Designing an AI Academy for everyone: our four-persona model
To meet this need at scale, we built a structured approach that we now call our AI Academy, built around four personas representing the different ways colleagues engage with AI:
1. AI for all
Every colleague across the Group, regardless of function, should understand what AI is, why it matters, and how to use it responsibly.
2. AI for leaders
Our executives and senior managers need to understand how AI can transform their business areas and how to lead teams in an AI-enabled world. This included our ‘leading with AI’ programme, developed in partnership with Cambridge Spark and our AI Ninjas initiative, which pairs leaders with our internal AI experts to drive two-way learning.
3. Builders of AI
Our specialist engineering and data talent who develop AI and Agentic products, requiring deeper, rapidly evolving technical pathways.
4. Enablers of AI
These are colleagues who shape the environment around the agent builders – architects, platform engineers, as well as risk, governance and change professionals.
We recognised that these personas would evolve over time, especially within ‘AI for all’ where different role types such as accountants, relationship managers and customer service colleagues would need tailored guidance on practical use cases.
This persona-led approach has since become a core part of how we design all new AI learning: targeted, practical, and grounded in real business impact. For all personas we are striving to move colleagues through the curve from AI literacy, through AI fluency to AI native.
Rolling out AI learning at scale: challenges and wins
Upskilling an organisation of our size (more than 60,000 colleagues) was never going to be straightforward. We faced several challenges that other organisations will likely encounter too:
The pace of change
AI evolves weekly, not annually. Training content can become outdated within months or even weeks. That meant building a culture of continuous learning, not a single course.
We also had to constantly adapt our ‘AI for leaders’ training as new capabilities emerged, re-engaging earlier cohorts to ensure long-term understanding.
No ready-made playbook
Much of the learning we needed didn’t exist in the market. Off-the-shelf training simply wasn’t sufficient. So we included a combination of in-house, co-created and off-the-shelf content, tailoring it to our use-cases and toolkits, and iterating it based on colleague feedback.
New styles of learning
We observed that colleagues, especially younger digital natives, were learning differently. Not through textbooks or formal certification alone, but through experimentation, mobile learning, and hands-on exploration. We made it a priority to create space and permission for colleagues to ‘play’ with AI.
A culture change, not a tech deployment
AI challenges long held assumptions about how work gets done. Our biggest win was encouraging curiosity: an organisational mindset where colleagues feel empowered to test, iterate, and improve processes using AI.
How our approach differs
Across the industry we’ve seen many organisations focus on narrow pockets of AI adoption; typically, within digital or technology teams. Our approach differed in two major ways:
1. We invested early in executive training
We were among the first major UK organisations to roll out AI training at scale for our Executive Committee, enabling them to lead confidently in a rapidly evolving environment. Other organisations have since taken similar steps, but our early investment helped set the pace.
2. We built a holistic, curated learning ecosystem
Rather than offer disparate training modules, we curated a broad yet deep set of pathways that include:
- contextual understanding of AI and its external landscape for all colleagues
- practical application guidance (“what you can do today”)
- technical development for builders
- governance, responsible use, and ethics.
This breadth, combined with depth where needed, is what made our approach stand out.
Leadership buy-in was critical
Our senior leaders, including our Chief Executive, Chief People & Places Officer, and Chief Operating Officer have been deeply supportive. That support wasn’t accidental; it came from a clear belief that AI will fundamentally change how banking works and a desire to lead that change rather than respond to it. As a result, we’ve had strong interest from other organisations looking to learn from what we’ve built.
