How companies can overcome the structural frictions that block AI at scale
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AI has entered the enterprise faster than most previous waves of technology, reshaping expectations about speed, productivity, and decision-making. Yet adoption alone does not produce transformation. The Frontier Firm Initiative (FFI), a joint effort between the Digital Data Design Institute at Harvard (D^3) and Microsoft, recently convened senior leaders from a dozen global organizations to address the “last mile” challenge, when a company tries to scale localized, successful AI pilot programs into a standard, enterprise-wide operating model. In the new HBR article “The ‘Last Mile’ Problem Slowing AI Transformation,” Karim R. Lakhani and Jen Stave of D^3 and Microsoft’s Jared Spataro identify a framework of the specific “frictions” stalling progress and outline a strategic blueprint to overcome them. In the insight below, we will zoom in on one friction and one corresponding recommendation from the blueprint to resolve it.
Key Insight: The Weight of What Already Exists
“[T]he primary obstacle to progress is rarely model quality or data availability, but rather the ‘last mile’ of transformation where technical capability must meet organizational design.” [1]
When organizations try to implement AI, they often assume that any issues will arise from the AI technology itself. However, the authors find that AI actually functions as a “diagnostic tool” that exposes problematic processes already present within a firm. For example, the authors label “process debt” as the accumulation of fragmented and inconsistent workflows built up over years of embeddedness and geographic specificity. The article cites, for example, one professional-services firm operating in more than 170 countries where the “same” process had dozens of different, regional variations.
Key Insight: Designing the Organization AI Deserves
“[F]or every bottleneck discovered in the ‘last mile,’ a corresponding shift in this blueprint provides the path forward.” [2]
One of the shifts by organizations making real headway is replacing legacy processes. For example, what the authors call “clean-sheet redesign” starts by asking whether a given workflow would exist at all if the company were built today around AI agents. This demands equally fresh thinking about people and governance: capturing expert judgment as a codified asset rather than a protected credential, redesigning roles toward oversight and interpretation rather than execution, and managing AI agents with the same accountability structures applied to human teams.
Why This Matters
For today’s business professionals and executives, the “last mile” is less a technical challenge and more a test of leadership imagination. Process debt and clean-sheet redesign are only two parts of a broader diagnosis of seven frictions and corresponding transformation strategies. Read the full article to see them all. The potential of the technology you have already purchased is immense, but realizing it requires the courage to redesign the organization to match the speed of an agentic world.
References
[1] Lakhani, Karim R., Jared Spataro, and Jen Stave, “The ‘Last Mile’ Problem Slowing AI Transformation,” Harvard Business Review, March 9, 2026, https://hbr.org/2026/03/the-last-mile-problem-slowing-ai-transformation.
[2] Lakhani et al., “The ‘Last Mile’ Problem Slowing AI Transformation.”
Meet the Authors
Karim R. Lakhani is the Dorothy & Michael Hintze Professor of Business Administration at Harvard Business School. He specializes in technology management, innovation, digital transformation, and artificial intelligence. He is also the Co-Founder and Faculty Chair of the Digital Data Design (D^3) Institute at Harvard and the Founder and Co-Director of the Laboratory for Innovation Science at Harvard.
Jared Spataro is Chief Marketing Officer, AI at Work, Microsoft.
Jen Stave
Jen Stave
is Executive Director of the Digital Data Design (D^3) Institute at Harvard. She was previously Senior Vice President at Wells Fargo, and has a PhD from American University.