Running AI in your business without unpleasant surprises starts with a careful AI check. Especially in 2025, with the EU AI Act coming into effect and customers becoming more critical about privacy, bias, and transparency, you do not want to improvise. The checklist below is written for SMB teams in B2B, such as wholesalers, distributors, suppliers, accounting and legal boutiques, installation companies, and real estate brokers. The goal is simple: faster value, less risk, demonstrably compliant.

How to use this checklist

Treat each section as a short control round with three possible outcomes: approved, attention point, or blocker. For each item, note the evidence you have, for example a policy, log file, or test report. If you hit a blocker, pause even a small pilot first, or switch to a safer scope with less data and a human-in-the-loop.

This checklist focuses on safe production deployment. If you are only running an experiment or proof of concept, still include the items around privacy, security, and logging, but in a lighter form. This helps prevent a “small test” from quietly ending up in production.

1. Purpose and risk class

Define the concrete business problem you are solving and determine the risk class under the EU AI Act. Many SMB use cases like quote assistance or CRM summaries typically fall into a limited risk category, but functions like credit assessment or HR screening can be higher risk and require additional safeguards.

• Describe the business goal, KPIs, and who owns the outcome.

• Classify the risk and document why that assessment is appropriate.

• State what is out of scope to prevent scope creep and additional risk.

2. Legal basis and privacy

Verify that you have a valid legal basis for processing and that personal data is truly necessary. Minimize, pseudonymize, or anonymize wherever possible. For higher-risk processing, perform a DPIA.

• Document purpose, legal basis, and retention periods, including rights of access and deletion.

• Describe how you detect PII and automatically redact it before data reaches the model.

• Check whether outputs contain personal data and how you securely handle or delete it.

3. Data minimization and data quality

If AI gets garbage in, it produces garbage out. Start small, with clean and relevant data. Be explicit about which sources are reliable and which must not be used in training or prompts.

• Create a whitelist and blacklist of allowed and prohibited data sources.

• Define validation rules, such as required fields or threshold values.

• Document known data quality issues and plan production fixes.

4. Vendor, region, and contracts

Ask vendors for absolute clarity on data usage and storage location. Choose an EU region or adequate safeguards where possible. Restrict model training on your data unless you explicitly want it.

• Check whether your data is used for model training or only for inference.

• Document DPAs and subprocessors, including transfers outside the EU.

• Verify uptime, support, rate limits, and cost caps in the SLA.

5. Security and access control

Treat AI systems like production systems. That means identity-first, principle of least privilege, and logging around API keys.

• Use role-based access, secrets management, and network restrictions.

• Apply data loss prevention and PII redaction on input and output.

• Enable audit logging for tokens, prompts, outputs, and decisions.

6. Threat model for LLM-specific risks

LLMs have unique attack vectors such as prompt injection, jailbreaking, and data exfiltration via tools or connectors. Design mitigations upfront.

• Filter and normalize prompts, use system prompts with clear boundaries.

• Isolate tool actions and check authorizations and data scopes in advance.

• Run red teaming or automated tests against injection and jailbreak scenarios.

7. Quality, evaluation, and guardrails

Define what “quality” means for your use case, set thresholds, and test with real cases. Use a human-in-the-loop when the risk is meaningful.

• Define metrics such as accuracy, completeness, latency, and cost per task.

• Add content guardrails such as prohibited claims, source requirements, or style rules.

• Document failure modes and what happens when the AI is not confident enough.

8. Bias and non-discrimination

Prevent models from making unwanted distinctions, especially in customer selection, pricing, or hiring. Measure bias with representative datasets and improve where needed.

• Hide or limit sensitive attributes if they are not required for the task.

• Evaluate outcomes per segment and adjust data curation or rules when results deviate.

• Explain how decisions are made and provide objection and remediation paths.

9. Human-in-the-loop and accountability

Set clear decision rights. The AI can propose, a staff member makes the final decision for higher-risk situations.

• Define who signs off when there is doubt and how escalation works in the workflow.

• Have the AI report its own uncertainty and request sources or justification.

• Train employees to review and improve AI output critically.

10. Explainability and transparency

Especially in B2B, customers want to know how you arrived at prices, advice, or classifications. Provide an understandable explanation.

• Show sources or references where possible and reduce hallucinations with RAG.

• Document decision rules or heuristics alongside the model.

• Communicate to customers when AI is used and what that means.

11. Operational monitoring and cost control

AI that performs well today can degrade next month. Continuously monitor performance, cost, and error types. Set budget limits and alert on anomalies.

• Log success rates, rejections, improvement loops, and time to completion.

• Use cost caps, watch for token explosion, and batch processing where appropriate.

• Retrain or recalibrate periodically and track versions clearly.

12. Integrations and data flows

Most risks arise at the edges, not inside the model. Limit which systems the AI can control and run pre-checks on actions.

• Create a data flow diagram showing sources, transformations, and destinations.

