Generative AI can draft, explain, summarise and interpret unstructured information. Its business value depends on where those outputs enter a workflow. A draft reviewed by a trained employee has different risks from an automatically issued decision or a change to a customer account.
Start with a task where the evidence, expected output and reviewer are clear. Avoid selecting a use case solely because a model can produce an impressive sample. The recurring workload and recovery path matter more than the demonstration.
Use cases with inspectable outputs
Document summarisation can help a reader navigate a long report when the original remains available. A knowledge assistant can connect a question to authorised documents. A support tool can draft a reply based on a verified order record.
For a hypothetical procurement team, AI might summarise supplier proposals into an agreed comparison structure. A buyer still checks missing assumptions, pricing and contractual conditions. The system should distinguish quoted facts from its own interpretation.
Compare the workflow fit
| Candidate | Useful first scope | Boundary to retain |
|---|---|---|
| Support replies | Draft from verified status and policy | Staff approve sensitive commitments |
| Internal research | Find and explain source passages | Evidence and access rules remain visible |
| Proposal comparison | Extract a structured summary | Buyer verifies commercial meaning |
| Meeting notes | Organise agreed actions | Participants confirm consequential decisions |
| Document intake | Suggest fields and categories | Review uncertain or conflicting values |
These are illustrative uses, not claims that every organisation will achieve a particular saving.
Examine source readiness
A knowledge assistant needs current, authorised and understandable source material. If policies conflict, a model cannot resolve ownership merely by writing confidently. Improve the documents or expose the conflict before building an automatic answer process.
For drafting tasks, define which input may be sent to a provider and what should be removed. An application can accidentally include sensitive attachments or full customer histories when a small set of fields would suffice.
Measure the real review burden
A generated answer is not necessarily faster to use if a reviewer must reconstruct every fact. Compare the current task with the full proposed process: preparation, generation, verification, corrections and handoff.
Measure where errors occur. A summary can omit a qualification while remaining grammatically clear. An extracted number can lose its unit. A support draft can promise a resolution the operational system cannot perform.
Keep actions separate from text
If the workflow can create records or trigger external operations, validate those actions through business services. The model's explanation should not bypass permissions, amount limits or approval rules.
Use explicit confirmation for consequential actions and return a real transaction reference. If an external tool times out, check whether the action occurred before retrying. Generative output should never be the only evidence that the operation succeeded.
Plan a bounded pilot
Choose a representative task set, including missing evidence and difficult cases. Agree an acceptable correction rate, handoff behaviour and permitted data. Compare against the existing workflow and a simpler non-generative approach where possible.
A useful first release has a narrow purpose, visible sources or inputs, and a reviewer who knows what to check. Expand only when the evidence shows that the workflow remains useful and controllable. This is a stronger basis for investment than a broad promise to add AI everywhere.
Primary references
Related reading
- What Is RAG? Source-Grounded AI for Business Documents
- Document AI Human Review: Decide What Needs Checking
Explore our ai solutions if you need help turning these choices into a reviewable project brief.