Designing AI to work well with human adaptability is core to increasing its support & success. As AI continues to revolutionize industries, it’s clear that successful change management has become a critical competency for organizations looking to capitalize on its transformational potential. Implementing AI isn’t just a matter of adding more technology to your team’s proverbial toolbox; it requires a new mindset and an understanding of both the technical and cultural shifts that accompany this transformation.
Through my work advising and working directly on AI initiatives for a number of industry leaders, I’ve seen first-hand how vital it is for organizations to embrace change thoughtfully to achieve sustainable success. “Move the movable” is a key mantra so innovations are introduced at a pace organizations are able to absorb. Here are a few lessons I’ve found invaluable to drive effective change management in organizations as they look to tap into the power of AI.
1. Establish a Clear Vision
For AI to truly take hold, leadership must develop and communicate a clear, compelling vision of what success looks like. Leaders who champion AI aren’t just paving the way for new technology—they’re aligning teams around shared goals that highlight AI’s potential impact. A strong vision provides an anchor that guides teams and builds momentum across departments. Change starts from the top, and organizations that empower their leaders to communicate and embody the potential of AI see far greater success in adoption than those who bank on organic adoption at the grassroots level.
2. Balance Innovation with Practicality
In AI-driven projects, there’s always a balance to strike between ambitious innovation and practical application. Take, for example, a project I’m working on with Google X’s team, Bellwether, which uses AI for predictive disaster risk and emergency management. The solution integrates multiple data sources for disaster response and strategic planning, but rolling out such advanced capabilities requires steady, incremental adjustments that respect existing infrastructures. It’s like upgrading a subway system—our aim is to enhance the existing infrastructure without disrupting existing service, innovating as we go while grounding our solutions in the reality of today’s operational environments.
3. Tailor AI Adoption to Different Readiness Levels
One challenge with AI adoption is recognizing that users vary widely in readiness and comfort with the technology. Some stakeholders are eager to dive into AI’s mechanics, while others seek intuitive solutions that provide value without complexity. This variation calls for a multi-faceted approach to change management, where tools and training are adaptable to different user needs. Ensuring that every level of user has a clear path forward not only accelerates AI adoption but also increases its value across the organization.
4. Identify and Overcome Organizational Barriers
AI brings a unique set of organizational barriers. Many teams are cautious due to risk concerns, job displacement worries, or existing process dependencies. To overcome these, organizations need to foster “pockets of readiness”—champions within the organization who are open to experimentation and can model AI’s benefits for their peers. Change management here resembles an influence campaign: we start with enthusiastic early adopters, gain incremental buy-in, and gradually scale support throughout the organization.
5. Listen Deeply and Build Empathy
A cornerstone of effective change management in AI is the ability to listen and empathize with those affected by the transition. It’s not enough to deliver a technology and expect immediate adoption. In my experience, understanding users’ everyday challenges and engaging with their feedback is essential. When leaders make the effort to “get into the trenches” and experience these pain points first-hand, the resulting solutions are better aligned to user needs. Creating a culture where feedback is welcomed and respected leads to stronger buy-in and more enduring change.
6. Embrace Flexibility with Strategic Commitment
AI’s rapid development requires a nuanced approach to flexibility and commitment. Companies need to remain agile to adapt as AI evolves, while committing enough resources to build foundational infrastructure. Organizations must weigh their options: do they maintain flexibility by waiting to see which AI applications prove valuable, or do they commit resources early to capture potential market advantages? This strategic balancing act is essential for organizations aiming to leverage AI as a differentiator while managing risk effectively.
7. Empower Teams to Embrace Innovation
Change management with AI doesn’t stop at leadership—it extends to every level of the organization. Frontline managers, in particular, play a vital role in guiding their teams to think differently, adopt creative approaches, and apply new tools. Managers today need to inspire a culture of exploration, where team members are encouraged to think beyond traditional practices and incorporate AI-driven solutions into their daily workflows. This shift requires a blend of structured innovation and an openness to experimentation—qualities that are essential for any organization aiming to stay competitive.
The Role of Change Management in AI Transformation
As we navigate this AI-driven landscape, it’s clear that change management is a foundational aspect of effective AI adoption. By creating a culture that listens, balancing vision with practicality, and empowering teams to innovate, organizations can unlock the full potential of AI. Integrating AI is no longer just a technical upgrade; it’s a strategic and cultural transformation that will determine the future trajectory of businesses. Embracing this transformation with a thoughtful change management strategy isn’t just beneficial—it’s essential.
About the Author
Andrew DeBerry is a Knownwell AI Advisory member, cybersecurity expert and co-founder of a Bay Area startup. With a background in engineering, public policy, and ethics from Notre Dame, he served in Air Force intelligence and cybersecurity operations across Korea, Germany, and Afghanistan. Andrew holds a Masters in Arabic and an MBA from Wharton, and has led AI initiatives at Microsoft, AWS, and Meta. He advises on cutting-edge projects at Google X, and US Cyber Command, driven by his mission to innovate for good.
