Employees say AI makes them more productive. Business leaders ask where those improvements appear in revenue and earnings.
There are two distinct challenges. The first concerns how individual capability translates into collective capability. In a city notorious for traffic congestion, replacing every car with a racing car won’t clear the roads. The second concerns how collective capability translates into business value. A murmuration can fly and react faster, but the flock’s survival still depends on reaching the right place at the right time.
AI is making individuals more productive, but those gains are not reliably translating into business value. The mainstream response to this paradox is familiar: redesign workflows, transform operating models, and measure the return on investment. That prescription tends to collapse two distinct challenges—turning individual capability into collective capability, and directing that capability toward business results. Clearer roles, better processes, and deliberate coordination are worthwhile investments. An organization in which work flows through well-designed processes is a worthwhile ideal. But getting there takes time, and much of the work continues to happen through arrangements that are informal, situational, and constantly changing. The mainstream response goes wrong when it treats that ideal as an adequate description of the organization—or as a sufficient explanation of how AI will create value.
Consider what happens after an individual completes a task faster than before. An employee uses AI to prepare a customer analysis in twenty minutes instead of half a day. But acting on it requires colleagues to interpret the findings against what they know. Sales sees an opportunity, product sees a conflict with an existing commitment, and operations identifies a constraint that changes the proposal. The next step emerges through their interaction. A workflow can route the analysis and assign a decision owner, but the participants still have to reconcile changing information, competing priorities, and commitments across teams. That work continues at the pace of human attention, conversations, and negotiated agreement. AI can increase the pressure by generating more possibilities for those same people to assess. This is the first proposition: individual productivity gains encounter limits in the organization’s capacity to develop shared understanding and coordinate action—even when formal workflows are in place.
- What leaders should do: Make work cellular and agentic. Organize work into complete outcomes, each with one accountable owner who orchestrates an agentic flow from start to finish. Shift from seven people completing separate fragments to an owner equipped to deliver the whole, drawing on others where their judgment or expertise is needed.
Consider what happens after an organization becomes faster, more adaptable, and agile. Teams spot changing customer needs sooner, explore alternatives faster, and coordinate responses with fewer delays. But choosing which opportunities deserve investment still depends on a small number of leaders, each with limited time, attention, and knowledge. The organization can generate possibilities faster than those individuals can assess and direct them, capping the speed, effectiveness, and capacity of its collective intelligence. Even when a direction is agreed, action waits on budget cycles, purchase orders, and travel approvals. The capacity to sense and respond has improved, but choosing the right targets and bets—and mobilizing around them—remains constrained. This is the second proposition: greater adaptability and agility create business value when the organization can choose the right targets and bets—and mobilize around them quickly enough.
- What leaders should do: Establish a unified model of the business. Connect strategy, customers, capabilities, activities, resources, and outcomes in a shared model, so individual contributions inform the whole and the whole guides individual action. Each cell operates within this shared context, with visibility into dependencies and clear boundaries for decisions. Make the model operational by giving owners the context and authority to choose and act, while keeping their decisions aligned with the business.
Both challenges require leaders to look beyond improving isolated parts. Agility and adaptability emerge from how people, decisions, and resources interact. Leaders accustomed to managing one function, task, or metric at a time need a shared view of those relationships—and a way to direct the whole without becoming the bottleneck themselves.
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