outputs are used in decisions about people
- legal basis, DPA status, and retention notes
- owner, approval date, and review date
This inventory becomes your source of truth for customer due diligence, audits, and internal reviews.
3) Define risk tiers with clear rules
Not every AI use case has the same risk profile. Classify quickly and apply clear approval rules.
Low risk
Examples: drafting generic copy, summarizing internal notes without personal data.
Rule: Pre-approved if standard guardrails are followed.
Medium risk
Examples: productivity workflows that may include business-sensitive or limited personal data.
Rule: Owner approval plus documented data handling review.
High risk
Examples: use cases affecting people’s rights or significant outcomes (for example candidate filtering, automated acceptance/rejection, profiling-heavy decisions).
Rule: Legal/privacy review before launch, documented rationale, and meaningful human oversight.
4) Put data handling controls in writing
“We trust the tool” is not a control.
Document minimum rules such as:
- no sensitive personal data in unapproved tools
- no production customer data in prompt testing
- only approved workspaces and managed accounts
- retention and deletion expectations per tool
- clear approval for who can enable integrations
For chatbot use cases in particular:
- assume users may enter personal data
- design prompts and UI copy to reduce oversharing
- add operational checks for accidental personal data processing
GDPR focuses on actual processing outcomes, not good intentions.
5) Run a monthly review rhythm
Governance is not a one-time setup.
Run a 30-minute monthly review to cover:
- newly adopted tools
- scope changes in existing tools
- incidents and near misses
- controls to tighten, simplify, or retire
This keeps governance active without creating heavyweight process.
Common pitfalls to avoid
1) Policy without implementation
A PDF policy alone is not governance. Teams need operational rules in daily workflows.
2) Legal-only ownership
AI decisions affect engineering, support, and operations too. Keep ownership cross-functional.
3) “We are too small to need this”
SMEs move fast, so unmanaged risk can spread fast as well.
4) No customer-facing narrative
B2B customers increasingly ask AI, privacy, and security questions. Prepare clear and consistent answers.
A practical 14-day rollout plan
Days 1–3: Assign owners and map all current AI tools.
Days 4–7: Classify use cases by risk tier and define minimum usage rules.
Days 8–10: Review chatbot and automation flows for personal data risk.
Days 11–14: Publish internal guidance and schedule the monthly review.
Done is better than perfect. Start small, then improve each month.
Final thought
AI governance is not about slowing teams down. It is about helping teams move faster with fewer surprises.
When legal, privacy, and technical teams work from the same inventory and rules, you get both speed and control.
This article is for general information and does not constitute legal advice. For legal interpretation, consult qualified counsel.
Want a practical starting point?