You ask for a piece of work to be finished, receive a confident “done,” and forward it to the next person. A little later, it comes back with questions. The summary looks good, but someone still needs to work out what happens next.
That is a frustrating place to be as a business owner or leader. You delegated the task to create room for other work, and now you are interpreting the result, filling in gaps, and explaining what you meant. Add AI to that situation and you may get a polished answer without getting a usable handoff.
Before deciding which AI tool should take on a recurring job, I think there is a more useful place to start: agree on what finished means with the person who has to use the result. Their answer can reveal requirements that never made it into the original request.
Start with the person receiving the work
Consider a hypothetical distributor preparing orders for its warehouse. An operations manager wants an AI assistant to turn incoming customer requests into a handoff for the shipping team. The initial instruction sounds reasonable: “Summarize this order and get it ready for the warehouse.”
The assistant produces a clear paragraph with the customer name, requested products, and delivery date. But the warehouse supervisor needs item codes, quantities in the correct units, a confirmed shipping address, and a clear distinction between the customer's requested date and a date the business has actually promised. The summary leaves some of those details unclear.
The supervisor now has to stop and ask. If the owner is the only person who knows which details matter, the task has returned to the same place it was supposed to leave. Founder dependency can survive inside an otherwise efficient-looking handoff.
The missing agreement is specific: what information must arrive, in what form, before this person can take the next step? Describing the task from the recipient's side helps make that agreement visible.
A completion message is only part of the picture
AI developers deal with a related distinction. Anthropic's engineering guidance separates an agent's record of what it did from the outcome that actually exists when it finishes. It also describes evaluation tasks in terms of inputs and success criteria. This is vendor guidance for evaluating AI agents, rather than a study of ordinary business delegation. Anthropic, “Demystifying evals for AI agents.”
My practical interpretation is that you should define the result before relying on the completion message. In the distributor example, a readable summary is one result. A handoff the warehouse supervisor can accept under the agreed rules is a different, more useful result.
MITRE makes a related recommendation in its work on federal AI testing: evaluate systems in realistic mission environments against outcomes that matter to the agencies using them. That recommendation comes from a federal mission context. Applying the principle to an order handoff is a business adaptation, not a finding about distributors. MITRE, “The AI Mission Testing Gap.”
For a business owner, the useful question is straightforward: would this output let the next person do the intended work? A sample that looks convincing on its own may leave that question unanswered.
Write the assignment around an acceptable result
You can explore this without buying software or building an agent. Choose one recurring task and describe the agreement on a single page. For our hypothetical distributor, the assignment might look like this:
- Starting point: Prepare a warehouse handoff when an order coordinator supplies the customer request and the confirmed order record.
- Required information: Use the order record for item codes, quantities, units, and shipping address. Keep the requested delivery date separate from any confirmed commitment.
- Acceptable output: Produce one structured handoff that matches the confirmed record, identifies the source of the order details, and shows any unresolved questions in a clearly marked section.
- Allowed actions: Prepare the draft for review. Do not release the order, change quantities, substitute products, or promise a delivery date.
- Missing information: Flag a missing or conflicting detail and return the question to the order coordinator. Do not fill the gap with a plausible assumption.
- Receiving person: The warehouse supervisor accepts the complete handoff before the order moves into the shipping process.
These details are illustrative, and another distributor may need a different agreement. What matters is that the recipient can explain why an output is usable or why it needs to go back. “Looks fine” gives everyone less guidance than “the quantities match the order, the units are clear, and the address is confirmed.”
It is also useful to define a legitimate stopping point. An assistant that identifies an unresolved address has produced a question for the coordinator, not a warehouse-ready order. Keeping those two outcomes distinct prevents an unfinished task from looking complete simply because the system returned an answer.
Clear work comes before more autonomy
Once the assignment is clear, you can decide how much of it should be supported by AI. A draft prepared for a person to review may be enough for the first version. Some tasks may be better served by a standard form or a simpler workflow.
That approach is consistent with Anthropic's guidance to begin with the simplest adequate solution and add complexity when the work requires it. The advice comes from a vendor's engineering experience; it does not establish which tool is right for your business. Anthropic, “Building effective agents.”
A written assignment also needs to be reflected in the way a system is set up. If the brief says “prepare a draft,” the system's permissions should match that limit. The instructions, available information, tool access, and review process all need to support the same job. A clear brief gives the people building and testing that system a starting point; it does not guarantee that the system will perform reliably.
This fits the progression from Documented Systems to Team Execution in the Execution Gap Framework. The knowledge becomes more useful when the people involved agree on what it means in practice. AI readiness begins to take shape in those operating details, well before you give a system broader responsibility.
One useful conversation this week
Pick one recurring task you are considering for AI assistance. Write down its inputs, acceptable result, action limits, and receiving person, then put a sample output in front of that person. Ask them what they would still need before accepting it.
Use their answer to revise the assignment. You may discover a missing detail, a decision that belongs to someone else, or a step that should remain with a person. Each gives you a clearer job to delegate and a more concrete basis for deciding where AI belongs.
If this exercise exposes a wider pattern of work returning to you for interpretation, the Execution Gap Assessment offers a structured way to reflect on those operating dependencies.
For the task you have in mind, what would the next person need to see before they could confidently accept it?