Over the past few years, organizations have begun aggressively driving AI adoption across their workforces, expanding access to tools, encouraging experimentation, and, in some cases, mandating use. However, redesigning operating models, ways of working, and the leadership needed to guide this shift has lagged behind the demands of this new era.
That gap is becoming the new fault line in the next phase of AI.
Across industries, leaders are discovering that deployment alone won’t deliver results. It’s one thing to roll out access to a new tool, but rewiring workflows, decision-making, accountability, and expectations is much harder, and far more consequential.
A new IBM study, Where AI breaks— or breaks through, shows that nearly two-thirds of executives say AI is reshaping roles and workflows. Yet many organizations have been slow to redesign the systems that support that shift, creating a widening disconnect between leaders’ perception of AI progress and how employees actually experience it.
While 78% of executives say employees are involved in designing AI-enabled workflows, only half of employees agree. At the same time, leaders report role transformation at twice the rate employees experience it, pointing to steady progress embedding AI into operations, yet just 17% of employees say AI is part of their daily work.
More than a perception problem, this points to a deeper organizational reality: AI is changing work faster than companies are changing the way work is done.
As AI takes on more routine and process-oriented tasks, such as synthesizing information and analyzing data, the human role increasingly centers on direction, judgment, and decision-making. In practice, this means that more employees are spending less time producing outputs themselves and more time identifying problems, guiding outputs and evaluating results. A data analyst no longer spends hours pulling reports but focuses instead on defining which questions matter. Developers start to ask, “What should this system accomplish?” instead of, “How do I code this?” Marketers spend less time drafting and more time refining strategy, and so forth.
This change in the nature of work forces a broader operating model shift, as many organizations built their operating rhythms around individual contributors producing high-quality work. Now, teams must also assign work to AI agents and integrate them into daily workflows, which requires a level of clarity, communication, and oversight many enterprises have never had to operationalize before. This also places new expectations on managers: they must coach judgment, oversee human-AI collaboration, and help teams adapt to new ways of working, even as many organizations have yet to evolve their management practices accordingly.
This new friction is becoming increasingly visible across the enterprise. Our study shows that many organizations still lack clear rules for when AI outputs should be challenged or overridden. And even where guidelines do exist, most employees say they already feel outdated or disconnected from how work actually happens.
These are not technology gaps. They’re operating model gaps, and the organizations pulling ahead are addressing them directly.
Providence Health offers a glimpse of what this looks like in practice. The health system deployed an AI-powered HR agent to streamline its hiring process and improve caregiver experience. Managers now spend 90% less time on administrative hiring steps, freeing them to focus on the judgment calls the system can’t make, evaluating which candidates are the right fit, deciding where caregivers are needed most, and overseeing decisions that affect patient care.
What many organizations are still missing is that AI doesn’t create value simply by accelerating existing tasks. It creates value when companies redefine who is responsible for what, where human judgment is required, and how decisions move through the organization. In Providence’s case, the technology matters but so does the accompanying shift in focus, expectations, and accountability.
Organizations that redesign decision-making, workflows, and incentives alongside AI adoption are achieving up to 73% higher revenue growth and an 11% operating margin advantage, along with stronger trust between leadership and employees. Getting there comes down to a few adjustments: clarifying who makes decisions in AI-enabled work, embedding checkpoints where human judgment is expected, and measuring performance based on outcomes, not just output. Just as important, employees need to understand how expectations are changing and feel confident applying AI in their roles.
AI is already reshaping how work gets done. The organizations that break through will align their operating models with the realities of AI-enabled work, turning a fragmented experience into a consistent, trusted, and ultimately more valuable way of working.
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