An AI assistant is software that uses generative AI, usually a large language model, to help a person with everyday work: drafting and editing text, summarizing documents and meetings, answering questions, searching for information and helping with analysis or code. Some are standalone apps; many are built into tools people already use, such as email, office suites, collaboration platforms and business applications. The person stays in charge, asking for help and deciding what to use.
At a glance
- AI assistants help a person with tasks on request; the user reviews and decides what to keep.
- They come as standalone apps or as features inside productivity suites and business software.
- Business versions often draw on company data the user can access, such as email, files and calendars.
- Output can be wrong (AI hallucination), so review remains necessary.
- Value depends on adoption and preparation, especially permissions and data handling.
What problem it solves
Knowledge workers spend much of their day on tasks that are necessary but repetitive: writing first drafts, summarizing long threads, preparing meeting notes, finding information spread across systems and reformatting content. AI assistants take on the first pass of that work, so people can spend more time on judgment and less on assembly.
For an organization, an approved assistant also gives staff a sanctioned alternative to unmanaged public tools, reducing shadow AI and keeping data under business terms. The catch is that benefits are uneven: some roles gain a lot, some little, and gains depend on training and habits as much as on the software.
How it works
Model. The assistant sends the user’s request to a large language model (LLM), hosted by the assistant’s vendor or a model provider it uses.
Context. Business assistants add relevant context to the request: the document open on screen, the email thread, search results from company files, calendar entries or CRM records. They typically retrieve only what the signed-in user has permission to see. Connections to other systems may use built-in connectors or standards such as the Model Context Protocol (MCP).
Response. The model generates an answer, draft or summary, often with links to the sources used. The user edits, accepts or discards it.
Administration. Business plans usually include admin controls for who gets access, which data sources are connected, retention of prompts and responses, and audit logs, though features vary by product and plan.
Well-known examples include assistants built into major productivity suites; Microsoft, for example, brands its assistants as Copilot. Our Microsoft 365 overview covers licensing and deployment in that ecosystem.
When it matters for buyers
- When planning a rollout. Review file sharing and permissions first; an assistant can surface overshared content quickly.
- When choosing between suites and standalone tools. Assistants built into your existing suite use its data and permissions; standalone tools may be stronger at some tasks but need separate integration and review.
- When budgeting. Per-user add-ons add up; pilot with a group, measure use and expand where it pays off.
- When handling sensitive or regulated data. Confirm data handling terms, data location and retention before connecting those sources.
- When setting policy. Tell staff what the assistant may be used for, what data may go in and that output must be checked.
Questions to ask vendors
- Which data sources can the assistant access, and does it strictly follow existing user permissions?
- Is our data, including prompts and outputs, used to train models, and where is it processed and stored?
- What admin controls, usage reports and audit logs are included in the plan we’d buy?
- Which languages and applications are supported?
- How is it priced, and are there usage limits or extra charges for heavier features?
- What accuracy safeguards are built in, such as source citations?
- Can we start with a subset of users and adjust seat counts later?
How it differs from agent assist
Agent assist is a contact center feature that supports human agents during live customer interactions, for example by suggesting answers, surfacing knowledge articles and drafting call notes. It is a specialized kind of AI assistant for one role. A general AI assistant serves any knowledge worker across everyday tasks. Both differ from agentic AI, which is designed to complete multi-step tasks with less human direction, and from a chatbot, which typically talks with customers rather than helping employees.