Sarah had been coaching for eleven years. She was good at it — genuinely good — with a loyal client base, strong referrals, and the kind of intuition that takes a decade to build. But somewhere in her third year of running a full practice, a quiet frustration had settled in that she’d never quite named out loud.
Between sessions, she was spending enormous amounts of time on the work around coaching rather than the work of coaching. Reviewing notes. Drafting session summaries. Preparing follow-up frameworks. Trying to hold the threads of eight or ten clients’ situations in her head simultaneously so she could show up to each session as if it were the only one. It wasn’t unsustainable — she was managing — but it was heavier than it needed to be, and she knew it.
When a colleague first mentioned using AI tools to support session preparation and follow-up, Sarah’s honest reaction was scepticism. She’d worked hard to develop her instincts, and something about the idea of AI involvement in her coaching felt like it might dilute what made her good. That concern is worth taking seriously, because it’s one a lot of coaches share. And it’s also exactly what didn’t happen.
What Sarah discovered, over about four months of careful experimentation, is the subject of this article — not because her story is unique, but because it illustrates something important about how AI assistance can strengthen the quality of coaching work when it’s introduced thoughtfully.
The Problem She Was Actually Trying to Solve
Before Sarah tried a single tool, she did something that turned out to be the most important step in the whole process: she got specific about what was costing her the most time and energy, and what she actually wanted back.
It wasn’t the coaching itself. The sessions were the part she loved, and she wasn’t looking to change what happened in the room. What she wanted was to spend less mental energy on session preparation, feel more organised across multiple clients, and produce follow-up materials that were genuinely useful to clients rather than brief notes she’d thrown together between back-to-back calls.
This clarity mattered enormously. It meant she wasn’t asking AI to replace her judgment or generate coaching content — she was asking it to handle the structural and administrative work that surrounded her actual coaching, so she could bring more of herself to the parts that required a human.
That distinction — between what AI supports and what the coach provides — is the thing that most determines whether AI assistance helps or hinders a coaching practice. Sarah got this right by accident, essentially, because she started from a clear problem rather than from curiosity about the technology. It’s a lesson worth holding onto.
What She Actually Did, Step by Step
Sarah started with session preparation. Before each client meeting, she’d pull together her notes from previous sessions, the goals they’d established together, and any reflections the client had shared between sessions. She began using an AI tool to help her identify patterns across these notes — recurring themes, language the client used consistently, areas where progress had stalled and areas where momentum was building.
She was careful — always careful — to anonymise client information before it went anywhere near a digital tool. Names came out. Identifying details came out. What remained was the substance of the work: the patterns, the language, the emotional texture of where a client was in their journey. The AI helped her see things in that material she might have seen anyway, but faster and more systematically than she could manage across ten clients simultaneously.
What she found, almost immediately, was that she was walking into sessions having already done a quality of preparation that previously would have taken twice as long. She was noticing patterns she might have caught eventually but could now name in the first ten minutes. She was asking sharper questions earlier, because she’d already done the pattern-recognition work before the conversation began.
The second area she experimented with was post-session follow-up. After each session, she’d spend a few minutes recording her own voice notes while the conversation was still fresh — what had surfaced, what felt significant, what she wanted to track going forward. She used AI to help structure these notes into a consistent format, identify key themes worth flagging, and draft the outline of a follow-up summary she could then review, edit in her own voice, and send to the client.
This part took some iteration. The first few attempts produced summaries that were accurate but flat — they captured what was said without the texture of what it meant. Sarah learned to give the tool more context in her voice notes, to flag the moments that felt pivotal, and to treat the AI output as a strong first draft she’d always personalise rather than a finished product she’d send as-is. Once she’d developed that habit, the follow-up summaries became one of the most valued parts of her client experience.
What Changed — and What Didn’t
Four months in, Sarah noticed several things that had shifted in her practice, and one important thing that hadn’t.
Her preparation time dropped significantly without any reduction in preparation quality — in fact, the opposite. She was showing up to sessions better prepared than she’d managed in years, because the pattern-recognition work was being done more systematically. Clients began commenting on it, though they didn’t know what had changed — they just noticed that she seemed to hold their whole story with particular clarity.
Her post-session admin — the part that had most consistently eaten into her evenings — became faster and less draining. The combination of quick voice notes and AI-assisted structuring meant she was spending less time staring at a blank document trying to remember exactly what had been said, and more time applying her own judgment to what it meant and how to communicate it.
She also found, unexpectedly, that the process of preparing AI-assisted session briefs made her a more reflective practitioner. The act of reviewing patterns across multiple sessions, and thinking about how to articulate what she was seeing, brought a quality of meta-awareness to her work that she hadn’t previously built in as deliberately.
What didn’t change was the coaching itself. The sessions were still entirely hers — her intuition, her questions, her presence, her ability to read what a client needed and respond in the moment. AI touched none of that. It worked at the edges of her practice, not at the centre, and that was exactly as it should be.
What This Means for Your Practice
Sarah’s experience isn’t a template — your practice is different from hers, your clients have different needs, and the specific tools and workflows that work for you will look different from what worked for her. But the underlying principles she stumbled onto are worth carrying into your own experimentation.
Start from a real problem, not from the technology. The question isn’t “how can I use AI in my coaching?” — it’s “what’s costing me time or energy that AI might help with?” That starting point keeps you in control of how AI enters your practice rather than letting the technology drive the process.
Keep the human work human. The parts of coaching that require your presence, your judgment, your relationship with the client — those aren’t candidates for AI involvement. The parts that are structural, administrative, or pattern-based are exactly where AI assistance tends to add real value without any cost to the quality of the coaching relationship.
Expect an adjustment period. Sarah’s early attempts weren’t as useful as her later ones, because she was learning how to work with the tools effectively. Build in time to iterate, and don’t judge the approach on the first few experiments.
Your Action Plan
- Map where your time actually goes across a typical coaching week. Before you try any tools, spend one week keeping a rough log of how you’re spending your time — session prep, note-taking, follow-up, admin, client communication. This map will tell you exactly where AI assistance is most likely to make a real difference in your practice.
- Identify one specific task to experiment with first. Based on your time map, pick a single area — session preparation, post-session notes, follow-up summaries, resource curation — and focus your first AI experiments there. Trying to change multiple things simultaneously makes it hard to know what’s working and why.
- Develop a consistent anonymisation habit before you start. Decide how you’ll handle client confidentiality before you involve any AI tools in your workflow. A simple habit — removing names and identifying details before any information is processed by an external tool — protects your clients and gives you confidence in your process.
- Start with your own voice notes as the raw material. Rather than trying to type comprehensive notes during or after sessions, experiment with brief spoken reflections captured immediately after — what felt significant, what patterns you noticed, what you want to track. This raw material, properly anonymised, is often more useful input for AI-assisted structuring than written notes.
- Treat AI output as a first draft, always. Whatever an AI tool produces from your session material, plan to review it, edit it, and infuse it with your own voice and judgment before it goes anywhere near a client. The tool provides structure; you provide meaning. Keep that division clear and your output will stay authentically yours.
- Track what changes in your practice over a defined period. Give yourself a genuine experimental window — eight to twelve weeks — and keep simple notes on what’s different. Time spent on admin, quality of session preparation, client feedback, your own energy levels. Reflection on what’s actually changing keeps you learning rather than just doing.
- Share what you discover with peers who are navigating the same questions. Your experiments, even the ones that don’t work perfectly, are useful information for other coaches in your community. The collective learning that happens when coaches share their honest AI experiences — what helped, what didn’t, what surprised them — accelerates everyone’s progress and builds the kind of practical wisdom that published guides rarely capture.
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