HSBC appointed its first Chief AI Officer in March 2026. The appointment was read across the sector as a signal that artificial intelligence had moved out of the technology function and into the operating model.
Dr Rita Fontinha, director of flexible working at the World of Work Institute, Henley Business School, researches workforce development, organisational change and skills in banking. Her argument is that the constraint on AI adoption in banks is no longer the technology but the readiness of the people expected to use it. The Fintech Times put written questions to her on where responsibility for that readiness sits, what a systemic approach looks like in practice, and which banks have something worth copying.
Why has AI adoption in banking become a workforce challenge rather than a technology one?
AI adoption in banking is no longer just about choosing the right technology. The bigger challenge is understanding how it will affect people's jobs, skills and sense of job security. AI may not replace most jobs entirely, but it will transform many of their tasks.
Our research suggests that AI can be both helpful and demanding. It can remove repetitive work and improve productivity, but it can also create uncertainty, pressure to learn quickly and concerns about monitoring or job security. The outcome depends greatly on how banks introduce it. Ultimately, AI creates value only when employees have the confidence, skills and support to use it effectively and responsibly.
HSBC has appointed its first Chief AI Officer. What does that signal about how the sector is organising for AI, and where does responsibility for workforce readiness now sit?
HSBC’s appointment signals that AI is moving from dispersed experimentation towards strategic leadership at a corporate level. Appointing a Chief AI Officer suggests that AI is becoming key to the bank’s operating model rather than remaining solely an IT responsibility.
However, appointing one senior leader should not concentrate all responsibility for workforce readiness in a single role. That responsibility must be shared across the Chief AI Officer, HR, learning and development, technology, risk, business-unit leaders and line managers. HR must translate the AI strategy into workforce planning, job redesign and development pathways. Managers must help employees apply the technology responsibly in their daily work, while employees should have a meaningful voice in implementation.
Are banks doing enough to prepare employees for AI-enabled roles, and where are the biggest gaps?
Many banks are investing in AI tools and introductory training, but training does not automatically translate into capability. Employees need opportunities to apply approved tools to real work, understand their limitations and receive feedback.
Key gaps include insufficient learning time, uneven access to development and limited preparation for line managers. Banks must also develop technical skills, as well as critical and ethical judgement.
You argue that one-off AI training is not enough to build long-term capability. What does a systemic approach look like in practice?
A systemic approach starts by identifying how AI will change tasks and skills across different roles, and then providing learning pathways tailored to those needs.
Learning should be continuous and embedded in everyday work through protected time, practical experimentation, peer support and coaching, rather than simply setting time aside for a mandatory two-hour training, for example. It must also connect with job design, workforce planning and career progression.
What role do managers play in helping teams adapt to AI, and what support do they need themselves?
Line managers are essential because they translate an organisation’s AI strategy into employees’ everyday experience. They determine whether people receive time to learn, feel safe admitting uncertainty and understand where AI can (or cannot) be used. They also identify how tasks are changing and where employees may need additional development.
However, managers cannot fulfil this role if they are themselves uncertain, insufficiently trained or already overstretched. They need early access to tools, role-specific guidance and clear escalation routes for ethical, regulatory and data-related concerns. They also need support in redesigning work, leading conversations about career change and evaluating performance when employees and AI jointly produce an outcome.
How can banks develop AI skills while reducing uncertainty and building employee
confidence?
Banks should communicate honestly about why AI is being introduced, how roles may change and what support employees will receive. Involving employees in testing tools, providing protected learning time and maintaining clear human accountability can make adoption less threatening. While there cannot be a promise of job security, training should also connect to credible career pathways, showing employees how new skills can support meaningful work and progression.
Which banks or interventions have you seen get this right, and what results have
followed?
There are several promising public examples, although it remains too early to draw firm conclusions about long-term workforce outcomes. NatWest has combined bank-wide access to approved AI tools with training for approximately 60,000 colleagues; more than half reportedly opted for additional development. Its bank-wide accreditation in AI and data ethics is also important because capability must include responsible judgement, not simply tool proficiency.
HSBC has introduced mandatory responsible-AI training alongside an AI Academy offering learning from beginner to advanced levels. This illustrates the value of combining common foundations with differentiated development.
A particularly useful intervention is protected experimentation time, such as initiatives encouraging employees to apply AI to real work problems regularly rather than only attending a course. The most convincing evidence will eventually come from organisations that evaluate not only adoption and productivity, but also skill development, employee confidence, workload, job quality and customer outcomes over time.
The post AI in Banking Is a Workforce Problem, Not a Technology One appeared first on The Fintech Times.
