How to Evaluate an AI Vendor: 8 Questions Every SMB Should Ask Before Signing

Introduction

The 8 Questions to Ask

• 1. Do you integrate with our existing systems, or do we adapt to yours? — The right answer is integration. AI workflows that require you to change how your team works to fit the tool create adoption problems, not solve them. A serious vendor should be able to list the specific integrations relevant to your stack.

• 2. Where does our data go, and does it ever train your models? — This is non-negotiable. Your business data should never be used to train AI models that could expose it to other users or third parties. IBM's 2025 Cost of a Data Breach Report found that shadow AI added an average of $670,000 to breach costs. A serious vendor will have a clear, written answer.

• 3. What does your security architecture actually look like? — 'Enterprise-grade security' is a marketing phrase. Ask for specifics: data encryption standards, access control mechanisms, audit trail capabilities, and deployment options. If your industry has compliance requirements — HIPAA, SOC 2, PCI-DSS — ask explicitly whether those are supported.

• 4. What does human-in-the-loop look like in your systems? — A vendor who can't describe their human review mechanisms in concrete terms is building fully automated systems — which means when errors occur, there's no graceful recovery. Ask: at which specific points does a human review AI output?

• 5. How do you measure ROI, and what do you guarantee? — A serious vendor should be able to describe how they track the performance of the workflows they build: cycle time reduction, error rate reduction, hours saved per week. If they can't describe what success looks like in measurable terms, they're not serious about delivering it.

• 6. What does post-launch support look like? — AI workflows require ongoing maintenance: models drift, data sources change, business processes evolve. A vendor who builds and disappears leaves you with a system you can't maintain.

• 7. How long does implementation actually take? — Well-scoped AI workflow projects should move from discovery to production in 6 to 12 weeks. Longer timelines often reflect over-engineering, under-resourced teams, or a mismatch between the vendor's capabilities and your needs.

• 8. Can you show us a reference from a similar business? — Not a case study — a reference. Someone you can call and ask what the implementation experience was actually like, whether the ROI materialized, and whether they'd do it again.

Why These Questions Matter