Finland’s persistent productivity challenge could be addressed through the skillful use of AI, making successful leadership in the AI transformation particularly important.
“Artificial intelligence will not solve companies’ problems or generate measurable benefits if expectations related to it are unrealistic or if the organisation is not ready or willing to change. That’s why a leader’s example, leadership style, and change management practices are especially crucial when introducing new technologies,” says Professor Paavo Ritala.
In their study Leading AI Transformation: Three Approaches for CEOs, Industry Professor Mika Ruokonen and Professor Ritala identify three leadership styles for advancing AI transformation: generalist, expert, and disruptive. Based on their findings, Ritala and Ruokonen describe the strengths and weaknesses associated with each leadership type as well as typical leadership practices. The study is based on interviews with CEOs and managing directors from companies of different sizes and industries.
“Knowledge work will change significantly as AI is adopted, and leadership must also change and keep pace with the times. Good leadership is not going away, but the toolkit of leadership may evolve. Leaders should continuously assess how well their leadership style aligns with the operating environment,” Ruokonen says.
Ruokonen notes that competition may emerge from unexpected directions.
“I would advise leaders to read, investigate, gather information, and learn from others. It’s worth reviewing a company’s most common processes and carefully selecting the actions through which technology can be integrated into work to create impact, performance, and renewal. For example, what recurring routines within the organisation could be targeted with AI investments?”
“Working hours freed up for growth initiatives”
As the amount of data increases, transparency also increases. According to Ruokonen, leaders should be careful about how they use data and transparency in management – for example, in measuring and monitoring work performance. Thanks to AI, people are becoming more capable, and the use of technology frees up working hours for new purposes.
“This is a matter of organisational culture and trust. The systematic use of AI requires trust when shared data is being utilised,” Ritala says.
What happens to the time freed up at work is an essential question. Will people take time off or will they come up with other activities that may not support the company’s objectives?
“Instead of excessive micromanagement, it makes more sense to lead people’s passion and energy. Could we do things that we previously didn’t have time for, should we invest the freed-up time in growth initiatives by developing something entirely new, or could we serve customers even better in the future? There are many leadership-related elements here. If a leader doesn’t actively manage the benefits, those benefits can effectively disappear,” Ruokonen explains.
“Finland’s persistent productivity problem may partly be addressed with the help of AI. Time-based measurement of work is outdated, so we need new metrics,” Ritala concludes.
Leadership styles in AI transformation
Generalist
- Recognises AI’s potential and role among the company’s various priorities but does not make a major personal investment in AI.
- Delegates, trusts teams, removes obstacles, and creates an environment for experimentation and expert-led implementation of AI initiatives.
- Biggest pitfalls: the AI agenda is driven by others rather than the leader, leading to inconsistency between the company’s strategic goals and AI development.
- Key decisions – do this: define the strategic direction, resource allocation, and team roles for AI initiatives; integrate AI transformation into the core business; ensure the right strategic technology and partnership decisions.
Expert
- Deep personal commitment and AI expertise, with a willingness to invest significant time and effort in the AI transformation.
- Leads by example, deepens AI knowledge, and actively participates in both operational and strategic AI transformation.
- Biggest pitfalls: consuming resources and energy with AI initiatives at the expense of other strategic priorities; declining motivation among other leaders and experts if they do not share the leader’s AI vision.
- Key decisions – do this: define the scope and pace of the AI transformation; create a plan to develop AI capabilities throughout the entire organisation; renew leadership practices to support the chosen AI agenda.
Disruptive
- Prioritises AI as the number one focus due to its acute business-critical role.
- Makes bold moves and challenges and breaks industry norms and repositions the company both internally and externally.
- Biggest pitfalls: overinvestment in AI may lead to poor choices and hard-to-reverse commitments; overselling or “hyping” AI to internal and external stakeholders can result in fatigue, resistance, or unrealistic expectations.
- Key decisions – do this: allocate resources to pioneering AI projects; design and implement new business models; define risk tolerance levels; ensure collaboration between traditional and AI-centric teams.
This article draws on the research article Leading AI Transformation: Three Approaches for CEOs and an episode of LUT Business School’s Myyntiradio Academic podcast episode titled Artificial Intelligence Is Transforming Business: How Will Sales Be Done in the Future? (in Finnish only).
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