• Use sandbox mode or dry-run before allowing real actions.

• Protect idempotency and prevent double postings in ERP, CRM, and finance systems.

13. Incident response and kill switch

If something goes wrong, you want to fall back quickly and in a controlled way. Prepare clear scenarios and a central stop button.

• Define incident types, who alerts, and who decides to shut down.

• Keep a manual fallback ready, including checklists and scripts.

• Run an outage scenario drill at least once per quarter with the team.

14. Change management and training

Adoption often fails because of behavior, not technology. Make AI easy to use, communicate the rules, and celebrate quick wins.

• Provide short, task-focused training, guidelines for prompts, and data safety.

• Appoint AI champions per team and set a rhythm for feedback and improvements.

• Establish a compact AI policy for what is and is not allowed with customer data.

15. Audit trail and evidence

Part of safe deployment is being able to prove you took the right steps. Collect evidence from day one.

• Store changelogs, model versions, test sets, evaluations, and acceptance decisions.

• Record which risks are covered per release and which residual risks were accepted.

• Archive customer communications that AI contributed to, with source references.

Sector-specific considerations for SMBs

Wholesale and distribution require extra attention to pricing logic, discount rules, and EDI. Do not let AI introduce unintended price discrimination, and ensure quote assistants handle GS1 or EAN data correctly. Check inventory recommendations for explainability for purchasing and sales.

B2B suppliers and manufacturers deal with product specifications, CE documentation, and service agreements. Ensure AI never makes incorrect safety claims, and that maintenance recommendations are reproducible.

Accounting and legal boutiques work with confidential files. Apply strict PII redaction, log every AI recommendation with sources, and require a professional to approve the final advice. Transparency and retention obligations are leading.

Installation companies and field services dispatch technicians using AI planning. Always add a human check for safety-critical decisions, such as gas, electricity, or structural load-bearing capacity. Document the rationale.

B2B real estate brokers must prevent discrimination in lead qualification and viewing scheduling. Test selection criteria for fairness and explain how leads are assessed.

For inspiration from highly regulated domains, responsible deployment is clearly possible. In healthcare and insurance, AI is successfully used for fraud detection, data security, and faster claims, see for example this analysis on the impact of AI on the health insurance sector: AI in healthcare and insurance.

Quality gates for a safe go-live

Go live in small steps with clear gates. Start in shadow mode, where AI runs alongside without taking actions. Compare outcomes with human decisions, and only move forward when deviations stay within your thresholds. Then enable actions with limited scope and keep dual control temporarily. Increase autonomy only when stability, costs, and customer impact are demonstrably strong.

Mini roadmap: 45 minutes today, results in 14 days

Start small and structured. Within one workday you can establish the basics, and within two weeks you can see measurable value.

• Write the goal, KPIs, and risk class on a single page and assign an owner.

• Draw a simple data flow diagram and mark where PII may exist.

• Set up a sandbox with logging, PII redaction, and a manual fallback.

• Create 10 realistic test cases with expected outcomes and acceptance criteria.

• Run a 5-workday shadow pilot and evaluate together with operations.

Common pitfalls, and how to avoid them

Scope creep often sneaks in via ever more data integrations. Stick to the plan and route changes through change requests. Hidden costs come from tokens and peak load, so set budget limits and batch where possible. Blind trust in a polished demo is risky, so test with your data and real exceptions. Finally, do not underestimate the human side, without clear rules and training, rollout is likely to stall.

FAQ

Is an AI check mandatory for SMBs? Not always legally, but in practice it is wise. The EU AI Act requires extra measures for certain applications. A structured check prevents mistakes, claims, and reputational damage.

When do I need a DPIA? When your processing is likely to result in a high risk to the rights and freedoms of individuals. Think of large-scale processing of personal data or automated decision-making with significant effects.

How do I know if my use case is high risk? Check against the EU AI Act categories and sector rules. HR selection, creditworthiness, and safety-critical applications more quickly fall into a higher class than, for example, a quote copilot.

How do I measure quality without a gold standard? Use a review protocol with multiple reviewers, define thresholds, and log deviations. Work with RAG and source references to improve factual accuracy.

What if my vendor hosts outside the EU? Put appropriate safeguards and contracts in place, such as model settings that prevent training on your data, SCCs, and transparency about subprocessors. Prefer EU regions when possible.

Should I let employees prompt, or automate everything? Start with guided prompting with clear guidelines. Automate only after a stable shadow pilot, with guardrails and a human-in-the-loop where risk is meaningful.

If you want a fast, no-nonsense AI check for your processes, including risk class, privacy mitigations, and a feasible pilot setup, schedule a free Quick Scan with B2B GrowthMachine. We help SMB teams go live safely, with visible value within weeks and ongoing governance that fits your scale.

Note: This article is informational and not legal advice. Consult your lawyer or DPO for interpretation of laws and regulations in your context.