Contributors: Chief Data & AI Officer, a FTSE 100 Retailer · Nirali Patel, Ex-CDO, Liberty Global · Victoria Gamerman, Founder, RWD Insights · Morgan Templar, CEO, First CDO Partners · Ines Ashton, Director of Data, Compleat Food Group · Bethan Blakely, Senior Research & Insight Consultant · Harleen Thethy, VP of Data, Barclays
We’re seeing more and more content published about how Chief Data, AI, and Analytics Officers are driving the use of AI in their organisations. Brilliant centre of excellence case studies are hitting the market, and I only expect that to increase.
Yet what I’ve found missing is the content on how these leaders are using it themselves.
You may see dribs and drabs on LinkedIn, but nothing comprehensive. So, I sat down with a brilliant group of senior executives and asked them exactly this: how are you actually using AI to make your work, and your life, better? Beyond just rewriting emails.
I’ve chosen to keep the examples anonymous, as some of them are a little sensitive in nature. But my thanks to the contributors for being so candid with their thoughts.
Here’s what they shared.
Know Thyself: AI as a Mirror
Perhaps the most unexpected theme to emerge from the conversation was how many of these leaders are turning AI inward, using it not to produce a document or analyse a dataset, but to understand themselves better.
One executive described prompting her enterprise AI tool, which has access to her emails, Teams calls, calendar, and meeting transcripts, to act as a self-reflection coach. She asked it to be honest, but kind. What came back stopped her in her tracks.
“I could not believe what it came out with, it had me sussed. It was so insightful and so useful.”
The feedback was specific and uncomfortable in the best way: she dives into her team too quickly, which can erode psychological safety, and she expects 10 out of 10 performance, when she needs to learn to accept 8 sometimes. These were observations drawn from her own behaviour, in her own organisation, over months of real interactions. This would have taken so many resources and time to achieve without AI, and she told me she’s already seen the benefits of this knowledge in her own development.
Another executive took a different angle. Having left an organisation after many years of service, she was navigating the particular vulnerability of career transition. She shared the link to a digital farewell board, where colleagues had written tributes, with an AI and asked it a single question: what are my superpowers?
“I’ve been trying to build myself up during this transition — and it pulled out things I hadn’t even thought to articulate.”
Here’s the prompt she used: Act like a trusted coach who understands my work, communication style and recent priorities. Based on my recent emails, Teams messages and interactions, give me 5 honest reflections that could help me improve as a line manager. Be specific and constructive. Include: what I seem to be doing well, any patterns or blind spots I may not have noticed one practical action I could take for each reflection.
A third leader used the same principle differently again. She built a structured exercise with an AI coach, working question by question through twenty-plus years of her career. Cataloguing not just her successes, but her failures, and crucially, what she learned from each. The result, built over six weeks in short ten-minute sessions, is a rich case log she now draws on before every interview.
“When you look at it, you go — oh my god. I have done so much. I’ve learned so much and I’m still learning.”
This has allowed her to walk into interviews with such confidence, and great examples from her 20+ year career that are hyper relevant to the job description and hiring organisation.
The lesson here for me is that it isn’t about which tool you use. It’s that AI, when given the right context and the right prompt, can offer a quality of honest, personalised reflection that’s genuinely hard to come by in a professional environment. People won’t always tell you that you jump into conversations too quickly, but AI will.
The Hard Conversations You’re Not Having
Running close behind self-reflection was another pattern, using AI to prepare for conversations that are difficult, politically charged, or emotionally loaded.
One CDO had been fighting for two years over a classic and maddening boundary dispute, who owns AI: the data function or the CTO’s office? This is something I know many within our data community are facing. The frustration had become personal. She described feeling upset and tearful, knowing that showing it would undermine her.
She turned to AI not for the answer, but for the structure to have the conversation without the emotion getting in the way.
“It just reminds you to take the emotion out of it and be a bit straight back.”
She walked into the meeting with her CTO armed with a clear articulation of where AI ownership sits traditionally, what her remit was, and precisely why the framing she kept hearing, “AI is just an output of technology” was incorrect. She didn’t lose her temper. She didn’t have to.
She explicitly asked the AI to be sensitive to how she was feeling. That nuance and framing matter. As one of the other contributors put it, adding that layer of kindness is important, comparing it to visiting a Doctor’s Office, they’re not going to hand you your results and say, “it’s awful.” There’s humanness to it. You can prompt for that.
Career Planning at Speed and Depth
Senior executives are rarely given the time or the safe space to think seriously about their own careers. They’re too busy enabling everyone else’s. This is something I see time and time again.
I think AI is quietly changing that. It’s giving leaders a private, non-judgmental thinking partner who will engage seriously with whatever they bring.
One contributor, currently between roles, described asking an AI to map out what it would take to get a role at Google, not because she particularly wanted to go there, but because the exercise gave her a clear-eyed view of her own gaps.
“It helped me feel a bit more purposeful, rather than having things happen to me.”
The AI returned a breakdown of relevant roles, interview processes, and a 1-, 3-, and 5-year pathway. The act of seeing the gap in black and white was clarifying in a way that years of good intentions hadn’t been.
I related to this so deeply, AI seems to be able to expose gaps almost neutrally, without judgment.
One of the exercises I do with Leaders who I work with on their external brand involves running a live AI search during our consulting calls. I find showing them in real time what an external person would find if they Googled or LLM searched them can be eye-opening.
