Six ways to strengthen workforce capability in AI‑enabled banking

The recent appointment of HSBC’s first Chief AI Officer is a clear signal that AI considerations span far beyond the remit of the CTO. But as AI adoption becomes a strategic play, the sector faces a key challenge. How to build a workforce that is confident, capable and ready to thrive in an AI-enabled environment.

Research across banking, industrial relations and skills policy is remarkably consistent on one point – capability building cannot be treated as a training exercise. It requires systemic, integrated interventions that address the real conditions shaping employee behaviour: access to development, time to learn, motivation to engage and confidence in future opportunities.

Banks that get this right will not only accelerate innovation but also strengthen retention, internal mobility and trust which are all critical factors in a period of rapid technological change. Whilst the CAIO role and remit is in its infancy, those institutions that treat AI capability as a ‘bolt‑on’ will risk widening skills gaps, eroding employee confidence and slowing transformation.

During my research and work with organisations in the sector, six key interventions have become clear, which will help banking leaders to build a resilient, future‑ready workforce.

1. Establish governed development pathways linked to retention and internal mobility

In an AI‑enabled environment, employees need clarity about how to grow and why it’s worth investing in their own development. Research shows that when employees cannot see credible internal opportunities, they are far more likely to look externally. Banks should therefore create structured, governed development pathways that link skills acquisition to specific job families, emerging roles and internal labour market opportunities.

Embedding these pathways into workforce planning sends a powerful signal. Development is not discretionary, but a trusted route to progression and job security. Co‑designing these pathways with unions or employee representatives can further strengthen transparency and fairness, reinforcing internal mobility and reducing attrition.

2. Embed mentoring and coaching as core workforce infrastructure

AI capability is not built through courses alone. It is strengthened through relationships, dialogue and applied learning. Yet research shows that disengagement from learning often correlates with reduced internal mobility and resistance to organisational change.

Mentoring and coaching should be considered as core infrastructure, not optional extras. Formal schemes, supported by leadership, governed for consistency and embedded into performance frameworks, will help ensure that development is relational, trusted and sustained. This will also build leadership capability, strengthen engagement and support employees who aspire to progress.

3. Protect time for development through workforce and capacity planning

Time is one of the most significant barriers to skills development in banking, particularly in customer‑facing roles. Without protected time, employees default to short‑term performance pressures at the expense of long‑term capability building.

Banks should introduce minimum, ring‑fenced development time, supported by operational planning and leadership accountability. Reviewing the training portfolio to prioritise high‑value development can further reduce noise and increase impact. These measures will also demonstrate the organisation’s commitment to long‑term employability and help retain employees who might otherwise feel left behind.

4. Link skills development to job security and future role clarity

Uncertainty about the future of work is a major source of anxiety in the sector. Employees want to know which skills will remain core, which are emerging and how they can reposition themselves as roles evolve.

Banks can reduce this uncertainty by explicitly linking skills development to future role design and workforce transition planning. Clear signals about future skills expectations which are embedded within learning governance and social partnership arrangements can help build trust that AI adoption is being managed responsibly and with workforce stability in mind.

5. Embed line‑manager accountability for AI skills development

Line managers are critical enablers of AI capability. This doesn’t mean that they need to be technical experts, but they do need to support applied learning, ethical judgement and responsible use of AI tools within their teams.

To facilitate this, banks need to make AI capability a recognised part of managerial responsibility, supported by guidance, time allocation, coaching and communities of practice. Without this explicit ownership, AI skills development risks becoming inconsistent, abstract or deprioritised in high‑pressure environments.

6. Use development governance to reinforce trust and engagement

Finally, motivation to learn is shaped by organisational context. Employees engage when development feels relevant, applied and connected to real outcomes. Banks should therefore treat learning governance as a trust‑building mechanism, reducing reliance on generic, compliance‑driven training and increasing experiential approaches such as shadowing, project‑based learning and cross‑team collaboration.

By following these interventions and aligning them with workforce preferences and sector best practice, the banking sector will be able to strengthen engagement and embed the necessary capabilities for a future-ready workforce.