For many business leaders, Artificial Intelligence feels like a double-edge sword. On the one hand, they experience immense excitement about its enormous potential, often amplified by shareholder pressure to "do something with AI”. On the other hand, many of them feel completely out of their comfort zone, freeze up and delegate the entire responsibility of this essential technology to the tech experts inside or outside their organisations: be it data analysts, machine learning experts, their IT department or external consultants.

While it may seem a logical step for tech experts to take the lead on tech projects, this often creates a deeper issue that – as I will explain below – is rooted in the tension between automation and human augmentation. In fact, over the years, I’ve come to believe that the idea of technology driving technological transformation is nothing more than a myth.

Not everything is an efficiency problem

People with an engineering mindset tend to be inclined to look at and approach reality from a rationality perspective. It’s also what they were taught in school. Most of their day-to-day tasks are about solving efficiency or productivity problems: “If we automate this, it will become faster and better”. To be completely fair, this rationality paradigm of growth and optimisation is often just as popular in the business departments of universities, though it is perhaps a little more nuanced.

As a result, AI is often primarily perceived through the lens of cost reduction and efficiency. There’s this widespread expectation that AI will primarily and significantly boost productivity. In doing so, it will free us from mundane tasks and enable us to accomplish more, faster, and with greater quality. Even the data supports this optimism. According to GitHub, developers using AI coding tools report an impressive 88% increase in productivity compared to those who don’t. Similarly, a case study by Nielsen Norman Group found that customer service employees could handle 13.8% more inquiries per hour with AI assistance. Moreover, research indicates that when highly skilled workers leverage generative AI technologies aligned with their expertise, their performance can soar by up to 40% compared to those who do not.

A zero-sum game

Not bad, right? But here is where it gets really interesting. When AI enables employees to complete their tasks more quickly, organisations face two possible ways to utilise the resulting "free" time. The first is the utopian one. It’s the story of enhancement that many tech gurus love to tell their eager audiences: AI can and will liberate our workforce from all the tedious, repetitive tasks that are numbing their brilliant minds. It frees up their time and their minds so they can collaborate more, brainstorm more, or creatively hyperfocus on innovation.

It's a lovely story. And I do believe that the potential is absolutely there, as I discuss in my most recent book, “The AI savvy leader”. The problem is that most companies extrapolate the same rationality framework — that they use for managing technology — to human decision-making and behaviour. Instead of empowering their employees to rewrite their own jobs, in collaboration with organisational leadership, by regenerating, seeking interpersonal connections, and more intellectual down time — all of which are prerequisites for creativity and innovation — they fill in the extra time that is freed up (by AI), with even more tasks. So rather than using that time, freed up by automating dull and repetitive tasks, to stimulate what is unique to us humans, they unwittingly discard the idea that AI should augment human abilities and instead reduce their employees to mere “task completers.”

In doing so, organisations create a zero-sum game, where organisational efficiency becomes a choice between humans versus AI. Given the rational kind of thinking that favours economic self-interest, it is likely that humans will end up on the losing side. This is simply because completing more tasks in the short-term appears more appealing than betting on investing in the potentially unpredictable human abilities to create real long-term value.

The human cost

The consequences run deeper than the emergence of a zero-sum game mindset. If we treat humans as efficiency machines in an AI-dominated work paradigm, there will be an emotional cost as well. My own research indicates that employees working frequently with AI do indeed achieve productivity gains; completing more tasks in less time. So, there is definitely good news.

But the more tasks employees were able to complete, in collaboration with AI, the more they kept going. The result of using AI to promote efficient working thus led to an increase in their workload rather than a reduction of it. The consequence of this was that employees then also felt more socially deprived, lonely, unhappy and tired. In an ironic way, those resulting emotions and harm to one’s well-being are also the ones that in the long-term will ultimately make them less efficient.

Indeed, employees who feel disconnected and emotionally unfulfilled at work tend to be less engaged, less productive, and less committed to their organisations. Furthermore, they are also less inclined to collaborate, innovate, or exceed expectations in their roles, and are more susceptible to burnout, absenteeism, and high turnover rates.

In many respects, this oversight of human primacy in AI adoption in a way may be rather surprising, especially given the fact that many organisations are becoming increasingly attentive to their employees' physical and mental well-being. However, when it comes to AI-human collaboration, this focus seems to quickly fade into the background.

A holistic view

It is clear that AI adoption is not (only) an engineering exercise, but maybe primarily a behavioural one. It brings massive opportunities, but it's also one of the biggest leadership challenges of our time. And that is exactly why we need business leaders who both understand the opportunities and the limitations of AI. To create real value through AI adoption, leaders need to embrace a holistic view rather than a reductionist approach. Success requires thinking beyond just automation, replacement, and economic growth. Indeed, successful AI-savvy leaders think in terms of human augmentation, enhancement and wellbeing.

It’s essential to recognise that the jobs of the future have to remain tailored to human needs, rather than reducing people to mere task performers. AI is not a magical solution for optimisation; it’s a tool that should be thoughtfully aligned with an organisation’s purpose and,as such, create value for all stakeholders involved.

Same as it ever was, but now with AI in the picture

I’m always intrigued when I hear people say that leadership needs to be re-invented to meet the needs of this AI era. I don’t believe that. The qualities of great leaders today are much the same as those of 20 years ago, though they may be balanced and prioritised differently. They still need emotional intelligence, empathy, communication skills, meta thinking, intuition, critical thinking, trust, psychological safety, resilience and the ability to learn from failures. They need to function as bridges between departments and stimulate cross-functional collaboration, which is more important than ever. They have to bring meaning and work in purpose-driven ways so they know which business questions can be pursued in collaboration with technology.

As AI becomes increasingly ubiquitous and commoditised, it is not the mere adoption of technology that will help differentiate your company or create a strategic competitive advantage. It is the knowledge you have about your organisation and customers that should guide your strategic decisions on where AI is relevant to use and where it is not. Implementing AI in ways that enhance your organisational purpose can boost engagement, unlock innovation potential, and indirectly support the wellbeing of your human capital.

Skills can be lost

That’s why I always tell business leaders to invest at least 50% of their technology budget in change management. Do we have the right talent? Are we providing enough training? Do we have the right infrastructure? What about R&D? Are we creating the right conditions for employees to thrive with AI? These are the human questions they should ask themselves about AI so that they can both enhance efficiency and create the jobs of the future that are suited for humans.

The training part is absolutely crucial here, because few people seem to realise that even soft and emotional skills can be lost. Take today’s teenagers, for instance. Research has shown that a lot of them are losing interpersonal relationship skills because they over-rely on technology. They text, for instance, because they feel anxious about talking on the phone or even meeting in real life. Similarly, if you reduce your employees to task machines in an efficiency paradigm, their soft skills will be eroded.

Just to give a recent example, Gartner found that 30% of employees are avoiding more people at work today than they did two years ago. Just think about what that will mean for the quality of collaboration if they are not trained to connect and work together again.

Don’t get me wrong, I am just as much an AI optimist as I am a humanistic optimist. But I also believe that we have to rethink the roles that humans will take inside organisations that are increasingly adopting AI. Measuring them up to the same efficiency standards as AI is a mistake and will come with many unwanted side effects. So, this is my advice to all the business and technology leaders: put your employees first and AI second. That will bring us much closer to the AI utopia that so many love to talk about. The alternative is simply unacceptable.

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