AI at Work: Faster Execution, Higher Judgment

AI is broadly embedded across most organizations, with 67% of respondents describing adoption as part of everyday operations. Another 32% are in uneven but advancing adoption, suggesting meaningful momentum even where rollout is still inconsistent. Only 1% remain at an early experimentation stage, indicating that limited use is now rare.

Maturity still varies in practice. The largest group describes AI improving core workflows such as documentation, collaboration, automation, and workforce management, while nearly one-third report slower organizational uptake driven by trust, confidence, or change management barriers. This points to a market where adoption is widespread, but cultural readiness and consistency still separate leaders from followers.

AI primarily supports managerial communication by helping people draft materials and prepare for leadership conversations. About seven in ten respondents use it heavily for reports, summaries, agendas, or first drafts tied to manager or leadership discussions, while 27% use it more lightly for occasional updates and only 4% do not use it at all in these contexts.

Heavy users treat AI as a preparation layer rather than a substitute for judgment, using it to organize thoughts, summarize work, and sharpen messaging before meetings. Lighter users tend to rely on it for routine updates or packets, suggesting a narrower productivity use case; outright non-use is rare, which reinforces how normalized AI has become in upward management workflows.

Employees are not replacing managers wholesale with AI, they are using it as a selective first stop. Nearly two-thirds, 64%, go to AI first depending on the question type, while 16% use AI first for routine questions more broadly. Only one in five still primarily rely on managers.

Question type is the key boundary. Employees turn to AI for generic information, drafting, research, and fast retrieval, but shift back to managers for company-specific context, judgment, alignment, and riskier decisions. That pattern suggests AI is reducing routine manager interruptions without displacing the human role in decision making and organizational interpretation.

Tool-mediated coordination is the dominant workflow, with 54% of respondents describing collaboration as highly system-driven. Another 41% rely on hybrid models that combine digital tools with human orchestration, while only 5% describe mostly manual coordination, showing that structured platforms now anchor how cross-functional work gets done.

Highly tool-mediated teams use task management, messaging, and automation in tandem to keep work visible and moving. Hybrid coordination remains substantial, suggesting that tools handle routine handoffs well, but people still step in to align complex, cross-functional efforts. Fully manual coordination is rare, indicating limited reliance on informal or ad hoc workflows.

Managers are increasingly redirecting time away from administrative coordination and execution toward higher-value strategic leadership. Nearly two-thirds, 62%, report a clear shift to planning, prioritization, coaching, and longer-term decision-making. Another 27% describe only a partial shift, while just 11% say managers remain largely unchanged and still concentrated on operational or administrative work.

The shift is meaningful but not complete. Many managers are gaining time through AI and digital tools, yet routine oversight and human review still limit how far responsibilities can move upstream. In practice, this creates a split reality: most managers are becoming more strategic, while roughly four in ten still remain at least partly anchored in hands-on execution and day-to-day administration.

AI is expanding role scope beyond core responsibilities for a clear majority of respondents. Nearly six in ten, 57%, describe broader, more end to end ownership, while 28% say their core role is largely unchanged but executed faster. Only 11% report stable roles with added AI or tool stewardship layered onto existing duties.

This pattern suggests AI is not just a productivity lever, it is often a capacity multiplier that pulls leaders into adjacent functions, more projects, and broader operational accountability. The contrast matters: most respondents describe true scope expansion, while smaller groups either absorb AI governance responsibilities or use AI mainly to accelerate the same work.

Management is being redefined, not removed. Nearly all respondents, 92%, said AI will make managers more strategic while keeping the role essential; just 7% focused primarily on automating routine management tasks, and only 1% expected management roles to materially shrink.

Managers are expected to spend less time on reporting, scheduling, and other administrative work, and more on strategy, coaching, culture, and team leadership. Even where some see wider manager spans of control and possible headcount efficiency, the prevailing view is that human oversight remains critical for performance, morale, and people decisions.