When the results are in black and white from an LLM, it tends to resonate, even when they won’t hear it from me. I can say as many times as I like to the people who haven’t updated their LinkedIn in five years that it will be harming their chances of being discoverable for the things they want to promote (whether that be a new job, clients, etc.), but when an AI shows them what a recruiter or client actually finds, often that’s when it clicks.
Writing: Finally, No More Blank Page
For leaders who don’t love writing AI has become something genuinely liberating.
One dyslexic contributor described her use of Copilot for performance development reviews. She inputs her ratings and bullet points for each person. The AI transforms them into coherent, grammatically correct paragraphs. The content is entirely hers. The expression is better than she feels it would have been otherwise. Although she notes that some may not like the fact that she uses Copilot to help with the task.
“You should thank me that I use Copilot for it — my brain would give you verbal diarrhoea otherwise.”
Another leader uses AI to prepare for panel appearances and interviews by feeding it her previous conference notes and talking points, then asking how it would answer new questions based on what she’s already said. It surfaces material she’d forgotten about and gives her a starting point to refine, rather than building from scratch each time.
One contributor adds she does something similar with her PowerPoint presentations, supplying something she’s already built. Not to change the content, but to make it visually compelling enough that the board would actually engage with it.
“If it looks good, they get it – People consume information with their eyes.”
One CEO who’d been training an AI on her writing voice for three years shared a hard-won lesson: when you train a model deeply on your own patterns, it eventually starts echoing your weaknesses back at you. She now deliberately uses different AI tools as a counterbalance to get the jolt of a perspective that hasn’t been shaped entirely by her own.
AI as an Accessibility Tool
In my opinion, this section deserves its own heading, because it was one of the most powerful things that came up.
One executive manages periods of significant brain fog as part of a health condition. She records her meetings, feeds the transcripts into her AI tool daily, and uses it to generate her to-do list and track her commitments. Without this, she described hanging up from a call with no memory that it had taken place.
“I hung up, and now I don’t even remember that we had a conversation, just that it’s on my calendar.”
She doesn’t disclose this widely. The AI makes the accommodation invisible. She stays on top of everything. Nobody needs to know.
To me, this is not a productivity hack. It’s AI enabling full participation in professional life for someone who would otherwise be disadvantaged. It deserves to be taken seriously as a use case in its own right, and it raises the question of how many people in our organisations are doing something similar, quietly, without any organisational support.
I think we should highlight and celebrate this use of technology much more.
Building Systems, Not Just Prompts
The most technically ambitious use cases came from leaders who had moved beyond one-off queries into building actual systems.
One executive, transitioning out of a global corporate role, described the shock of going from having a large team structure to just herself and a laptop.
“I had a chief of staff, an EA, direct reports with teams under them — and now it was just me and my computer.”
Her response was to build an AI chief of staff in Claude that was connected to her work email and calendar and then create six specialist agents around it. One handles sales and marketing (explicitly not her strength). Another tool ingests her LinkedIn contacts and helps her identify who in her network matches her ideal client profile. She moves information between them manually for now, and is migrating to a more automated setup. This has helped fill the gap of the people she’s lost in the transition, enabling her to spend her time doing the stuff she’s really great at.
Another contributor, working within a large organisation, took a different approach. Tired of stakeholders constantly asking her the same questions, she built a custom GPT loaded with everything her team had, datasets, documents, and segmentation frameworks. With guardrails applied, she shared it directly with them. They could self-serve answers instead of routing questions through her.
She then did the same for a customer segmentation project, so the marketing team could query the persona framework directly, asking what kind of language or hook would work for a particular segment, without needing a data analyst in the room.
What This All Adds Up To
A few things are worth pulling out from across these use cases.
Firstly, the most powerful applications are quite sensitive and personal. The things that help an individual think more clearly, prepare more effectively, and show up more confidently. I think those are where the early value is being felt most acutely. They’re also examples that aren’t being widely shared anywhere else.
The framing matters as much as the tool. Asking for honesty with kindness gets a different output than asking for feedback. Asking for one question at a time gets a different output than asking for seventeen. The people getting the most from these tools are the ones who have learned to prompt with intention. One leader shared that she often uses Whispr rather than typing out her prompts and responses, as it allows for a much more detailed and natural dialogue.
I think it’s fair to say that no one is using just one tool. Every person in this conversation uses multiple AI platforms for different purposes, playing to the particular strengths of each, and deliberately using variety as a safeguard against their own blind spots. This is really smart, and something I think we’re going to see spread into the wider organisation strategy, too. I don’t think there will be one tool that does it all, but rather multiple tools being used for their strengths. One of the challenges I predict in the near future will be the discussions with the CFO on this method, but that won’t take place widely until the hype has finally died down.
And finally, there’s no correlation between seniority and sophistication. The most advanced use cases here came from people who had given themselves permission to experiment, often during a period of transition, when the usual guardrails of a corporate environment weren’t in place.
The organisations that figure out how to create that permission at scale, to encourage their most senior people to experiment as freely as they would on their own time, are going to be a long way ahead.
It was a real joy researching for this article. I hope you’ve found it inspiring. We’ll be revisiting this conversation in six months to see how the technology and how we use it have progressed. If you have use cases of your own to share, get in touch or leave a comment.
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