Will Drover's Research Reframes AI Disruption Around Organizational Endurance
Artificial intelligence is changing so quickly that the traditional playbook for technological disruption is starting to break down. Rather than planning for a new normal to emerge, business leaders need to design organizations built to operate through continuous change.
That is the argument Will Drover of the TCU Neeley School of Business and Rory McDonald of the University of Virginia’s Darden School of Business make in a new article published in MIT Sloan Management Review. Drover is professor and chair of Neeley’s Entrepreneurship and Innovation Department, as well as founding director of Neeley AI Forward, a schoolwide AI initiative. McDonald is widely known for his work on disruptive innovation, including collaborations with Clayton Christensen and Michael Raynor that have helped shape how leaders navigate disruption.
The authors describe the emerging environment as “steady-state disruption,” a condition in which advances in AI are continuous and build on one another, leaving organizations without the periods of relative stability that traditionally followed major technological shifts.
Unlike earlier technological shifts that eventually established a new normal, AI capabilities continue to advance while the time between significant changes shrinks, leaving no settled position to plan toward.
For business leaders, the distinction has significant implications The old playbook was built for disruptions that end, so its instincts, move faster and wait things out, stop working when nothing settles. Treating continuous AI change as a series of individual transitions can leave employees carrying much of the burden of keeping up, increasing fatigue and burnout as new tools, capabilities and expectations continue to arrive.
As Drover and McDonald explain in the article, “Leaders who optimize for speed alone will lose to those who also build for endurance.”
Building Organizations for Continuous Change
The article shifts the AI conversation to a new challenge. Companies must both adapt
quickly and build organizations capable of sustaining that pace.
Drover and McDonald point to research showing that AI can intensify workloads and cognitive demands even as it eliminates some routine work. The authors identify three emerging practices that can move some of that burden from individuals to the organization itself.
First, companies can establish permanent AI units responsible for tracking developments, evaluating opportunities and coordinating implementation. Rather than relying on temporary committees whose members add AI responsibilities to existing jobs, organizations can make managing AI change an ongoing function.
Neeley AI Forward operates on this model. It runs through five standing pillars covering students, faculty, research, operations and infrastructure, and industry engagement, giving the school a permanent structure for evaluating AI developments and coordinating how they are put into practice.
Second, organizations can operate on two different clocks. One allows teams to experiment quickly with emerging AI capabilities, and the other protects the deliberate work required to build reliable infrastructure and long-term systems. Airtable applied this approach by dividing its product organization between fast-moving AI development and longer-term infrastructure work.
Third, companies can make AI learning part of employees’ regular work rather than relying primarily on periodic training or expecting them to keep up on their own time. Continuous, role-specific development can help employees build skills as the technology evolves. Drover and McDonald highlight Salesforce as an example, where tailored learning opportunities are integrated into employees’ regular workflow.
Together, the practices shift some of the burden of keeping pace as AI keeps changing onto the organization rather than leaving employees to absorb each new development on their own.
From AI Users to AI Builders
The MIT Sloan Management Review article connects with another AI challenge Drover addressed in an August commentary for Fortune. That piece examined the gap between using AI and building with it.
In the article, titled “Ask your employees one question about AI. The silence will tell you everything,” Drover described the “builder activation gap.” The term describes the gap between employees capable of building AI-powered assistants, agents and workflows and those actually doing it. He noted that one-time AI assistance can create productivity gains, while reusable tools and workflows can create value that scales.
The Fortune article grew partly from Drover’s experience teaching applied AI in Neeley MBA and executive education programs and featured Neeley MBA alumna Caroline Davis ‘25, now Chief of Staff at Capital Factory, as a compelling example of the shift from AI user to builder.
Drover recommended incorporating employees’ first AI builds into training, recognizing successful builders and measuring employee-created AI tools rather than focusing only on AI adoption rates.
Across both publications, a larger challenge emerges for business leaders. Organizations need employees prepared to experiment and build with AI. They also need structures that make that level of adaptation sustainable in the face of steady-state disruption.
As AI capabilities continue to change, the two publications suggest that organizations will need more than speed. They will need to build the structures, rhythms and learning systems that make continual adaptation possible over the long run.