AI governance
AI in Technology Procurement: Where It Helps and Where It Should Stop
For CIOs, procurement, security and executive sponsors
AI can accelerate research, structure requirements, identify inconsistencies and help teams work through large volumes of information. It becomes risky when generated output is treated as evidence or when an opaque model effectively becomes the decision authority. The safer pattern is AI assistance inside a governed process with traceable sources, visible uncertainty and accountable human approval.
Use AI for acceleration, not authority
AI can support synthesis, classification and analysis, but consequential conclusions should remain tied to evidence and human responsibility.
Treat generated content as generated content
An AI answer is not proof merely because it is fluent. Material claims need traceable evidence, and unsupported outputs should not silently enter the decision record as facts.
Bound access and actions
The AI layer should have only the information and capabilities required for its task. Security and authorisation controls should exist independently of prompts or conversational behaviour.
Questions buyers ask
Practical questions, bounded answers.
Why not just use ChatGPT or Copilot?
General-purpose AI can help with research and drafting, but it does not by itself provide a governed decision record, controlled evidence provenance, explicit uncertainty, accountable approval or durable decision history.
Can AI make the final recommendation?
AI can assist analysis, but accountable human approval should remain explicit for consequential enterprise technology decisions.
Need to apply this to a real decision?
Move from general guidance to a governed decision context.
PROVE TDI structures the requirements, evidence, alternatives, uncertainty and accountable conclusion for a specific enterprise technology decision.