Evaluate the operating method before the demo
A polished demo tells you very little about whether an AI roadmap engagement will work inside a real SMB or mid-market operating model. RSM middle-market AI survey, the OECD report on AI adoption by small and medium-sized enterprises, and Deloitte State of AI in the Enterprise 2026 all support a practical screen: AI programs need workflow ownership, data readiness, and a path from experiment to production.
Before a demo, ask the consultant to map one workflow, name the source systems, identify the data owner, and explain the review model. If the answer starts with features instead of operating evidence, the project is not ready for approval.
Use the AI readiness assessment buyer guide as the first filter.
Price the hidden work, not only the visible license
NIST AI Risk Management Framework and CISA AI Data Security Best Practices make the implementation cost visible. The budget needs room for source cleanup, permission design, testing, review workflows, retained logs, user training, and support after launch.
OpenAI Enterprise Privacy is also a reminder to verify the actual enterprise data controls for any tool or implementation partner. The buyer should know what data is submitted, how access is governed, what is retained, and who can approve production use.
Use the 90-day AI implementation plan to compare roadmap promises against a practical sequence.
Score the consultant on production evidence
The best evaluation question is simple: what will be true at the end of the first production workflow that is not true today? A strong consultant can define the baseline, workflow owner, adoption cadence, review process, and decision rule for continuing or stopping.
For SMB and mid-market companies, the output should be an implementation path the leadership team can manage, not a broad strategy deck or tool comparison. The first milestone is an accountable workflow with a measurable operating result.
Use AI ROI measurement without fake savings to keep the decision grounded.
