Beyond Digital Transformation: Strategic Leadership for AI Implementation
Thierry Warin
Digital, Data, and Design (D^3) Institute at Harvard - Data Science for International Business (HEC Montreal) - Harvard Business Analytics Program
Thierry Warin, PhD
As artificial intelligence (AI) becomes a cornerstone of modern business operations, companies face critical decisions on how to implement AI solutions effectively. While AI is often viewed through the lens of digital transformation (DT), limiting its scope to that framework can lead to misunderstandings about its potential. Implementing AI is not merely an IT upgrade or a sophisticated automation project; it requires a more nuanced approach that integrates AI deeply into both the business and technological dimensions of an organization. Therefore, the question arises: who should be driving AI initiatives within a company?
Moving Beyond Digital Transformation
Digital transformation projects often center on upgrading legacy systems, streamlining processes, or automating routine tasks. Although AI plays a role in these efforts, treating AI implementation as just another DT project can obscure its true potential. Two common misconceptions arise from this narrow focus:
The Strategic Role of AI: More Than Automation
To harness AI’s full potential, companies must go beyond viewing it as a tool for automation. Implementing AI involves understanding the complexity of various AI paradigms—predictive, explanatory, and generative—and how they interact with the broader business environment. This is not a task to be managed like any other project; it must be treated as a fundamental change to the company’s "hardware" (i.e., its processes and structures) and "software" (i.e., its technologies and capabilities). In my view, the success of AI projects depends on the C-suite's direct involvement. Executives must understand AI’s strategic importance and commit to reshaping the company's operations to integrate AI effectively. Without this high-level buy-in, AI initiatives risk becoming siloed, underfunded, and ultimately unsuccessful. Research shows that 85% of AI projects fail, often due to insufficient executive engagement (McKinsey, 2023).
The Pitfall of Delegating AI Decisions to Domain Experts
Many organizations involve domain experts early in the AI process, often by asking them what AI can do to improve their workflows. While this approach has good intentions—gaining buy-in from those directly impacted—it can lead to suboptimal outcomes. Domain experts may frame their requests around familiar solutions, usually focused on task automation. This tendency stems from their desire to maintain control over the transformation and focus on easing their immediate workload (Warin & Jablokov, 2024).
However, AI's power lies in its ability to solve broader, more complex problems. The conversation should not be about implementing predefined solutions but rather about clarifying the strategic objectives the company seeks to achieve. AI engineers must be empowered to design solutions based on these objectives, leveraging AI’s ability to transform processes in ways that domain experts may not anticipate.
Reframing the Problem: From Solution-Oriented to Objective-Oriented
A successful AI strategy begins with a deep understanding of business objectives rather than focusing on specific technological solutions. For instance, instead of asking how AI can automate a particular task, the focus should be on identifying the larger business goal—whether that’s improving customer engagement, reducing operational costs, or enhancing decision-making capabilities. From there, AI engineers can determine how to employ the right tools—whether predictive, explanatory, or generative AI models—to achieve those objectives (Jablokov & Warin, 2022).
This shift in perspective not only helps prevent the common pitfall of limiting AI to automation but also opens up new avenues for innovation. AI can uncover insights and identify opportunities that would otherwise remain hidden, fostering a more adaptive and forward-looking organization.
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Involving the Right Stakeholders: A Multi-Level Approach
To navigate the complexity of AI implementation, companies must ensure the involvement of key stakeholders at various levels of the organization. These include:
Conclusion: AI Requires a New Strategic Mindset
Implementing AI solutions in a company requires a strategic reorientation that goes beyond digital transformation. It is not a standard IT upgrade, nor is it solely about automation. AI is a transformative force that requires involvement from the highest levels of the organization, as well as a deep understanding of the business objectives it is meant to serve. By framing the problem correctly—focusing on strategic goals rather than predefined solutions—companies can unlock the full potential of AI and drive significant value across their operations (Pratt et al., 2023).
References
McKinsey. (2023). "The State of AI in 2023." McKinsey Global Survey Results.
Teece, D. J. (1997). "Dynamic Capabilities and Strategic Management." Strategic Management Journal, 18(7), 509-533.
Nonaka, I., & Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press.
Warin, T., & Jablokov, I. (2024). "From Debriefing Management to Briefing Management: Pioneering Future-Oriented Strategies in the Digital Age." California Management Review Insights.
Jablokov, I., & Warin, T. (2022). “How Augmented Intelligence is Bringing the Focus Back on the Human.” California Management Review Insights.
PRATT, Lorien, BISSON, Christophe, WARIN, Thierry; ? Bringing advanced technology to strategic decision-making: The Decision Intelligence/Data Science (DI/DS) Integration framework ?, Futures, vol.?152, 2023, p.?1-11. https://doi.org/10.1016/j.futures.2023.103217
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