Guide · 12 min read
AI Governance Framework for SMBs
A practical framework — adapted from RAISEF — that a small or medium-sized business can actually implement without a compliance team. Diagnose where you stand with SARI, then use this guide to close the gaps.
Why SMBs need AI governance now
AI governance sounds like an enterprise problem. It isn't. The moment your team pastes a customer email into a chatbot, connects a model to your CRM, or lets an agent take actions on your behalf, you have adopted AI — and you have inherited the risks. Small and medium-sized businesses feel those risks disproportionately: a single wrong output, a leaked record, or a biased decision can consume a week you don't have and cost trust you can't rebuild.
Governance for SMBs is not a policy binder. It is a small set of habits — who owns what, how you decide, what evidence you keep — that let you move fast and stay defensible. This guide adapts the RAISEF framework (Responsible AI Safety and Ethics Framework) into something a 10–200 person business can run.
Step 1 — Diagnose with SARI
You cannot govern what you have not measured. Before designing policies, run the SMB AI Readiness Index (SARI). It scores your business across General AI adoption, Agentic AI, and Responsible AI in about 13 minutes, and highlights the specific drivers where you are most exposed.
Treat your SARI result as the diagnostic that scopes the rest of this framework. High-scoring pillars need lighter governance; low-scoring pillars set your priority list. Re-run it quarterly — governance is a moving target as your AI use grows.
The three pillars
RAISEF organises responsible AI into three pillars. Think of them as the why, the how, and the so what of every AI decision you make.
Principles that keep AI aligned with human values — protecting people and data, making decisions explainable and contestable, and making responsibility clear.
Engineering discipline across the lifecycle — how systems are designed, tested, deployed, monitored and improved so they stay robust, secure and reliable in real conditions.
Broader effects beyond the product — informed use, accessible outcomes, real human agency, and trust with customers, employees and regulators.
The fifteen drivers, translated for SMBs
Each pillar breaks into drivers. Below is each RAISEF driver with the one question an SMB owner should be able to answer. If you can't answer it, that driver is your gap.
Ethical Safeguards
Who could this system disadvantage, and how would we know?
Can the people this affects actually use it and push back on it?
Where could our data or prompts skew outcomes, and what do we do about it?
Who signs off before this goes live, and who owns it after?
What personal data goes in, where does it end up, and can we delete it?
Operational Integrity
Do we have a written owner, review cadence and change log for each AI use?
Have we tested how this behaves on weird, rare or adversarial inputs?
Can our team see what the model is doing and why, well enough to debug it?
Can the affected user get a reason they understand — and contest it?
Are keys, prompts and data protected from misuse, exfiltration and injection?
What outputs or actions are unacceptable, and what stops them at runtime?
Societal Empowerment
Are the compute, cost and maintenance choices ones we can live with?
Where does a human review, override or escalate — with real authority?
Do users know they're interacting with AI, and what its limits are?
Does the system consistently do what we say it does?
Governance across the AI lifecycle
Governance is not a launch checklist — it threads through the whole lifecycle. RAISEF names seven stages; for SMBs, four is usually enough:
- Frame — Write one paragraph: the problem, who is affected, and which drivers matter most. Kill the project here if the risk clearly outweighs the benefit.
- Build — Pick a named owner, log data sources and prompts in a shared doc, and decide up front what "good" looks like on your top three drivers.
- Launch — Run a small pilot, capture what broke, and disclose to users that AI is involved. Approve the wider rollout in writing.
- Operate — Review it quarterly against the same drivers, watch for drift, and document any incident with what you changed as a result.
A minimum viable governance checklist
Everything below fits on one page. If you can tick these, you have credible AI governance for an SMB:
- An inventory of every AI system your business uses, with a named owner for each.
- A one-page acceptable-use policy your team has actually read.
- A rule that no personal or confidential data goes into a public model without sign-off.
- A written review and approval step before any AI-driven action affects a customer.
- A logged human-in-the-loop for anything with money, employment or legal consequences.
- A quarterly review — SARI re-run, incidents reviewed, top-driver gaps updated.
- A named person to contact if a customer or employee wants to contest an AI outcome.
Next steps
Start with the measurement, not the policy. Take SARI, look at your lowest-scoring drivers, and pick the two you'll close in the next quarter. Come back to this guide when you're ready to write it down.
This guide adapts the RAISEF framework by Ricky Sinnott et al. for a small-business audience. For the full academic treatment, read the source at raisef.ai.