Setting Realistic AI Expectations
Large language models are effective at working with language and patterns, but fluency is not the same as correctness. Set expectations around the task, evidence, and impact rather than around a broad promise that “AI understands the business.” Translate the agreed boundary into production Agent Prompts, apply it through agents and workflows, then use Feedback to check whether real outputs meet it.
Use AI Where the Work Fits
AI is usually a strong assistant for summarizing, drafting, classifying, translating, extracting structured fields, comparing documents, brainstorming alternatives, and answering questions over approved sources.
Use Choose Tasks AI Can Do Well and Recognize Tasks AI Should Not Own when communicating these boundaries to pilot users.
Reliability decreases when work depends on:
- exact calculations without a calculation tool,
- current facts without a live connection,
- pixel-perfect measurements from technical images,
- complete recall outside the supplied context,
- deterministic wording across repeated runs,
- legal, medical, financial, or safety judgments without expert review.
Define an Operating Boundary
For every production agent or workflow, document:
| Boundary | Example decision |
|---|---|
| Allowed | Draft a response from approved support articles |
| Allowed with review | Compare contract clauses and flag possible differences |
| Tool required | Calculate totals using a controlled calculation function |
| Human decision | Approve a contract, payment, hiring decision, or safety action |
| Prohibited | Invent missing evidence or bypass an access restriction |
Communicate Uncertainty
Prompts and workflow instructions should require the system to separate sourced facts, interpretations, assumptions, and open questions. When evidence is missing, the correct response is to identify the gap—not to complete the story.
For consequential work, require citations or source links and make the reviewer confirm that the cited material actually supports the conclusion. See AI Safety & Quality for evaluation and release controls.
Set a Better Success Measure
Avoid promising a universal accuracy or time-saving percentage. Measure the deployed workflow instead:
- reviewer acceptance rate,
- correction rate and severity,
- time to a verified outcome,
- coverage of required evidence,
- failed or rejected tool actions,
- user adoption in the target process.
An initiative succeeds when it improves a defined outcome inside an acceptable risk boundary. A visually convincing response is not, by itself, a successful result. With that boundary agreed, continue to Finding AI Initiative Candidates.