As artificial intelligence reshapes the pace of work, one researcher’s global conversation with business leaders reveals the human skills that will decide who thrives, and who is left behind. In corporate boardrooms, on factory floors, and even in call centers, there is an undercurrent shift taking place. Artificial Intelligence is reducing time taken by teams of people to complete certain jobs in days down to minutes, and organizations are scrambling to incorporate the technology. However, a lot of data points towards the fact that the rate at which technology is evolving is much faster than the rate at which human beings are keeping up with it. The outcome of this disparity is not increasing efficiency but rather, the growing mismatch between the capabilities of artificial intelligence and people’s ability to use them to its full potential. A mismatch that is costing businesses dearly, and compelling organizations to consider carefully what kind of human skills really matter in the modern AI-infused world of work.
It is this dilemma that has been on Nicholas Brice’s mind for most of the last year. Brice is the Chief Executive Officer of Soul Corporations®, a speaker, consultant and author of The Mindful Communicator, and throughout his career, Brice has been deeply interested in understanding the human side of organisational transformation. How people think, communicate, collaborate, and create culture and customer experience in business. The starting point for his inquiry was deceptively simple. “What happens when technology changes much faster than the people using it can change?”
This question, according to Brice, is the central issue around which the implementation of AI in organisations revolves. The vast majority of organisations are actively urging their employees to adopt the technology, and the first and obvious impact is the speed. An increase in speed that, from his interviews, he discovered far exceeds any organisational efforts to redesign collaboration itself. One leader told him that “The technology roadmap is light years ahead of the behavioural roadmap.” Yet another leader warned that AI might only enable us “to go faster in the wrong direction.” Brice identified five key issues based on his interviews:
Human value is moving up the chain
The first theme concerns where human contribution now sits. Brice argues that the challenge facing most organisations has moved beyond simple AI literacy. From knowing how to operate the tools towards how to use them well. Experimentation matters, he found, but converting fresh ideas into real business value increasingly requires skills and practices that must be built both individually and collectively.
AI is compressing routine activity at scale: research, summarising, analysis, report writing, slide production and customer routing. At one major media and telecommunications company, AI now analyses hundreds of thousands of customer-call transcripts, work that was previously done manually by large teams. His reading of this shift is that humans are not being made redundant so much as repositioning their value. Moving from doing and processing toward interpreting, questioning, deciding, coordinating and acting. Where organisations fail to make that transition, he identifies what he calls a ‘capability gap.’
That gap carries a measurable cost. Research from Kingston University, commissioned by Publicis Media, modelled the skills mismatch in UK advertising and marketing and found an average productivity loss of 24.6 per cent per role equivalent to roughly £10,584 per employee each year. As AI reshapes the work itself, he notes, capabilities such as problem framing, interpretation, communication and influence become increasingly critical; without them, AI risks amplifying gaps rather than closing them, producing rework, inconsistency and a growing burden of checking.
The Kingston research also proposes three ‘fusion skills’ for working alongside AI: intelligent interrogation, reciprocal apprenticing and judgement integration- each demanding a strong base of human capability. Brice raises a further concern: if AI removes the junior tasks through which people traditionally learn a profession: researching, checking, drafting, making mistakes and correcting them- then the conventional path to expertise may disappear along with it. A senior consulting leader raised this directly in conversation with him, and Brice argues that organisations will need to build new mechanisms for developing experience once those tasks are automated away, a shift with direct implications for both curriculum design in education and development strategy inside employers.
Is human judgment becoming even more valuable?
The second theme centres on judgment. AI, Brice observes, produces work that is cheap, fast and plausible-looking but plausibility is not the same as accuracy. As AI-generated answers proliferate, he argues, people need to become sharper at sifting through them, and heightened cognitive discernment becomes critical rather than optional.
