Have you seen this fascinating research by Marc Zao-Sanders from the UK, featured in one of Harvard Business Review’s most popular articles titled ‘How People Are Really Using Gen AI in 2025?’ The original study dives into how generative AI tools are being applied across daily life. One finding from his 2025 Top-100 Gen AI Use Case Report stands out above the rest particularly for how it relates to coaching: therapy and companionship have become the most common uses of generative AI. To me that’s astonishing – even though I know the power of these tools and I’ve been working in the space of conversation AI for 8 years. It still blows me away and is worrying at the same time. But, societal issues aside, before we unpack what this means for learning and development, let’s take a look: the top five generative AI use cases in 2025, according to Marc’s research.
The Top 5 Gen AI Use Cases in 2025
• Therapy / Companionship – AI is being used for emotional support, grief processing, and self-reflection. People are seeking comfort, clarity, and connection—often in private, judgment-free spaces.
• Organise My Life – From scheduling to habit tracking, AI helps users structure their routines and reduce cognitive overload.
• Find Purpose – Users engage with AI to explore values, goals, and life direction—essentially using it as a sounding board for existential clarity.
• Enhance Learning – AI supports personalised education, tutoring, and skill development, helping learners grasp complex topics and stay motivated.
• Generate Code – Developers use AI to write, debug, and optimize code—speeding up workflows and reducing technical barriers.
These use cases reveal a shift: people aren’t just using AI to get things done—they’re using it to grow. People aren’t just using AI to write code or summarise documents. They’re turning to it for comfort, clarity, and connection.
AI may be driving conversation and connection for people, but is it driving change? We need to ensure that it’s a productive conversation and not filling an emotional hole, which it would be better to have people’s needs met in other, more human ways.
From my own experience developing generative AI conversation tool, designed to support learning transfer and the behavioural change part of learning, one of the keys was reducing unnecessary verbosity to ensure that every conversation is meaningful and leads to real action, rather than just being a conversation for its own sake. Coach M encourages time-bound interactions, which we have found to be more effective for change than open-ended discussions that can go on for hours. Coach M is designed to be a useful too, not a companion, much like a human coach.
The Anatomy of Transfer
Marc’s report reveals a shift toward introspection and personal growth. That aligns with the core elements of learning transfer and the Turning Learning into Action™ (TLA) methodology.
• Structured reflection: AI can prompt it, but TLA guides the reflection
• Action planning: AI could suggest steps, but TLA ensures ownership.
• Accountability: AI can remind, but TLA builds commitment.
In short, general AI may be good for a conversation—but TLA ensures it leads somewhere.
Why L&D Must Lean Into Transfer
If therapy and companionship are the top use cases, it means people are ready to engage. They’re open. They’re listening. That’s a golden opportunity for L&D bring AI tools into learning in a meaningful way to solve the problem of scrap learning.
• Design programs that go beyond content into conversation that creates accountability.
• Build systems that support follow-through and application, not just attendance.
• Measure success by implementation, not just inspiration, or intention to apply.
Learning transfer isn’t a nice-to-have—it’s the mechanism of real change.
Final Thought: Insight Is the Spark, Transfer Is the Flame
Marc’s findings show us what people are craving. Learning transfer shows us how to meet that craving with depth, structure, and sustainability. Generative AI is listening—really listening. And people are opening up to it in ways that are surprisingly deep.
But my question has always been: what do we do with what we learn?
It’s not just about smarter tools—it’s about using those tools to spark action and make learning stick.
Personal note – JC and I work closely together on our blogs. Thank you, JC. The first draft JC came back with for this blog sounded so good it was obvious that AI had written it, and not me or him (and JC knows there’s no offence in that!). It sounded too perfect, too crafted and too word smithed to be ‘real’. But it was impressive. I’ve made it now more my voice, and thoughts, but can you tell which phrase I left in? I couldn’t decide if ‘Insight Is the Spark, Transfer Is the Flame’ was too good to leave in or too good to take out? Thoughts in the comments please.
