What is an AI employee?
An AI employee is a product category used for software that takes an assignment in natural language, uses available business context and tools, and returns work for a person or team. The term does not tell you which systems it can access, which actions it can take, or how reliably it completes a task. Those details depend on the product and your setup.
For a small business, the practical question is specific: can it turn the records you already keep into something useful, such as a morning brief, a customer follow-up list, or prepared invoice details? Evaluate that handoff before expanding its responsibilities.
AI employee, chatbot, or automation?
These categories overlap. A chatbot can have tools, and an automation can include AI. Compare the actual workflow rather than relying on the label.
| Approach | Typical starting point | What to check |
|---|---|---|
| Chat assistant | A question, conversation, or document to work on. | Whether it has the context and tools to finish work beyond the conversation. |
| Rule-based automation | A defined trigger and sequence of steps. | What happens when data is missing, a condition changes, or a step fails. |
| AI employee | An assignment, relevant context, and an expected result. | Supported actions, source quality, review controls, and evidence of completion. |
A fixed workflow can be a good choice for stable, predictable steps. An AI assignment can be useful when the work includes reading context, summarizing, or preparing a draft. Either approach needs clear boundaries and a way to notice failures.
Choose a first task you can inspect.
Start where the input is available, the result is easy to review, and a mistake can be caught before it affects a customer. Small service teams can consider three handoffs:
- A morning operations brief that groups open jobs, overdue items, and follow-ups. Check that each priority points to an actual source.
- A customer follow-up list that explains why a customer may need attention and prepares a next step. Check the context before contacting anyone.
- An invoice preparation review that organizes completed-job details and recorded charges. Keep accounting checks, posting, and delivery explicit.
A broad request such as “run my operations” is difficult to evaluate. “Review this week’s completed jobs and flag missing invoice details, without posting or sending anything” gives you a concrete result to check.
Run a pilot with a definition of done.
Before repeating a task, agree on what a successful result looks like. Use a representative sample from your own business, including an incomplete record and an exception.
- Name the sources. Choose the records the assignment can use. Decide which source wins if records disagree, and how unavailable information should be reported.
- Specify the output. Ask for a format your team can use: a brief with source links, a list with reasons, or draft details with missing fields flagged.
- Set action boundaries. Separate reading, drafting, changing a record, sending a message, and making a financial commitment. Confirm which steps are supported and which need approval.
- Compare against a human review. Check whether important items were missed, whether any facts were invented, and how much correction the result needed.
- Repeat before expanding. Review another run with different records. A good demonstration does not establish that a schedule will remain reliable or that every edge case is handled.
Track review time and correction effort alongside completion. A fast draft that needs extensive repair may be less useful than a narrower assignment your team can trust.
Questions to ask an AI employee provider.
What does “connected” actually mean?
Ask which account and environment are supported, which records can be read, and which actions can be written back. Access to files does not necessarily include email or calendar access. A sandbox demonstration does not establish production support.
What happens when information is missing?
Request an example with an unavailable source, an ambiguous status, or a missing amount. The result should make the gap visible and explain what needs review rather than quietly filling it in.
How do approvals and completion work?
Ask to see the difference between a draft, an approved item, and a completed external action. Check how the provider handles a failure or an uncertain response before retrying work that could create a duplicate.
What will the first workflow cost?
Ask for implementation scope, recurring charges, usage limits, support, and any third-party subscriptions. Compare the total cost of the agreed assignment, including your team’s setup and review time.
How is business data handled?
Read the provider’s privacy policy, check who can access your records, and understand how to disconnect services or request deletion. Confirm that the proposed permissions match the assignment.
Where Tobin fits.
Tobin is an AI employee for small business operations, built by Keystone Labs LLC. You assign work in plain English, and our team helps define the first assignment, available context, access, and review steps. Recurring work can use an agreed schedule.
Our public workflow examples are starting points for a conversation about your business. They are not a blanket promise of every integration or external action. We confirm implementation scope, support, and costs before setup begins.
Bring a task you would give an employee. We’ll work through what Tobin would need, what the result should look like, and where you remain in control.
Talk through your first assignment or explore the workflow examples.