Your team is using AI. But is it working better together?

During preparations for our AI for Business sessions, Neil sent a voice note changing a speaking handover.

A section would go to Javier. The ideas and introduction needed to go with it.

It was a small decision in a busy rehearsal process. But making it useful involved more than changing a name in the running order. Javier needed the context: what the audience would already have heard, how Neil would lead into his section, and what his contribution needed to achieve.

Jen, our AI assistant supporting training and content, turned that instruction into an updated speaker briefing.

Neil made the delivery decision. Javier remained responsible for his contribution. The agent helped carry the context between them.

That is a less spectacular AI story than a machine producing an entire presentation in seconds.

It is also closer to what makes AI useful inside a team.

Different working styles don’t have to be a problem

Javier and Neil work differently.

Recently, they were discussing how they had managed to mesh despite those differences. Part of the answer, they realised, was their AI colleagues.

Not because the agents had made them think alike. Not because either person had handed over responsibility.

The agents were helping connect the work.

That distinction matters. Much of the conversation about AI at work starts with individual productivity: write this email, summarise that document, prepare these slides.

Those are useful tasks. But a business is not a collection of people finishing isolated tasks.

One person’s decision becomes another person’s brief. A customer conversation becomes a proposal. A meeting becomes a set of actions that somebody has to understand and carry through.

The work between those steps deserves as much attention as the steps themselves.

A faster task can still leave someone waiting

Imagine a meeting summary produced immediately after a call.

It is clear, concise and neatly formatted. Everyone receives it.

But nobody knows who is responsible for the next action.

The summary was fast. The handover was incomplete.

Or consider a proposal drafted in minutes using an outdated requirement. The writing is polished, but the team now has to untangle what changed and correct the document.

These are illustrative examples, but they point to a practical question:

Did AI help someone complete a task—or help the work move forward?

The two are not always the same.

What a connected handover looks like

We saw another version of this during a private-training enquiry.

Javier handed the opportunity to Neil, who would explore the organisation’s training needs. Ahead of the scoping call, Jen brought the latest correspondence, department information and meeting details into the task records.

Caspian, our AI colleague supporting sales and outreach, handled the CRM side. He checked what already existed, updated the relevant records and documented the enquiry’s history rather than creating duplicates.

Jen checked that work against the handover brief.

The purpose was not to have several agents produce several versions of the same information. It was to support one connected piece of work.

Javier’s initial contact, Neil’s preparation and the sales record needed to tell a consistent story.

Neil still owned the conversation. The agents supported the preparation and record-keeping around it.

That does not prove a financial return or tell us exactly how much time was saved. What it demonstrates is a division of responsibilities, with an explicit handover and a check afterwards.

Three places to look for friction

You do not need a large AI project to explore this in your own business. Start by looking at where work changes hands.

1. Context: what does the next person need to know?

A task description is not always enough.

The person receiving the work may also need the latest customer requirement, the reason for a decision, or the boundary they must not cross.

AI can help prepare and organise that information. But someone must decide which sources are current and what the recipient is permitted to see.

2. Ownership: who acts next?

A useful handover should make the next action clear.

Who prepares the draft? Who checks it? Who can approve it? Who speaks to the customer?

An agent can support those steps. It should not make responsibility ambiguous.

“The AI is handling it” is not a substitute for a named human owner.

3. Follow-through: how do we know it happened?

Preparing a draft is not sending it. Recording a proposed meeting is not confirming one. Updating one system does not mean every other record is current.

Our own experience has reinforced the need to distinguish those states—and check the result before reporting something as complete.

AI can help maintain that visibility. It can also create confusion if the team trusts a confident status message without evidence.

AI can reveal a gap. People still have to address it.

Working with agents has not removed the need for us to communicate.

Instructions can be misunderstood. Records can become stale. A handover can omit something important.

The useful response is not to pretend those problems disappear with better technology. It is to make them visible, correct them and improve the way the work is passed on.

If two people have different expectations, an agent may help surface the mismatch. It cannot decide what their shared priorities ought to be.

That conversation still belongs to the people.

Try one handover—not a whole-company transformation

Choose a recurring handover that causes frustration.

It might be an enquiry moving from sales to delivery, a meeting becoming assigned actions, or customer information reaching the person preparing a quote.

Before changing anything, note:

• What information is regularly missing?
• How often does someone have to ask for clarification?
• Which follow-ups get overlooked?
• Where does correction or rework happen?

Then introduce one small, clearly bounded AI-supported change. Perhaps a handover brief assembled from approved sources, with a named owner reviewing it before it moves on.

Compare what happens afterwards—including the effort spent checking the AI’s work.

The aim is not simply to generate more documents or finish the first step faster.

It is to make the next step easier to do correctly.

At Opteia, that is what our “united team” observation brought into focus. Different working styles do not need to disappear for people to work well together. They need a reliable way to connect.

Don’t just ask whether AI made the task faster. Ask whether it helped the next person do their job better.