Give AI a Better Starting Point
“Before I start, here’s how I’m reading this.”
That is a useful exchange between colleagues. It is also a useful step to build into an AI workflow.
To give AI a better starting point, define the task, provide the relevant source information, and have it outline its understanding before it acts. The person responsible for the result can then refine the direction and contribute context that would otherwise stay in their head.
For an established business, this creates an opportunity to turn experience into instructions and examples that support more of the team. AI can take on preparation while experienced people shape the work and decide what happens next.
Bring your team’s experience into the workflow
An experienced operator knows which information matters, which exception needs attention, and what someone needs before they can make a decision. Those details are part of how a business gets work done.
When a colleague takes on a task, that knowledge often travels through a conversation. Someone adds a customer’s history, points to the right record, or explains what the last project taught them. An AI workflow benefits from a deliberate place for that context, too.
Start by having the agent state what it understands the task to be, show the information supporting that interpretation, and propose its next step. The person who owns the outcome can check that summary against the original request and add what is missing.
A well-chosen review point gives the agent clearer direction while the work is still easy to shape.
What a useful first exchange looks like
Consider a hypothetical customer email asking what happens to their records if they cancel.
Before preparing the response, an agent could outline its understanding:
- Request: The customer wants to understand what happens to stored records after cancellation.
- Interpretation: This is a policy question. The customer has not requested an account change.
- Source: The original customer email.
- Context to confirm: The applicable retention policy and any contract-specific terms.
- Proposed next step: Prepare an answer using the confirmed policy, leaving account status unchanged.
The person reviewing it now has a focused job. They can confirm the interpretation, supply the relevant policy, and add relationship context before the agent drafts a response.
The agent can then handle more of the preparation with a clearer brief. The operator can spend their attention on the details that require their experience.
Where should human review sit in an AI workflow?
Place review where a person’s knowledge can materially improve the direction or where an action requires their authorization. Keep final review when the outcome calls for it. Routine, well-tested work may need exception handling rather than approval at every step.
Anthropic’s guidance on building effective agents describes intermediate checks, feedback from the working environment, and human checkpoints. Our recommendation applies that pattern to business work: give the person responsible a clear opportunity to shape an agent’s interpretation before it drives the next action.
Ask the agent to show:
- What it understands the task to be
- Which source information supports that interpretation
- What context or information is still missing
- What it proposes to do next
Check the original records as well as the summary. An agent’s explanation helps focus the review, but the evidence still matters.
Give AI examples of work worth repeating
A useful customer brief, a clear exception queue, or a complete handoff shows an agent what the people receiving the work need. Include examples alongside the task instructions, with an explanation of what makes each one useful.
Start with the work itself. Who uses the result? What decision will they make with it? Which policies or prior actions must the agent consider? What should it do when information conflicts or falls short?
The answers make an operator’s expectations more explicit. As the team refines the workflow, those instructions can become a starting point for colleagues handling similar work.
That is how knowledge from one process can become useful beyond the person who first worked it out. Our four stages of AI maturity explore how capability can spread across a business.
How to test your first AI workflow
Choose one recurring piece of work with a clear owner, known inputs, and a result someone can judge. A bounded task gives the team a practical way to test its instructions and learn where human input adds value.
Agree on the questions the test needs to answer:
- What should the agent prepare, and what remains the person’s decision?
- Which information and examples will help it do that work?
- When should it pause for clarification or approval?
- What would show that the finished work is more useful?
Test examples with known outcomes, including requests that need clarification. Compare the agent’s interpretation and proposed action with the owner’s assessment. Track time spent preparing and reviewing the work, the amount of correction needed, and whether the result helps someone take the next step.
Use what you learn to improve the instructions before expanding the workflow. For help choosing the starting task, read Where to Use AI in Your Business: Start With the Work.
Turn a useful exchange into shared capacity
The first workflow gives your team a place to practice making its knowledge explicit. Each refinement can become an instruction or example the next person can use.
That creates a practical route to more capacity: AI handles more of the preparation, and experienced people have a clearer place to contribute their judgment. As the workflow earns trust through testing, the team can decide which steps are ready for greater independence.
Start with one piece of work your people understand well. Define the job, give AI the context, and test the result together.
FAQ
How do you give an AI agent better context?
Define the task, identify who will use the result, and provide relevant source information and examples of useful work. Have the agent outline its understanding so the person responsible can refine the direction before it continues.
Where should human review sit in an AI workflow?
Place review where a person’s knowledge can materially improve the direction or an action needs their authorization. Keep final review where appropriate, and use exception handling for routine work that has been tested.
What is a good first AI workflow for an established company?
Choose a recurring task with a clear owner, known inputs, and an outcome people can assess. Agree on what the agent prepares, which decisions stay with a person, and how the team will judge the result.
How can one team’s AI learning help the rest of the business?
Document the instructions, examples, and review decisions that make the workflow useful. Other teams can use those materials as a starting point, then test and adapt them to their own work.
Related reading
- Where to Use AI in Your Business: Start With the Work: Choose a bounded piece of work worth testing before expanding to a larger process.
- The Four Stages of AI Maturity: Understand how useful AI capability can spread from individual teams to the wider business.
