The disconnect between AI’s transformative potential and the actual scale of implementation represents one of today’s most significant organizational challenges. In their new article for the Harvard Business Review, “A Guide to Building Change Resilience in the Age of AI,” Karim Lakhani, Dorothy and Michael Hintze Professor of Business Administration at Harvard Business School and faculty chair and co-founder of the Digital Data Design (D^3) Institute at Harvard, Jen Stave
Jen Stave
, executive director of the Digital Data Design (D^3) Institute at Harvard, Douglas Ng
Douglas Ng
, Director of Design at the Digital Data Design (D^3) Institute at Harvard, and Daniel Martines, managing director at BCG X, argue that this mismatch arises from structural issues and propose change resilience as a systematic approach to building the organizational capabilities necessary for AI success.
Key Insight: The Missing Ingredient
“The primary obstacle is the ability of companies to adapt, reinvent, and scale new ways of working. We call this change resilience.” [1]
In the fast-paced business environment created by AI, leaders are no longer able to apply traditional operating models to episodic development cycles. Previously, as Lakhani and his co-authors suggest, “You modernized your systems, trained your people, and operated in a stable environment until the next wave of disruption hit.” [2] However, if your old approach is falling short in today’s environment and you’re feeling left behind, you aren’t alone: the results of a BCG survey discussed in the article report that “just 26% of organizations have achieved value from AI.” [3] Responding to both the challenges and opportunities AI presents, the authors call for a fundamental shift: companies must move beyond simply managing AI-driven change and instead embed AI as a core organizational competency through the continuous and comprehensive strategy of “change resilience.”
Key Insight: The Mindset
Sensing – Rewiring – Lock-In
Change resilience, according to the authors, is made up of three ‘muscles’ working in concert to create a sustainable AI ecosystem. Sensing enables organizations “to pick up weak technological, competitive, or societal signals early.” Rewiring is “the capacity to redeploy talent, data, capital, and decision rights in days or weeks, not fiscal quarters.” Lock-In is “the discipline to codify what a team learns (in process, code, or policy) so the next initiative starts from a higher baseline instead of reinventing the wheel.” [3] The authors describe Shopify as a company that exemplifies these characteristics, as it constantly evolves rather than adding AI to old systems. As one example, in 2023, Shopify spun off its logistics arm to concentrate on product innovation, enabling rapid development of AI-native tools like Sidekick for entrepreneurs.
Key Insight: The Playbook
Learn – Do – Imagine – Act – Care
Lakhani and his co-authors break down change resilience into five components: Learn, Do, Imagine, Act, and Care. Learning involves widespread AI experimentation to shift attitudes, empower employees, and discover opportunities to take advantage of AI. Doing targets deficiencies with fast-paced AI initiatives. Imagining puts your entire organization up for discussion, challenging you to invent new operating models instead of duck-taping existing ones. Acting makes these cycles continuous in order to establish change resilience as a foundational strategy rather than a one-off solution. Finally, Caring emphasizes wellbeing measures to ensure that employees feel supported and avoid burnout. The article discusses Accenture, Singapore-based DBS Bank, Moderna, P&G, and Cisco as already leading the pack by incorporating these elements into their strategy and operations.
Why This Matters
For executives and business professionals, developing change resilience represents a crucial strategic priority for competing effectively in the AI era. By focusing on the three muscles and five-steps, leaders can position their companies to leverage AI and adapt to future technological advances. The companies already achieving breakthrough AI results share a common strategy: they invest in their organization’s capacity to change as aggressively as they invest in AI technology itself.
If you’re wondering how change resilient your organization is, “A Guide to Building Change Resilience in the Age of AI” also includes a set of questions that can act as a litmus test.
References
[1] Karim Lakhani et al., “A Guide to Building Change Resilience in the Age of AI,” Harvard Business Review, July 29, 2025, https://hbr.org/2025/07/a-guide-to-building-change-resilience-in-the-age-of-ai.
[2] Lakhani et al., “A Guide to Building Change Resilience in the Age of AI.”
[3] Lakhani et al., “A Guide to Building Change Resilience in the Age of AI.”
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.
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.
Douglas Ng
Douglas Ng
is Director of Design of the Digital Data Design (D^3) Institute at Harvard. As a digital strategist, technology educator, and innovation researcher, he specializes in AI transformation and translates the institute’s research for industry leaders.
Daniel Martines is Managing Director with BCG X, where he specializes in Generative AI, AI platform engineering, and data management.