GUEST COLUMN:
Chris Butt
CEO & Founder
DeepLearnHS
We’ve been using forms of AI in hiring and workforce planning for more than a decade now. From the earliest CV-sifting tools to today’s predictive workforce analytics, the underlying purpose has remained broadly the same: to help people make better, more informed decisions about other people.
The difference now is the scale, sophistication and speed at which these tools operate, and the growing awareness that, used thoughtfully, AI can support careers from school right through to retirement.
Much of the early AI in HR was born out of necessity. No human being can sift through thousands of applications and make consistently objective decisions. We get tired, we get distracted. Automating parts of the process brought fairness and consistency where before there was often little. From there, tools evolved to support succession planning, workforce intelligence and performance tracking. In large organisations, especially, this shift has been transformative.
But the value of AI goes beyond operational efficiency. At its best, it brings a human-centred lens to systems that too often felt transactional or detached. The technology should follow the principle of people first, systems second. If the purpose is to help human beings be the best they can be – within an organisation or in their own career – then AI has an important role to play. But if the goal is simply to accelerate output or reduce headcount, then it risks becoming just another blunt tool for productivity.
That’s why I believe in integrating these technologies from the earliest stages of education. At DeepLearnHS, our work starts in schools, where we help young people prepare for the world of work in a way that is informed by data but driven by personal agency. This continues into higher and further education, apprenticeships and eventually into employment. Through this approach, AI can support more tailored career journeys based on real insights, rather than assumptions.
There’s huge potential in helping individuals and employers spot patterns, whether it’s identifying the likely risk of attrition, recognising gaps in skills, or highlighting those with the aptitude to progress into leadership roles. AI enables us to join the dots more quickly and accurately. And with enough good-quality data, it can do this at a scale no human ever could.
Of course, the quality of the insights depends on the quality of the data. If you’re working from outdated or poorly constructed surveys, you won’t get the results you’re hoping for, no matter how sophisticated your system is. It’s not just about the volume of data; it’s about structure, consistency and context. And the more regular and real-time that information becomes, the more useful it is.
The same logic applies to employee experience. A once-a-year engagement survey, while still useful, offers only a narrow window into what’s happening on the ground. AI allows us to track trends more continuously and offer timely interventions, whether it’s wellbeing support, reskilling opportunities or changes in role design. It also helps to surface information that may be buried under admin or internal noise, giving leaders the clarity to act.
This matters because too many people enter the workforce without a clear sense of what suits them best. The old model – learn for 18 years, then figure it out on the job – is no longer fit for purpose. If we can use data from education, early employment and beyond to spot patterns and tailor pathways, we give people a better chance of doing work that is aligned with their strengths and interests.
It also helps level the playing field. When I was growing up, having access to the Encyclopaedia Britannica at home gave you a clear advantage. Today, generative AI tools are democratising access to information in a similar way. They’re not replacing thinking – they’re reducing the cognitive clutter and freeing up people to focus on the work only humans can do: creative, empathetic, relationship-driven work.
That’s the opportunity. But we must also recognise the responsibility. People will only trust AI if they understand it. Organisations need to be clear about what these systems do, how they’ve been built, and what data they rely on. Ethics has to be part of the design process from the outset – not an afterthought. And transparency is essential, especially when these systems influence recruitment, promotion or performance management.
Looking ahead, I hope we’ll see a greater shift towards experiential work – more onboarding, more human interaction, more learning on the job. If AI can reduce administrative load and provide better insights, it creates space for businesses to invest more in their people. That’s what I’d like to see. AI as an enabler, not a replacement. A tool for progress, grounded in purpose.
Listen to Chris discuss this and more in episode one of the DeeplearnHS podcast series, AI in Hiring & Workforce Strategy, here
