Middle managers are key catalysts of AI change

Organisations rolling out AI should give their middle management an important role in the implementation process, according to new research from BearingPoint. As an essential link between the strategic and operational objectives of the business, middle managers can provide a priceless bridge between theory and practice during AI’s roll-out process.

“When organisations invest in their middle management, they build a culture in which AI is embraced,” explains David Bergsma, a partner at BearingPoint. “Middle managers are key drivers of AI change and new ways of working.”

According to him, middle managers have been crucial to realising change programmes for decades. While the top of an organisation determines the strategic course, workers on the work floor actually put the new way of working into practice – but the middle management of a company straddles both worlds, and can ensure that the two work in syncronicity.

New research from BearingPoint has flagged up the fact this is no different when implementing AI.

“We see that organisations that invest in the development of middle managers are able to apply AI much more effectively,” says Bergsma. “It is not just about technical knowledge, but especially about trust, leadership and the ability to involve people in change.”

These are the themes in which middle managers play a crucial role. For example, many people fear the possible impact of AI on work and their jobs – so winning their trust is important to succeeding in an AI implementation. Middle managers have better insight than anyone else into the needs of talent, and help to provide the necessary training and reassurances within teams to offset such fears.

“AI changes existing functions and creates new roles,” Bergsma continues. “Middle managers can involve people in the rationale behind change and, where necessary, also reassure people. By communicating openly about what AI can – and cannot – do, they create trust and understanding within teams.”

Middle managment in addition play an important role in monitoring the use of AI, and considering the steps that are needed to maximise the adoption of AI tools. This also applies to translating AI insights into decision-making and policy, for example in the area of ​​investments and innovations in processes.

BaringPoint’s research meanwhile also found that only 35% of organisations that implement AI have a structured change program. “That is a missed opportunity. Without a clear strategy and support, the opportunities of AI remain untapped and the chance of resistance increases,” Bergsma concludes.