Co-authored with Beejan Giga
A century ago, progress moved in cycles. Today, it moves in sprints.
getty
Every generation of managers thinks they’re living through the biggest transformation. This one might actually be right.
Technological advancement is not new. Businesses have been adapting to new tools and systems since the Industrial Revolution. What’s new is the rate of change. A century ago, progress moved in cycles. Today, it moves in sprints. The pace at which companies are expected to adopt, adapt, and perform has accelerated dramatically, and it’s reshaping how organizations need to operate.
When Speed Outruns Stability
Today’s managers generally understand the logic behind the information systems they use. They can articulate the technology’s basic purpose and often see the potential in its promises. But when technology rapidly changes, the pace of innovation can leave managers with little time to stabilize the underlying processes. When systems change faster than the change can be absorbed and assimilated, organizations struggle to adapt their ways of working before the next wave of change arrives.
Artificial intelligence is a prime example. Every month, new tools redefine how work is performed, but until people develop consistent patterns and processes around those tools, they can create as much rework as they gain in efficiency. Technology that doesn’t “resettle” into the operating rhythm of the business rarely delivers its intended benefit.
In heavy industry, you can actually feel the rework. The leaders of a transformer manufacturer recently added a clever capacity model. It seemed like a big step forward. Then, they pushed three tweaks in the first month. Supervisors stopped trusting the sequence, so they staged parts “just in case.” Inventory buffers ballooned, and actual lead times worsened. When our firm was brought in to help, we didn’t replace the software; we slowed the change window, proved the model on a narrow product family, and let wins compound and confidence build.
Adoption is a Behavioral Process
Change management, at its core, is human. There is a common belief that people resist technology because they are afraid of the change it brings. In reality, many people resist technology because they haven’t yet seen its value. If you roll out a new project-management tool and no one else on your team uses it, or you can’t see how it makes your work easier, you’re more likely to revert back to how you did it before. Technological adoption takes root when people experience firsthand how a tool improves their work or life.
Prototyping and pilot programs provide focused onboarding and visible early wins. These are essential because they create a peer group that champions the change as well as the psychological safety that encourages others to follow. Simply explaining the benefits isn’t enough. Behaviors change when people are required to try something new, and when the results make their work better. The benefit reinforces the behavior far more powerfully than the training ever could.
When Knowledge Moves into the Black Box
As systems become more intelligent, the organization itself risks becoming less intelligent. In the past, managers had a better understanding of the logic behind scheduling work through a process because they were part of building it. But with modern ERP, CRM, and AI-driven systems, that institutional knowledge is often embedded in a “black box.” The system uses algorithms to make the decisions. Over time, fewer people understand why those decisions are being made.
That loss of understanding has real consequences. When the underlying planning parameters become inaccurate, performance erodes. When managers stop trusting system outputs, they override them based on instinct. It’s not the technology that has failed; it’s the human connection to it.
Bridging the Gap Between Humans and Systems
As AI matures, the challenge will intensify. Many of the traditional learning environments used to develop managerial intuition—entry-level analysis, scheduling, problem-solving, and data validation—are at risk of being automated away. Those early experiences are where future leaders learn how systems work, and why they sometimes don’t.
If that developmental stage disappears, organizations could face a growing knowledge gap in understanding how their own technology and their operations function as a whole. That doesn’t mean AI offers more threat than opportunity. It means we need to be intentional about how it’s introduced, managed, and taught.
From Output to Understanding
Over the last few decades, technology has undoubtedly increased productivity, but not always in the ways we expect. The real gains have come less from cost reduction and more from output expansion. Tools like Excel didn’t make work cheaper; they made it faster, broader, and more capable. The same will likely hold true for AI. The winners won’t be those who adopt the most tools, but those who understand how to harness them by embedding their benefits into the habits, rhythms, and decision-making of everyday management.
Because progress doesn’t come from speed alone, it comes from learning how to make speed sustainable.
