Sometimes the best AI decision is to keep part of the process simple. We start with the work your team wants to improve, then choose where AI judgement helps and where clear rules do the job better.
Identify recurring work and decisions
Think of the request that arrives every morning, the supplier email someone has to interpret, or the approval that gets stuck between teams. That is a better starting point than choosing a model or announcing an agent project.
If an approved form always goes to the same place, a straightforward workflow may be enough. If the information arrives in different formats and someone must work out what it means, AI may help with that specific step.
Define the agent’s responsibilities and permissions
An agent can choose tools or steps to reach a defined goal. That is useful when the route varies, but it also creates more ways to go wrong. OpenAI’s practical agent guide recommends checking whether that flexibility is actually needed.
Imagine an incoming service request. AI could suggest a category and draft the next action. The application still checks the customer record, required fields and permissions. A person can approve a consequential update. That combination often makes a useful first product.
Compare a limited pilot with the existing process
We would rather put a few real examples on the table than debate labels for weeks. Compare a rules-based workflow, a constrained AI step and an agent on the same tasks. Watch where each one saves effort and where people still need to step in.
Include confusing requests and missing information. Count useful completions, corrections, waiting time and running cost. A system that asks for help at the right moment can be more valuable than one that confidently guesses.
Evaluate results before expanding
Start with one workflow and a small set of tools. Keep a record of what the system received, what it tried and what happened. Expand the scope when the team has evidence that it works.
The interesting product opportunity often appears during these early trials: a better approval flow, a simpler customer journey or a useful new service. Keeping the first version focused gives those ideas room to emerge.
Before you start
- Name one task and its completion condition.
- List read actions, write actions and approval points.
- Prepare examples of successful, ambiguous and invalid requests.
- Assign an owner for exceptions and ongoing evaluation.
Related engineering work
MaidInHK combines recruitment matching, interview workflows and membership services. Its case shows how AI-assisted steps sit within a complete product journey; it is not a claim that every step is autonomous.
View the project case