AI will support decision-making, but the new “AI super-leader” will still make the decisions, working within a distributed power structure, and taking people with them.

These decisions will demand resolute focus on the business goals and solutions to be served by AI, and not solely on what AI can solve. These will be goals and solutions supported by data on a new scale, shaped and challenged by the human brain.

A radical refocusing of skills and resources will be required, but with the human leader still very much front and center. Here we explore why – and how – HR and L&D leaders can drive this critical shift in their organization.

As more applications become technologically possible and easier to execute, the human/technology relationship will be redefined. Goals and their KPIs will need to be adjusted in line with new distributed decision-making and organizational design. HR and L&D leaders must make sure best-practice, progressive approaches are adopted – and fast.

As organizations increasingly have access to the same AI capabilities, thanks in large part to the growing availability of generative AI, they should be laser-focused on their greatest business opportunities and most pressing challenges. “Going forward, more focus should be on business solutions rather than technology-based business solutions,” says Terence Tse, Professor of Finance at Hult International Business School. “But in order to do that, people across the organization will need to acquire a high-level knowledge of what technology can do and how it works.”

Correctly identifying the business goals and problems that need solving will also become a key differentiator for organizations wishing to remain competitive. Ultimately, AI doesn’t always have to be the answer and there’s a danger in being deafened by the buzz surrounding it. “Be careful of technology looking for solutions. Look for problems that can be solved with technology. Very easy to say – much harder to do,” warns Philip Hult, Chairman of EF Education First, the world’s largest private education company, of which Hult EF is the B2B arm. The role of AI in solving those problems and supporting business opportunities must be carefully considered. “AI is best at accomplishing tasks that are standardized, routine, and repetitive,” says Tse. “AI can solve a lot of calculation problems but there are many more business problems that it cannot figure out, however much you train it. It’s the uniquely human skills of creativity, lateral thinking, and connecting the dots that are needed to do that.”

In a world of AI, success is dependent on relentless focus on setting goals and making decisions powered – but not made – by AI. This in turn hinges on having the right data.

There has been a significant focus in recent years on being data-driven, but with arguably insufficient focus on whether the data – and the questions that are asked of it – are right.

To ensure they’re ready to capitalize on the upsides of AI, organizations need to focus now on getting the right infrastructure and clean data. AI tools do not necessarily need to be built in-house – they can be purchased, piped in, and augmented with proprietary data. “For generative AI to become a really robust, commercial, competitive tool, we should be thinking about training it with in-house data,” says Jennsen Fung, Executive VP, Digital Marketing, EF Education First. “As all AI tools will be bought on a software license model, the real differentiator and competitive edge for businesses will be around the data they’re capable of feeding to the AI engine.”

Equally, if not more, important is how the data is used. “We can give team members very clear directions on how to use the technology and data, but the more critical question is what are the key problems we want to solve? Is the issue about productivity or growth? Is it decision-making or protection? Once you have clarity on these and can marry them with the business priority, you can achieve whatever you need to do in a faster way,” continues Fung.

The time to start sorting out your data is now. “AI/machine learning will need data on a new scale,” says Hult. “To that end, we have been working hard for several years to get our data consolidated, accessible, and into agnostic formats. This work is very slow.”

Part of the data requirement to inform fast, good decision-making will come from KPIs. These will need to be fundamentally different to reflect the high-change environment that AI will invariably create. “Performance measurement cannot be predetermined by KPIs looking at the past,” says Mark Esposito, Professor of Economics and Strategy at Hult International Business School. “How do we use anticipation to anchor performance measures in what will happen in the future? Otherwise, we’re creating a consistent form of discrimination based on KPIs that do not match the challenges of the job.”

Esposito believes organizations need to become experimental by taking more of a hypothesizing and testing approach and not just assuming they know the answer. “It requires leadership in the purest form to assess the viability of KPIs and the ability to challenge them,” says Esposito. The sandbox testing and experimentation method, for example, has been successful in setting KPIs and measuring them, using interdisciplinary ideas and thinking to test approaches in real-time.

Rapid decision-making is also dependent on having the right organizational structure to support it. Organizations must move from hierarchical structures to networks enabling distributed decision-making and risk-taking. Esposito continues: “There needs to be a deeper shift in how organizations allocate resources, autonomy, and delegation, so they’re not based on reinforcement of power at the top. This is an act of trust that only happens in an organization where you’re leading toward the future, rather than just managing the process itself.”

As AI forces a reimagining of the organization and those needed to operate within it, human qualities are becoming increasingly important. New AI super-leaders will need to emerge, who will use AI tools to inform their decision-making but still make the final call themselves – and not necessarily that suggested by AI. These AI super-leaders will need to include the people around them in making decisions, working in a structure that is distributed rather than top-down. They will be communicators, collaborators, and change-makers. And their eyes will be pinned squarely on the future.

