How Can AI Help Business Coaches Prepare for Client Sessions?
Most business coaches prepare for a client session by looking backwards.
You review your notes from the last meeting. Check the actions you agreed. Perhaps look through a couple of emails or messages from the client. If you have time, you might look at their website, LinkedIn activity or something happening in their market.
Then the meeting starts and you ask some version of: "So, what's happened since we last spoke?"
There's nothing particularly wrong with that. But AI gives us the opportunity to change it.
Because preparing for a coaching session no longer needs to mean remembering what happened last time.
It can mean understanding what's happened since.
How are business coaches currently using AI to prepare for sessions?
The obvious starting point has been meeting notes. AI transcription tools can record a coaching session, summarise the conversation, identify actions and give you a useful record to return to before the next meeting.
Large language models such as ChatGPT and Claude can take that further. Give them your notes, information about the client and enough context and they can help identify themes, suggest questions or challenge your thinking before the next conversation.
Both can save considerable preparation time. But they're still largely looking backwards. They know what you told them. They know what happened in the previous conversation.
What they don't necessarily know is what has changed in the client's business during the three weeks since you had it.
And for a business coach, that's potentially the more interesting question.
What has changed since the last coaching session?
A business doesn't pause between coaching meetings. Competitors launch things. Customers change their behaviour. New technology appears. Performance moves. People leave. Regulation changes. New partnerships become possible. Market conditions shift.
Opportunities appear that neither you nor your client knew existed when you last spoke.
If you're coaching ten, twenty or thirty businesses, keeping a continuously updated mental picture of every one of them is practically impossible.
So we rely on the client to tell us what's happened. That creates an interesting limitation.
You can only discuss the things either you or the client already know are worth discussing.
AI can change that.
What if your preparation happened between the sessions?
Imagine opening a client briefing before your next coaching session and already being able to see:
how they're progressing against their goals;
which KPIs or milestones have moved;
what actions were agreed and what happened next;
significant changes in their market;
relevant competitor activity;
emerging risks;
new growth opportunities;
things that may deserve a strategic conversation.
Now the coach isn't spending the first part of the session reconstructing what's happened.
You can get into the conversation that matters. And importantly, AI hasn't replaced the coach.
It has prepared the pitch before the coach walks onto it.
That's a much more interesting use of AI in coaching than simply saving ten minutes writing up the meeting afterwards.
Can ChatGPT or Claude help you prepare for coaching sessions?
Absolutely.
I use general-purpose AI extensively, and I'd encourage coaches to do the same.
If you give ChatGPT or Claude good information about a client, their strategy, previous conversations and current challenges, they can be excellent thinking partners.
You can ask:
What am I missing?
What assumptions are we making?
What questions should I ask?
What might explain this performance?
What alternatives haven't we considered?
Where might I challenge the client?
That's valuable.
But there's an important limitation. You still have to know what to ask. AI is incredibly good at responding to questions.
The next opportunity is AI that helps identify which questions should be asked in the first place.
From reactive AI to proactive AI
Most of our relationship with AI is reactive. We prompt. It responds.
That's fine when we know what we need. It's less useful when the most valuable thing is something we don't yet know about. A coach can't ask:
"Should my client investigate the partnership opportunity I don't know exists?"
They can't prompt:
"Tell me about the competitor move I haven't noticed."
And they can't investigate a market change they aren't aware has happened.
This is where proactive AI becomes useful.
Instead of waiting for the coach to ask a question, AI can continuously monitor the things that matter to a client's business and surface changes that deserve attention.
That creates a different relationship with AI.
It stops being just a tool you use during preparation.
It starts preparing continuously.
Better preparation should create better questions
This doesn't mean arriving at a coaching session with all the answers. I'd argue the opposite.
The purpose of better information is to enable better questions. If you know a competitor has just entered a new market, the coaching question might be:
"Does this change anything for us?"
If performance against a goal has stalled:
"Is the goal wrong, the route wrong, or are we simply not executing?"
If AI has identified a potential opportunity:
"Is this actually relevant to where you're trying to take the business?"
Those are judgement questions.
AI can provide evidence and context, but the coach still brings experience, curiosity, challenge and an understanding of the human being sitting opposite them.
That's the bit I don't think we should be trying to automate.
How Syncity AI helps coaches prepare for client sessions
This is one of the reasons we built Syncity AI around Strategic Navigation rather than simply creating another AI assistant. Each client sets their destination: their goals, priorities, KPIs and strategic direction.
Between coaching sessions, Syncity's AI Scouts continuously monitor the business and the world around it for opportunities, risks and changes that could affect that journey. When something matters, Syncity investigates it and creates a briefing explaining what has changed, why it matters, the evidence behind it, what it could potentially be worth, possible actions and the risks involved.
So before the next session, the coach isn't starting with a blank page. They have a picture of the journey. What's moving. What's changed. What might deserve attention. And what might require a decision.
The coach still decides what conversation to have.
Syncity simply helps make sure it isn't a conversation you could have had three weeks ago.
AI shouldn't make coaching less human
There's an understandable concern that more AI in coaching means less human interaction.
It can. If we use AI to automate the relationship, replace conversations or manufacture generic advice at scale, we risk removing precisely the things that make good coaching valuable.
But that's a choice about how we use the technology. I think there's a much more valuable role for AI. Let the machines monitor more. Let them research more. Let them remember more. Let them analyse more.
Then let the coach spend more of the session doing the things humans are good at: listening, challenging, interpreting, understanding context and exercising judgement.
The goal isn't to automate the coaching session. It's to arrive better prepared for it.
Start preparing before the meeting is in the diary
For years, preparation has been something a coach does shortly before the session.
AI means it can become something that happens continuously in the background.
Your client sets the destination. The business continues moving. AI watches the journey.
And when you next sit down together, you have a much better idea of where the conversation might need to go.
That's how I think AI will make business coaches better. Not by coaching for them. By helping them see more before they coach.