Brice illustrates the risk with an encounter he had with a young training manager building train-the trainer capability for a fast-growing part of a business. Offered well-tested material that could save time, the manager’s response was telling he did not need any materials at all, he said, because he could simply have AI produce the training. Brice sees in that exchange a wider danger: not that AI makes training easier to produce, but that ease of production is increasingly mistaken for quality of learning. The point was reinforced, he notes, by the CPD Standards Office, which introduced a new AI in Training & Education Standard earlier this year after raising concerns about AI-produced training submitted without sufficient expert human review.
The consequences of unchecked AI output can be serious. In 2023, Australian academics used AI to generate case studies for a parliamentary submission concerning major consulting firms; some of the allegations were invented, included without proper verification, and later had to be corrected. Brice heard versions of this concern repeatedly in his interviews: organisations are training staff to check AI output, yet leaders still observe people accepting attractive-sounding answers too quickly. A recap from a Culturati: LIVE session captured the underlying discipline required: do not confuse a polished AI output with a sound business decision.
Individual productivity must become collective capability
Brice’s research also points to a shift from individual to collective performance. Brice’s research found seven recurring shifts across all his interviews, among them: doing becomes interpreting and acting; management control becomes coaching and orchestration; and individual productivity must become collective alignment. One senior software engineering leader told him that while individuals are becoming proficient at using AI in their own work, teams as a whole still have considerable ground to cover. Output requires judgment, information requires sense-making, and constant activity requires intentional focus. Technology adoption, in other words, must be matched step for step by human capability.
Capability depends on the conditions in which people work
The fourth theme concerns attention. While AI accelerates output, Brice argues, human attention is coming under sustained pressure. A marked contrast, he suggests, to earlier generations who could spend an evening on focused, uninterrupted work. At a recent Engage Summit, Sean Tolram, Head of Mindfulness at HSBC, argued that distraction, constant context-switching and overloaded calendars are weakening attention, memory, decision-making and communication, advising leaders to “work with your brain, not against it.” Brice’s own conclusion from his interviews is unambiguous: organisations cannot demand better quality thinking while designing working conditions that leave no human capacity to think.
People need space to learn, reflect and experiment together as well as alone, he contends. “It is a little like a group of cyclists climbing into Ferraris. Small, correctable mistakes on a bike can become a pile-up at Ferrari speed.”
The ability to detach deliberately from external and internal noise, he argues, should be treated as a strategic competence rather than a wellness extra. One that creates the space to observe, question and choose before reacting. Going faster with AI, in his view, sometimes requires organisations to go slower and deeper first, and to do so together.
A common language for human capability
The final theme addresses how these capabilities are actually built. A single training day, Brice notes, may raise awareness, but sustained behavioural change requires ongoing practice, feedback and reinforcement. A conclusion the Kingston/Publicis report reaches independently, warning that one training programme alone will not close a fundamental skills gap. A Customer Experience Proposition Manager at a major national retailer, who completed a three-to-fourmonth coaching programme built around Brice’s Seven Principles™ methodology, reported lasting improvements in attention, listening, planning, prioritisation, communication and team practice, explaining that the change endured precisely because the programme was not a one-off intervention but a slow build over time.
To make that development practical, Brice has developed what he calls the Human Capability Tree™, a framework built around seven ‘superpowers’ designed to be practised individually and collectively so that people and teams can work well alongside AI. A shared vocabulary, he argues, helps embed human capability into everyday AI enabled work rather than confining it to a classroom, prompting teams in meetings and coaching sessions to ask whether they are present enough to notice what matters, clear about the intended outcome, trusting AI appropriately, safe enough to challenge one another, aligned with stakeholders, and focused on the value they are actually creating.
A different North Star
Reflecting on what his interviews revealed collectively, Brice suggests that the more urgent question facing organisations may no longer be how quickly they are adopting AI, but what kind of humans they are becoming in the process.
“Faster, better and more efficient is not our only North Star,” he states. For Brice, that reframing is the essential takeaway of his research: AI’s greatest returns will not come from speed alone, but from the deliberate cultivation of these essential human capabilities. A shared language that informs the superpowers needed to handle the speed and generate real value for stakeholders.
