A convincing AI demonstration can produce a polished answer in seconds. A useful business application has a longer job: it must understand the task, use the right information, fit the team’s process, and make errors visible enough to correct.
Generative AI can create and transform content such as text and code. The product decision is where that capability belongs in a workflow. Our starting point is a specific task, a defined user, and a result that someone can inspect.
Start with one task people already do
Describe the current work before choosing a model. Who performs it? What information do they need? What makes an output acceptable? A narrow task makes it easier to compare the AI-assisted process with the existing one.
For an initial release, favor work with available inputs and a clear review step. Drafting a service response, preparing a document summary, or assembling a research brief gives the team a concrete artifact to evaluate.
Choose how much freedom the system needs
A fixed workflow follows steps the application defines. An agent can select tools and decide some of its next steps. Anthropic’s engineering guidance distinguishes these approaches and recommends adding complexity only when it improves the task.[1]
A predictable sequence may be sufficient for a document summary. A research task with unknown follow-up questions may benefit from more flexible orchestration. Compare the additional quality with the extra latency, cost, and debugging effort.
Make review part of the experience
Design the reviewer’s screen as carefully as the generated answer. Put relevant source material beside the draft. Make missing inputs, failed lookups, and unresolved questions easy to see. A reviewer should be able to correct a field or reject a draft without restarting the entire task.
Separate preparing an action from executing it. For example, the assistant can assemble a follow-up message while an authorized person decides whether it should be sent. The handoff should reflect the consequence of the action and the team’s operating rules.
Measure completed work and correction effort
Build a small evaluation set from representative tasks, including incomplete inputs and awkward exceptions. Agree on the review criteria before tuning prompts. Keep a baseline from the existing process so a faster draft does not hide a longer correction cycle.
- Task completion: does the output satisfy the actual request?
- Review effort: what must a person correct before using it?
- Operating cost: what does an accepted result cost, including retries?
- Reliability: what happens when a source or tool is unavailable?
Release a workflow with an owner
Give the first release a named business owner, a support path, and a process for reviewing failures. Record which version of the prompt and configuration produced an output so the team can investigate changes.
Expand only after the team can explain where the application helps and where it needs intervention. A well-understood workflow creates a stronger foundation for the next use case than a broad assistant with unclear responsibilities.
PUT IT INTO PRACTICE
Choose one useful task, define the review step, and evaluate the complete workflow. The quality of the handoff matters as much as the quality of the generated text.