AI at work used to be fairly simple. You opened a chatbot, typed a question, copied the answer, and got on with your day. That’s still how plenty of people use AI. But something else is starting to happen in offices.
AI is moving beyond individual questions and small tasks. Newer AI agents can be connected to the tools people already use at work, such as email, calendars, spreadsheets, customer databases, and project management software. Instead of waiting for someone to tell them what to do at every step, they can work through a task and decide what needs to happen next.
It may not sound like a huge change at first. But think about how much office work involves a series of small jobs rather than one big job. That’s where agents can make a real difference.
A lot of office work is made up of small, repetitive job
Think about a normal working day. You check your emails. You look for a document someone mentioned. You update a spreadsheet. You copy information from one system into another. You attend a meeting and then spend another 20 minutes writing down what was discussed. Someone asks for a report, so you pull information from three different places before you can even start writing it. None of these tasks is particularly difficult. They just take time.
This is one reason AI agents in the workplace are getting attention. An agent can potentially handle several of these smaller jobs together instead of simply helping with one of them.
For example, an agent could receive a customer request, find the customer’s information, check an internal system for the relevant details, and prepare a response. A person can then review it rather than doing all of the searching manually.
Microsoft’s 2026 Work Trend Index reported that the number of active agents in its Microsoft 365 ecosystem had grown 15 times year over year. It also found that many AI users felt the technology was giving them more time for higher-value work.
Email is an obvious place to start
Email probably isn’t the most exciting example of AI, but it’s a very practical one. Most people don’t spend their entire day writing emails. A lot of the time goes into sorting them, finding information to answer them, and remembering which messages still need a reply.
An agent can potentially help with that whole process. Say a customer emails asking about an order. Instead of simply generating a reply, an agent could look up the order, check its status, and gather the information needed for the response. It could then draft the email for an employee to check.
For routine messages, a company might eventually allow the agent to send the response itself. That doesn’t mean you should let an AI loose on your entire inbox and hope for the best. Sensitive conversations, complaints, and anything involving an important decision still need human judgement.
Meetings can create less work afterwards
The meeting itself is rarely the only problem. There’s usually a second job waiting once everyone leaves the call. Someone has to remember what was agreed, write down the action points, assign tasks, and send follow-ups. If the meeting involved several people and several projects, things can easily get missed.
AI can already transcribe and summarise meetings. Agents can take that a step further by doing something with the information.
Spreadsheets are another area that could change
There are plenty of jobs involving spreadsheets that don’t really need someone’s full attention. Cleaning up data. Combining information from different files. Updating recurring reports. Looking for figures that don’t match. Preparing a summary for a manager.
An agent can potentially take care of some of that routine work. Imagine that you receive the same sales data every Friday. Instead of spending an hour copying figures into a spreadsheet and preparing the usual summary, an agent could pull the information together, organise it, and flag anything that looks unusual. You would still want to look at the numbers. You’d just be looking at the numbers instead of spending your first hour preparing them.
That’s an important distinction. Good workplace automation shouldn’t simply move work around. It should remove some of the work that doesn’t really need a person doing it manually.
Research can happen in the background
Research is another task where agents can be useful because it rarely involves just one action. Suppose you’re preparing a report about a competitor. You need to find information, check different sources, compare what you’ve found, and organise it before you can start writing.
A standard AI tool can help with individual parts of that job. An agent can potentially work through several of them. It might search for information, realise that something is missing, look for another source, compare the results and then organise the useful material.
Microsoft’s research into workplace AI use found that a significant amount of AI-assisted work involves activities such as analysing information, solving problems, evaluating options and creating content. That doesn’t mean the AI should make the final decision. It means the person may not have to spend as much time doing the groundwork before making that decision.
