AI Capabilities: What Assistants Can and Cannot Do at Work

AI capabilities include drafting text, organizing supplied information, comparing alternatives, and helping carry out tasks through available tools. What an assistant can reliably do depends on its model, the information it receives, its connections, and the checks around its work. A convincing answer alone does not show that a task was completed correctly.

Tools like fenbs keep project instructions, decisions, and progress outside one conversation, so connected assistants can start with the same work record. That improves the starting information; people still verify results and approve decisions. The useful question is how much of a particular task you can delegate under those conditions.

Which parts of everyday work can an assistant help with?

Start with work that has a clear input and a result you can inspect. An assistant might turn your meeting notes into a draft agenda, compare two supplied proposals, or suggest questions for an incomplete project request. You know the source and can judge whether the transformation preserves its meaning.

These are starting points for a trial, rather than promises about every AI app. Try one familiar example before depending on the result. For a broader catalog of use cases, see AI applications for work; this guide focuses on the boundaries around those capabilities.

Why does tool access change what the assistant can do?

Writing a suggested customer reply and sending that reply are different operations. Sending requires a suitable connection, permission to use it, and a successful response from the outside service. Ask whether the assistant has actually used a tool or has only described an action you could take.

Anthropic’s tool-use documentation↗ (opens in a new tab) explains how Claude requests tool operations and receives their results. This helps explain why model ability and connected access are separate: an assistant can understand a task without having a route to execute it.

Before allowing a consequential action, specify its scope. “Draft the response for review” gives a clear stopping point; “handle this customer” leaves too much unspecified. After an allowed action, inspect the service record rather than accepting a conversational statement that everything worked.

Why can it miss information already mentioned?

An assistant works from the information available for its current response. A context window is the material a model can consider at once, rather like a temporary work surface. It is different from training knowledge and from information an app stores for later.

Anthropic’s context-window guide↗ (opens in a new tab) makes that distinction and explains that conversation content consumes the available space. Avoid assuming a detail remains available because it appeared somewhere in an earlier conversation. Supply a short current account of the facts that affect the next task.

Say a designer has changed the project audience from homeowners to contractors. Save the approved change and its reason, then ask the next assistant to state the audience before drafting. The AI brain guide explains the outside record, while how fenbs works shows how rules and Where we left off provide that starting point.

How can a plausible answer still be wrong?

Fluent wording can hide a missing source, a mistaken assumption, or an invented detail. NIST’s Generative AI Profile↗ (opens in a new tab) identifies confidently presented false content as a risk. Treat confidence in the writing as separate from evidence for the answer.

For a proposal comparison, trace each claimed delivery date to the supplied proposal. For a calculation, check the input values and arithmetic in a suitable calculator or spreadsheet. For a statement about policy, find the controlling policy rather than asking the same assistant whether it feels certain.

• Request a source location for each material factual claim.
• Keep missing information visibly marked as unknown.
• Distinguish an observation in the source from an interpretation.
• Have a qualified person review work involving legal, medical, financial, or other professional judgment.

How should you test a capability before relying on it?

Define success before the trial. For a support-summary task, success might mean preserving the reported problem, listing attempted fixes, and adding no new customer facts. Gather a few examples you understand, including an incomplete one and one where two sources conflict.

1. Give the assistant the same approved instructions for each example.
2. Compare each result with an answer a responsible person has checked.
3. Record omissions, unsupported additions, and the effort needed to correct them.
4. Repeat after changing the instructions, then decide which cases still need close review.

A good result on a clean example says little about an unfamiliar exception. Test the work you actually expect, and narrow the assignment when review becomes harder than doing it yourself. Keep the observed limits beside the instructions so a different teammate does not repeat the same assumption.

What authority should remain with a person?

Separate making an option visible from choosing it. An assistant can outline tradeoffs and record a person’s choice; the accountable person decides whether the evidence supports that choice. A tool connection does not make every available action appropriate for every request.

For example, a purchasing assistant may identify an overdue order and draft a vendor question. Changing the delivery commitment affects other people and needs the authority your process requires. Write that boundary as a concrete rule, with the exception path and the person to ask.

fenbs keeps Decisions and rules and records changes in History, but it does not certify that an assistant’s answer is true. For the practical delegation routine, see how to use AI at work, and treat the phrase AI employees carefully when assigning responsibility.

How to do this with fenbs

1. Create a free account, then add one project whose inputs and results you can check.
2. Add a task for the trial and write its purpose, permitted actions, expected result, and review method in its plan.
3. Record the boundaries a person approved in Decisions and rules. Keep unanswered choices open for a person.
4. Connect a supported assistant, ask it to read the project context, and compare its proposed next action with those boundaries.
5. Save the trial result, remaining checks, and next step in task progress and Where we left off. Turn an observed failure into a lesson for similar work.

fenbs is free for one person, no credit card. It is useful when instructions and progress must survive another session; it is not the right fit if all you need is a single answer with no ongoing work record. Explore the AI at work guides when choosing the next small task to test.