How to delegate to an AI employee (without babysitting it)
Most founders who struggle with AI employees have a delegation problem, not an AI problem. They give a vague task, get a vague result, and conclude that AI isn't ready. The issue is the spec, not the employee.
Why delegation is harder than it looks
Bad delegation to a human employee looks like: "Can you take care of the marketing stuff this week?" The employee does something. It might not be what you wanted. You spend more time correcting than you would have spent doing it yourself. You conclude the employee is underperforming.
The same dynamic plays out with AI employees, faster and more visibly. The AI has no hesitation about starting, no social pressure to ask a clarifying question, and no background knowledge about what "the marketing stuff" means to you specifically. It will produce something confidently and completely, and it will be based entirely on the information you gave it.
The upside: delegation failure with an AI employee is cheap to fix. No feelings hurt, no relationship to repair. You update the task description and try again.
The three elements of a good task spec
A task description that produces useful output from an AI employee has three things:
- Output format. What does done look like? "A 700-word blog post," "a spreadsheet with 50 rows," "five subject line options," "a Markdown doc with three sections." Be concrete. If the output format is ambiguous, the output will be.
- Success criteria. How will you know it's good? "The post should target the keyword 'AI employees' and include a CTA at the end." "The subject lines should be under 50 characters and not use the word 'free.'" "The competitor list should include pricing and whether they have an API." Criteria that are testable are better than criteria that are subjective.
- Constraints. What should the AI employee not do? "Don't include pricing — it's changing next month." "Don't reference competitors by name." "Use a direct tone, not corporate." Constraints narrow the solution space and prevent the most common wrong turns.
You do not need a multi-page brief. Two to four sentences covering these three elements is usually enough for a well-scoped task.
The approval gate: why it matters
In Summon, AI employees complete tasks but can't publish, send, or deploy anything without a human approval. This is not a safety workaround — it is the correct architecture for any delegation relationship.
Good managers do not review every email a direct report sends. But they do review work before it goes to a customer, a board, or a public channel. The approval gate is where you catch the 20% of outputs that need correction before they cause a problem.
The gate also creates a feedback loop. When you reject an output and explain why, that context improves future tasks. Over time, the task spec you write for a repeat task gets shorter because the AI employee has a history of what good looks like for your company.
The review cadence: batching your attention
The instinct is to check constantly — to treat AI employees like a live chat window that needs monitoring. This is the fastest path to exhaustion and the least efficient use of your time.
Better: set a daily review window. Fifteen to thirty minutes each morning to look at what came in overnight, approve what's ready, reject what isn't, and add new tasks to the board. That's it.
The AI employee works while you sleep, while you're on calls, while you're building. You check the board like a manager checks their inbox — with purpose and a fixed time, not with constant attention.
For most founders with three to five active departments, one daily review window is sufficient. For founders with more active tasks, a morning and an evening check covers everything.
What good delegation produces over time
After 30 days of consistent delegation with good task specs, you will notice two things. First, your task descriptions get shorter because you know what to include. Second, the AI employee's outputs get more useful because they're building on a history of work that matches your standards.
The relationship compounds. A founder who delegates well to AI employees at month one gets leverage. A founder who delegates well at month six gets leverage plus accumulated context — a department that knows how their company communicates, what their product does, and what good looks like for their audience.
That is not babysitting. That is management.
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Start for free →Frequently asked questions
What happens if the AI employee produces something wrong?
The approval gate exists precisely for this. Nothing an AI employee produces goes live without a human review. If the output is wrong, you reject it and add context to the task description. The AI employee tries again with better information. The cost of a bad output is a few minutes of feedback, not a performance review or an awkward conversation.
How specific does a task description need to be?
More specific than you think at first, less specific than you fear. The key elements are: what the output looks like (a doc, a list, a draft), what success means (ranked keywords, 800 words, 5 subject lines), and any constraints (don't mention X, use our brand voice, reference Y competitor). You don't need to write a brief — a few clear sentences usually suffice.
How long does reviewing an AI employee's work actually take?
Most task reviews take 5–15 minutes. Reading a blog draft and marking edits: 10 minutes. Checking a competitor research list for accuracy: 5 minutes. Reviewing a financial model update: 15 minutes. The review cadence for an active AI employee is roughly 30–60 minutes per week total.