When AI Disruption Never Ends
AI has turned disruption into a permanent condition. Leaders must strategically manage the organizational fatigue that follows.
Phil Bliss/theispot.com
AI has turned disruption into a permanent condition, with technologies changing too fast for employees to keep up. Traditional change management was built for disruptions that end, but AI capabilities keep arriving in accelerating waves. Companies can manage this steady-state disruption through three organizational practices that shift the pressure from individual employees to the organization itself: permanent AI infrastructure, split cadences, and embedded learning.
A vice president of product opens her laptop on a Monday morning to find that the AI model her team had worked with for the past six weeks to build a customer workflow has been leapfrogged by a cheaper, faster alternative. Again. Her Slack feed is blowing up with links to the announcement. The CEO has already forwarded an article about what a competitor is doing with the new tool, with the subject line “FYI.” She hasn’t finished rolling out the last integration, and now she’s wondering whether to scrap it. She is not resistant to AI. She is worn out by it.
Most leaders look at this picture and see an execution problem: The organization wasn’t moving fast enough. The cautionary tale that reinforces that instinct is Chegg, the education company whose market capitalization collapsed when the launch of AI-powered alternatives rendered its core tutoring model obsolete. The lesson everyone has drawn is obvious: Move fast or die. So leaders push harder, with more pilots, more mandates, and a constant drumbeat of urgency.
But that lesson, taken too literally, backfires. Bracing only against the danger of moving too slowly, leaders managing AI adoption underestimate a quieter risk: that they will wear out their organizations by racing toward a finish line that does not exist. The old playbook was built for disruptions that end, and its instincts (move faster, push harder, wait for things to settle) become liabilities when there is no end state. Leaders who optimize for speed alone will lose to those who build for endurance as well.
What follows is a reframing and a set of emerging practices for leading through an AI disruption that will not settle.
From Process to Permanent Condition
Research on disruption has been circling this problem for years. One influential strand that one of us (Rory) developed with Clayton Christensen and Michael Raynor pressed on the point that disruption is a process, not an event. The recurring incumbent error is to judge the threat by where it stands rather than where it is heading. Yet, even correcting for this carries a quiet assumption that the threat’s trajectory has an ultimate destination. After Netflix disrupted Blockbuster, streaming became the new normal. Each wave of new technology ran turbulently for a while and then hardened into arrangements a company could see and plan around.
