If you’re looking for indicators of how seriously organisations take AI adoption, you don’t have to look too hard.
In our most recent survey of UK CEOs, 81% cited AI as a top investment priority for their organisation, claiming they expect to spend at least 10% of their budget on AI. Seventy-one percent said they were redesigning roles and career paths to account for greater AI collaboration, while 52% said they were bringing in external expertise to help their workforce embrace AI.
For people working in HR and people functions, those numbers are hugely encouraging; evidence of senior leaders appreciating the importance of AI and putting their money where their mouth is.
Yet there’s still plenty of disquiet. We’ve found that 75% of organisations aren’t seeing a return on their AI investments, for example.
My take on the AI challenges facing employers is that too many still treat AI adoption as a technology transformation, rather than a people transformation. I’m also unsure whether enough go beyond generic digital and AI upskilling to think about the bigger considerations; the soft skills, the managerial demands and the staff wellbeing implications, for example.
The financial services perspective
Keen to understand whether what I’m seeing around AI skills is replicated across financial services , I checked in with my colleague Sara Belchamber. Sara, who specialises in organisation and people transformation in the sector, was broadly in agreement.
“Plenty of financial services organisations are rolling out extensive digital training,” she said, “but the current skills requirement goes further than that. I’m thinking about skills like the critical thinking and judgement that are required to use AI in a safe, ethical and compliant manner; something which organisations in this sector worry about more than most.
“People’s roles and responsibilities are already changing as a result of AI being incorporated into their work. Employers need to recognise this, formally redefining the tasks employees are expected to perform and thinking carefully about the skills this requires. The mistake many organisations make is trying to fit AI into existing roles, rather than redesigning the roles themselves. When AI replaces or radically reshapes core tasks, the roles themselves cease to exist in their previous form. What emerges is a new role with different responsibilities, decision making authority and skill requirements – often ones that people were never hired or trained for.
“The point about struggling to get a return on investment feels right to me. Many organisations are starting with well-intentioned technology pilots but are then struggling to scale up from there. It may even be that they never formally defined what they were hoping to get out of it in the first place, as it was all so experimental.
“To address this, they need to treat this as they would a full-blown transformation programme, complete with business case, benefits and tracking. And it needs to be a people transformation programme, with all the formal rigour, engagement, training, senior leadership sponsorship and adoption tracking that entails, not solely a technology implementation.”
The productivity challenge
Sara also mentioned how the expectation that using AI automatically equates to increased productivity can be a challenging one for employees. Under pressure to deliver that productivity, but wanting to perform all the checks they know are their responsibility (and which are never as black or white as you might think), something has to give. That’s where things get missed; checks get overlooked. Everyone assumes the quality is right, when actually it isn’t.
I thought that was really interesting, thinking about the pressure that AI places on employees. In pre-computer days, when I was at the point of cognitive overload and needed to do something different, I’d do some basic filing and tidying up for 20 minutes. What’s the equivalent in today’s AI age, when all the mundane tasks have been taken off us?
The non-stop application of these elevated skills that allow us to view AI’s outputs with healthy objectivity can be exhausting. Senior people leaders are going to need to think very carefully about how best to support employees who are constantly working at that level.
The bottom line here is that there’s so much to consider within the learning and upskilling side of AI adoption. You’ve got to upskill your whole workforce on the basics. You’ve got to think about the differing needs of teams vs managers vs leaders; the gung-ho adopters vs the laggards. And that’s before we get into softer skills, job shaping and strategic workforce planning. It’s a massive challenge – but the pay-off could (or should) be huge.
