Generative AI: what is useful, what is risky, and what to test first

The executive question

The useful question is not "Should we use generative AI?" It is "Which decision, handoff, review, or knowledge task becomes better if this capability is placed inside the workflow with clear evidence and accountability?" If that sentence cannot be completed, the work is not ready for tooling, procurement, or a public promise.

Where it can create value

The strongest use cases are close to work people already understand: internal assistants, document workflows, support drafting; evaluation, monitoring, and human review; integration into existing tools and responsibilities. In these cases, AI should reduce search, preparation, comparison, drafting, or review effort while keeping the final business judgment visible. If the team cannot inspect why an output is useful, the use case is not mature enough.

• internal assistants, document workflows, support drafting
• evaluation, monitoring, and human review
• integration into existing tools and responsibilities

What must be true before a pilot

• There is a named workflow owner who can decide what good output means.
• The system has allowed sources, permissions, and data boundaries before anyone tests live work.
• There are example cases that include normal work, edge cases, and unacceptable failure modes.
• Users have a way to reject, correct, and escalate weak output without slowing the process more than before.

Implementation blueprint

• Write the workflow in plain language: trigger, input, decision, output, reviewer, and follow-up action.
• Build a narrow evaluation set before building the interface, otherwise the demo will judge itself.
• Choose architecture after the data boundary is known: public API, private cloud, local model, retrieval layer, or hybrid pattern.
• Ship the pilot with logging, review rules, owner handover, and a stop condition.
• Review real use after launch: the most valuable findings often appear in rejected outputs and user workarounds.

Questions buyers should ask

• Which source, rule, or person does the system rely on when cases conflict?
• What will be logged, and who can inspect the trace when something goes wrong?
• How does the system show uncertainty, missing context, or forbidden action?
• What gets better for the team in week four, not only in the first demo?
• Who owns the process when the model, data, or policy changes?

Common failure patterns

• the team buys a tool before naming the workflow, reviewer, and operating owner
• evaluation ignores weak, ambiguous, and adversarial cases involving generative AI
• the pilot drifts toward source-free answers in regulated decisions before review paths exist

When not to use it

Do not use generative AI for source-free answers in regulated decisions. It is also the wrong choice when nobody can explain what a correct output looks like, or when the organization wants the model to absorb responsibility that should stay with a person or process owner.

How to measure progress

Measure output usefulness, review effort, source quality, risk reduction, and adoption by the people doing the work. Do not use model accuracy alone as the business metric. A technically impressive system can still be a poor investment if users distrust it, reviewers need too much time, or support ownership is unclear.