As AI accelerates the pace of change, organizations need the kind of leader who thinks about the bigger picture in which their organization is operating, acts accordingly, and is technologically savvy.

“Leaders need to have a wider perspective because of the rapid geopolitical, demographic, and market changes, not to mention understanding the different types of technologies that are emerging,” says Tse.

“I think leaders must have a broad view of what needs to be done internally and a willingness to step away from the day-to-day activities,” continues Tse. “They must be highly adaptable, able to act decisively and quickly, and be proactive in the way they connect and collaborate to make the most of opportunities.”

To judge and act decisively – and at speed – will require leaders to have an understanding of what digital can do and how it works, so their business decisions are informed by what’s possible. “Technology should be an integral part of the whole C-suite’s jobs,” says Tse, “rather than merely being delegated to the CTO. The thinking must be both business and technology – there shouldn’t be a distinction anymore.”

Most of what a typical leader does is make decisions, with the big decisions that impact strategy, people, and organization the most critical. “AI is going to give us many more ingredients to make bold decisions,” says Jennsen Fung. “But the gatekeeper still has to be human.”

AI provides the opportunity to improve the quality of these decisions, by drawing on a bigger, broader dataset than is possible for the human. This helps make sure too much weight isn’t given to personal preferences that may be discriminatory. “Considering people’s unconscious bias, decision-making can be vastly improved if you allow computers to do the initial analysis of all the relevant data sources,” says Erik de Haan, Director of the Ashridge Centre for Coaching, part of Hult EF Corporate Education.

Nevertheless, de Haan believes the actual taking of the decision is still a highly sensitive, emotive, and ethical action that has a lot of consequences and so must still be taken by a person. “The best approach is for a human to take the information and analysis provided by AI and make the decision themselves,” he says. “AI can replicate the calculating mind, but it is not able to replicate the doubting, feeling, ethical or caring minds, which are essential in decision-making.”

Once the decision is made, AI super-leaders bring their people together behind it by engaging rather than instructing. Effective communication is core to this, allied with an understanding of what motivates people.

AI can perform a useful function in testing the waters of what teams want and need. “AI has the capability to help you establish what points need addressing through passively scanning online communications, whether that’s emails, social media, or chat activity,” says David Bish, Head of Academic Affairs at Hult EF Corporate Education. “This can help spot critical information or communication gaps.”

In fact, AI is now being used to perform many kinds of communication – online chat, email and document drafting, to name but a few. And to a certain degree, generative AI, such as ChatGPT, has the ability to emulate the communication style of a human, through choice of tone or language according to the emotion it is told to portray. But given the importance of communication in business, leaders need to exercise careful judgement in deciding when and where to use it.

Communications that are crucial to the success of a team arguably need a high proportion of human input, if not being entirely generated by the human. “When a leader needs to communicate about a change that is sensitive or controversial, the choice of language and demonstration of empathy are crucial – and depend on a deep understanding of the situation and the concerns of the audience,” says Bish. “Communication needs to operate at an emotional level. The human must always make the final decision in any communication.”

In what is an increasingly uncertain world, leaders need to be able to create a feeling of confidence and excitement for their people. For Riaz Shah, EY’s Global Learning Leader and member of the Adjunct Faculty at Hult International Business School, the ability to lead in uncertain times is what leadership is all about. “Being able to set an exciting vision for people, and work with people to develop a plan, along with the view of how what you’re doing fits into the overall why – all of those qualities haven’t changed, and they won't change going forward.”

Function leaders must ensure AI serves the needs of the business. At the same time, HR and learning leaders must support their people in becoming more adaptable and having a learning mindset. “Critical thinking, emotional intelligence, and creativity are the skills that will enable humans to compete,” says Shah. “How do you increase these skills in your organization and become more of a learn-it-all culture rather than a know-it-all culture? I think it’s down to HR and learning leaders to excite people to want to learn new skills.”

“Leaders ought to provide certainty to people where there is none,” continues Shah. “We don’t like uncertainty – we like to have the answers, but we increasingly won’t have them. I always say you don’t need to have the answers, what you need is to ask the right questions. The smartest people are always revising their understanding of something they thought they’d already solved. And they’re open to contradictions to their way of thinking,” he says.

“So, I think the job of a leader is to give everybody a vision and be clear on the why. But also make people realize that these are times of uncertainty and, although we don’t know what the answers are, that’s okay because we can ask more questions. And that’s a great way of getting more innovation and creativity in our businesses,” concludes Shah.

In the field of language learning, we will investigate how AI will shift the focus from native vs. non-native teachers to human vs non-human teachers, and what the implications of that might be. We will dive into how AI can replicate the calculating mind but less the doubting, feeling mind – and how coaching can greatly help with the latter. And, finally, we will explore how leadership development will evolve to support the needs of the AI super-leader.