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Introduction
Artificial intelligence (AI) rapidly transforms how businesses operate and deliver value (Davenport & Ronanki, 2018). As intelligent technologies continue advancing at an unprecedented pace, there is a growing imperative for leaders to guide their organisations through the coming AI revolution. Leadership in the age of AI calls for new thinking frameworks that leverage these emerging technologies not just for narrow tasks but to reimagine entire business models, processes and worker roles centred around data harnessing (Rao & Verweij, 2017).
Leaders Must Get Acquainted with Intelligent Technologies
Successfully leveraging AI requires leaders who deeply understand intelligent technologies and recognise the business challenges these innovations can address (Wilson & Daugherty, 2018). As AI matures, leaders must gain hands-on experience through pilot projects and specialised training to develop an intuitive grasp of AI’s capabilities and limitations across different functional domains (Daugherty & Wilson, 2018). With this techno-business acumen, forward-thinking leaders can spearhead cultural change initiatives to foster greater experimentation and continuous learning using AI within their organisations (West & Allen, 2018).
Build a Learning Culture
Cultivating “active learners” who continuously seek out problems to solve using AI is pivotal, as these skill sets will drive competitive differentiation (Brynjolfsson & McAfee, 2017, p. 197). Leaders should thus prioritise upskilling employees through immersive education programs, cross-departmental collaboration using AI, and promoting greater data literacy across the enterprise (Wilson & Daugherty, 2018). Building this learning culture lays critical groundwork, enabling organisations to absorb rapid advancements in AI and integrate enhancements effectively.
Transparent AI Governance Frameworks
As intelligent systems grow more autonomous, leaders must retain high-level oversight through key performance indicators tied directly to strategic goals, balanced against audits for algorithmic biases (Agrawal et al., 2018). Transparent AI governance frameworks outlining accountability across human and machine decision-makers are indispensable for earning trust in AI solutions over the long term (Rao & Verweij, 2017). Leaders must model comfort with allowing AI to guide significant decisions once accuracy benchmarks are satisfied.
Break Down Data Silos
The sheer volume of quality data needed to develop robust AI solutions poses a challenge, compelling collaborations across internal business units and external partnerships (Brynjolfsson & McAfee, 2017). Leaders play a crucial role in breaking down data silos and aligning stakeholders to build shared data infrastructure as an enterprise asset (Ransbotham et al., 2017). Strategic investments in cloud computing, IoT sensors and big data analytics talent help establish the data-rich feedback loops AI systems need to learn dynamically (Agrawal et al., 2018).
Leaders Must Recognise AI's Current limitations
As AI capabilities grow more pervasive, virtually every profession will integrate intelligent automation to various degrees. Leaders must recognise AI’s current limitations in handling creative, complex or interpersonal tasks and thoughtfully implement hybrid human-AI teams that play to the strengths of both (Davenport & Ronanki, 2018). Labour market transitions will inevitably still occur, requiring leaders’ stewardship of displacement and retraining programs to allay workers’ anxieties about embracing automation (Brynjolfsson & McAfee, 2017).
Leaders Should Articulate a Vision for AI in Business
While AI integration poses many challenges, leaders who take decisive steps to understand, strategically implement, and culturally embrace intelligent technologies position their organisations for transformative growth in the coming decades. The AI revolution calls for leaders to articulate a vision for AI in business, actively upskill workforces, foster continuous learning cultures and implement transparent AI governance. Leadership in the age of AI demands new acumen, alliances and attitudes attuned to the coming machine age.
Reference
Agrawal, A., Gans, J. and Goldfarb, A. (2018). Prediction machines: the simple economics of artificial intelligence. Boston: Harvard Business Review Press, pp.27-73.
Brynjolfsson, E. and McAfee, A. (2017). The business of artificial intelligence. 1st ed. [ebook] Harvard Business Review Press. Available at: https://books.google.co.uk [Accessed 17 Dec. 2023].
Daugherty, P. and Wilson, H.J. (2018). Human + machine: reimagining work in the age of AI. [Place of publication not identified]: Harvard Business Press.
Davenport, T.H. and Ronanki, R. (2018). Artificial Intelligence for the Real World. [online] Harvard Business Review. Available at: https://hbr.org/2018/01/artificial-intelligence-for-the-real-world [Accessed 17 Dec. 2023].
Ransbotham, S., Kiron, D., Gerbert, P. and Reeves, M. (2017). Reshaping Business With Artificial Intelligence. [online] MIT Sloan Management Review. Available at: https://sloanreview.mit.edu/projects/reshaping-business-with-artificial-intelligence/ [Accessed 17 Dec. 2023].
Rao, A.S. and Verweij, G. (2017). Sizing the prize: what’s the real value of AI for your business and how can you capitalise?. [online] PwC. Available at: https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html [Accessed 17 Dec. 2023].
West, D.M. and Allen, J.R. (2018). How artificial intelligence is transforming the world. [online] Brookings. Available at: https://www.brookings.edu/research/how-artificial-intelligence-is-transforming-the-world/ [Accessed 17 Dec. 2023].
Wilson, H.J. and Daugherty, P.R. (2018). Collaborative Intelligence: Humans and AI Are Joining Forces. [online] Harvard Business Review. Available at: https://hbr.org/2018/07/collaborative-intelligence-humans-and-ai-are-joining-forces [Accessed 17 Dec. 2023].
