e&
2024 - 2026
Accelerating AI adoption using personas
Adoption across a group of 9,000+ people was never going to be one programme. I built personas for the main stakeholder groups at e&, mapped where each one was starting from and what was actually stopping them, then designed a different route in for each.

Overview
Starting with the people we are designing for
To accelerate AI adoption across e&, I started by identifying the people we needed to bring on the journey with us. Rather than treat the organisation as one audience, I built six personas based on the real stakeholder groups I was working with, from the C-suite through to the wider workforce, so I could design initiatives around what each group actually needed rather than run a single generic programme at everyone.

Step 1
Talk to people
Spoke directly with leaders, team leads, and employees to understand where each group actually stood with AI.
Step 2
Build the personas
Grouped the recurring patterns into six personas, from CXOs to the wider workforce.
Step 3
Design for each group
Built a distinct initiative for every persona instead of running one programme at everyone.
CXOs and Senior Leaders
Persona #1: Leader Lateef
Expected to lead on AI before he has used it much himself. He wants to set the example for his team, but he will not put his name to something he cannot point to results for.
What he needed: To feel genuinely part of the AI journey, not just informed about it from the sidelines. He wanted to learn, but he needed that learning tied to real evidence of impact before he would advocate for it publicly.
Why he mattered: Nothing below the top would move until leadership owned this themselves rather than leaving it to IT. In my conversations with leaders like Lateef, the appetite was already there, what was missing was real understanding of AI, and proof he could point to before he'd put his name to it.

Demo of AI Use Cases Repository
I designed an AI Use Cases Repository based on the Roadmap for visibility on the prioritised domains and use cases.
Team Members
Persona #2: Reluctant Ramy
Ramy has done things his way for years and doesn't see how AI would fit into how he actually works. He isn't against AI, but he needs proof before he'll trust it with something this important.
What he needed: The chance to test the tool hands-on, and to try to catch it out, before he would trust it with a process he had spent years refining.
Why he mattered: Team-level buy-in decided whether any of this actually worked. In my conversations with people like Ramy, the resistance wasn't to AI itself, it was to handing a careful, high-stakes process to something new without proof it wouldn't get it wrong. A tool doesn't earn the right to touch a process like his just because it exists, it has to earn trust first.

Innovators and Techies
Persona #3: Innovator Inaya
A trailblazing AI advocate, already reaping the benefits and inspiring others to follow suit.
What she needed: Real projects to keep pushing on, not just workshops, and a formal channel to mentor others rather than advocate informally on the side.
Why she mattered: Peer proof lands in a way a leadership announcement never does. In my conversations with innovators like Inaya, the results were already there, she simply had no stage to put them on. Giving her that stage did double duty. It didn't just spread what she knew, it moved her own contribution from doing the work herself to shaping how others did it, which is where her time started to matter more.

Most employees
Persona #4: Curious Chloe
Chloe is curious about what AI could do for her role, but with no clear, low-effort way to start exploring it, that curiosity has nowhere to go.
What she needed: Small, easy entry points she could work through in a couple of minutes, not a course or a mandate, just a way to follow what was possible without a big time commitment.
Why she mattered: This is the largest group in the organisation, and the one most at risk of interest fizzling out for lack of an easy way in. In my conversations with employees like Chloe, the appetite to learn was already there, what was missing was a low-effort way to act on it, one that could keep pace with how quickly the subject itself kept changing.

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AI Champions
Persona #5: Ready Rashid
A trusted tech or strategy voice within his own team, ready to carry the AI roadmap forward the moment someone hands him the mandate to do it.
What he needed: Real autonomy to run his part of the roadmap, a clear line back to leadership, and resources he could point his team to without having to build everything from scratch.
Why he mattered: A strategy sets direction from the top, but someone still has to keep it moving inside each team, day to day, closing the gap between a decision made in a council meeting and a change that actually happens on the ground. In my conversations with people like Rashid, that was exactly the role they wanted, real ownership of their corner of it and accountability for the outcome, not just a mention at a town hall.

External Partners and Stakeholders
Persona #6: Stakeholder Sara
Watching from outside the organisation, deciding whether e& is genuinely ahead on AI or simply talking about it.
What she needed: Credible, public proof that AI was embedded in the organisation's strategy, not just in its internal messaging.
Why she mattered: Internal adoption is only half the picture. In conversations with board members and investors like Sara, what mattered wasn't what was happening inside the organisation, it was what was visible from outside it.

Wrap up
Bringing it all together
These six tracks ran in parallel, reaching over 9,000 employees through the entry point that actually made sense for each of them. None of it would have worked without starting from real conversations rather than assumption, that human-centric approach is what let six different initiatives land where one generic rollout would have failed.








