Start with diagnosis
An AI consultant who starts with a demo is starting in the wrong place. McKinsey State of AI 2025 shows why: scaled value comes from redesigning workflows and transformation practices, not from isolated tools. The first conversation should expose the business process, decision owner, data sources, exception paths, and value metric.
IBM Institute for Business Value AI capabilities research is a useful second screen because it frames AI as a capability system. If the consultant cannot discuss data readiness, operating model, adoption, and measurement in the same conversation, the implementation will likely become a tool rollout instead of a business improvement project.
Require governance and integration evidence
NIST AI Risk Management Framework gives the evaluation checklist: map context, measure risks, manage controls, and govern accountability. Ask the consultant to show how those steps appear in their discovery, build, and adoption process.
Microsoft 365 Copilot data protection architecture is relevant even when the final system is not Copilot, because enterprise AI work usually touches identity, permissions, files, email, and collaboration data. A serious partner should know how access boundaries and auditability affect the build plan.
Score the partner before the software
PwC 2025 Responsible AI survey reinforces that responsible AI has to sit with the teams making decisions, not only with a central policy owner. The right consultant will name the business owner, risk owner, data owner, and adoption owner before estimating implementation work.
Use Human Renaissance AI transformation services and the AI Opportunity Score to evaluate the use case before a vendor-led demo sets the scope.
