How People Actually Use AI at Work

AI has become part of everyday work.
People use it to write emails, summarise documents, generate ideas, analyse information, write code, and get through tasks that would otherwise take longer.But there’s a difference between using AI occasionally and making it part of how work actually gets done.

Most people don’t sit down in the morning and hand their entire workday over to AI. They bring it into specific moments when they need help.
And those moments tell us something important about where AI at work is today.

AI often starts with a blank page

One of the most common ways people use AI is to get started. You have an email to write but don’t know how to phrase it. You have an idea but need help organising your thoughts. You have a document to prepare and want a first draft.

AI makes that starting point easier. Instead of staring at a blank page, you can describe what you need and get something to work with.
The same applies to brainstorming, outlining, rewriting, and summarising. AI reduces the effort involved in getting from an idea to a first version.
But getting started is only one part of the work.

People use AI to make sense of information

Work involves a lot of information. Emails, meeting notes, documents, conversations, reports, and messages all compete for attention. AI can help make that information easier to process.
You can ask it to summarise a long conversation, extract key points from a document, compare information, or turn meeting notes into a list of action items. This is useful because the challenge isn’t always finding information. Sometimes it’s understanding what matters and deciding what to do with it.

AI helps with individual tasks, but work doesn’t happen in isolation

Consider a simple example. You receive an email from a client asking to schedule a meeting. You might use AI to draft a reply. You could ask it to suggest a few possible meeting times. You might even use it to prepare a follow-up message. But there’s still more to do.

You need to check your calendar, update the relevant contact information, send the email, and make sure the next steps are recorded. The AI may have helped with individual parts of the process, but the work itself still moves across different applications. And that means you remain responsible for connecting everything.

AI can help with the task without necessarily handling the workflow.

The context problem

One reason this happens is that work information is spread across different tools. Your email contains conversations. Your calendar contains meetings. Your contacts contain details about the people you work with. Your documents contain important information about projects and decisions. When you use an AI tool separately from these applications, it may only know what you’ve shared with it.

You end up copying information, explaining situations repeatedly, and providing context that already exists somewhere else. This creates a strange situation: AI can be very capable, but it doesn’t always have enough information to be useful in the way you need. The issue isn’t always what AI can do. It’s what AI knows when it’s asked to do it.

From helping with tasks to carrying work forward

We think the next step for AI at work goes beyond generating content or answering questions. It’s about giving AI the context and capabilities to help carry work forward. Imagine an environment where your AI can understand the relevant information across your email, calendar, contacts, and other work applications. Instead of asking you to explain everything again, it can work with context that already exists. And instead of stopping at a recommendation, it can help take the next action, within the permissions you’ve given it.

This is the direction we’re exploring with Workpeg. Workpeg is a cloud OS for work where applications, known as Pegs, operate within the same environment. Fabric provides shared memory and context across those Pegs, while AI Core powers the intelligence that works across the system. The idea is to move beyond AI that simply helps with isolated tasks and towards AI that can participate in how work actually happens.

The next question isn’t just what AI can do

It’s how well AI understands the work it’s being asked to do. Writing a good email is useful. Knowing who it’s for, understanding the conversation behind it, and being able to carry out the next step makes that assistance more meaningful. Summarising a meeting is useful. Knowing which project it relates to and what needs to happen afterwards makes it more valuable.

AI has already changed how people approach individual tasks. The bigger opportunity is changing how those tasks connect. Because getting help with one task is useful. Having your work move forward is even better.

That’s the kind of experience we’re working towards at Workpeg.
The Workpeg Team