Many leaders are too hands off when it comes to AI. It is time for them to lead, expert urges

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Top organizational leaders are failing the artificial intelligence test, according to technology expert and Northeastern University business school dean David De Cremer. Instead of becoming personally involved, learning the details and communicating a vision and purpose, they are leaving it to the tech experts.
“When it comes to AI-driven transformation, leaders are not leading,” he writes in The AI-Savvy Leader.
After all, the tool is technically complex. While top executives are being frantically warned they need to act now or fall behind it’s hard to know what action to take if you don’t have a handle on things. The result, Mr. De Cremer says, is deferential leaders. And floundering organizations because change this important must be led from the top.
Worse, organizations are primarily seeking financial benefits and optimized efficiency when bringing AI on board rather than viewing it as a powerful tool to augment – not replace – human intelligence and unlock more innovation and creativity in workers.
The perspective is narrow and reductive: Delegating decision-making and thinking to this new tool and reducing human employees to mainly being task completers. He calls this “tech-driving-tech” transformations and they are common.
Tech is seen as the solution. Tech experts are seen as the folks to make it happen. Top executives are sitting back, watching, when they should be learning as much as they can about the technology, sketching out the possibilities and leading the initiatives. “The secret to leading AI adoption successfully – besides deciding to do it in the first place – is to practise all the important leadership skills you’d apply anywhere else,” he says.
As with anything new, it starts with learning. “Get to learn AI, and learn to use it as a leader,” he urges. Leaders must close the gap between their understanding of AI and the growing use of it. He insists they don’t have to become AI experts. They just need to be AI-savvy enough to recognize the benefits for the organization and its stakeholders.
They need to learn what kind of AI is suitable to their business context and why. Then they need to empower and drive human-AI collaborations so performance is improved. “To achieve these goals, leaders need to identify opportunities for AI integration in the workflow of teams and anticipate its potential benefits for the different teams and the projects they have going on in the organization,” he writes.
Throughout, they must not succumb to the belief that AI is superior in intelligence to human workers. AI algorithms have great power, but he stresses AI cannot think like a human and falls short of truly being creative (let alone dealing with emotions).
“Creativity is more than simply generating novel ideas. Being creative also involves the ability to evaluate whether the newly generated idea is meaningful and will solve a problem that humans care about. AI does not have this ability, as it fails to understand which problems humans consider important to solve,” Mr. De Cremer writes.
AI-savvy leaders, he says, have to see the bridge between how the organization is working today and how it could be working in the future if AI is adapted. That’s a very practical way of saying leaders must be visionary. But they also must be supreme communicators, inspiring others to see the bridge and want to cross it. They must rally people to a common goal, showing how the organization will be better prepared to be competitive and successful.
“If AI adoption fails, it is usually not an issue of technology but rather a lack of vision from leadership,” he says.
He has found too often leaders can imagine an AI-enabled future but have a hard time seeing the bridge to get there, because of both a lack of technical understanding and the rapidly shifting nature of the technology. A second factor is they expect an immediate payback and are almost always disappointed when the technology falls short or when they can’t achieve the desired scale with the adopted AI solutions.
“To ensure that AI adoption succeeds across the entire organization, you cannot engage in opportunistic piecemeal approaches, where each project uses AI for its own narrow goals and benefits. Rather, you need to present a holistic vision that calls for collaboration from multiple cross-functional teams and departments,” he advises.
That might mean the IT department providing appropriate infrastructure; data engineers ensuring data access, analytics and management; HR maintaining motivation by providing training and explaining how jobs will change; and the risk management team helping with governance and investments. Meanwhile, throughout the organization, specific improvements are occurring.
He advises leaders that to demonstrate their own credibility they should start with the problem they’re solving, not the technology. One approach is to begin reviewing every repetitive and manual task, ranking them in order of effort or cost, and then looking for AI-based solutions to those problems. If possible, you should focus your AI investments on solving high-stress problems for which there are low-cost solutions that are easy to implement. At the same time, have an overall strategy in mind: The bridge the organization must collectively pass over.
It’s basic management – what organizational leaders know how to do. They should stop being deferential, afraid of the technology, and lead in a sensible and sensitive way.
Harvey Schachter is a Kingston-based writer specializing in management issues. He, along with Sheelagh Whittaker, former CEO of both EDS Canada and Cancom, are the authors of When Harvey Didn’t Meet Sheelagh: Emails on Leadership.