Companies are racing to introduce artificial intelligence into the workplace. They're investing in tools, redesigning workflows and asking employees to embrace new ways of working.
But a growing body of research suggests organizations may be overlooking something important: the experience of the people expected to use the technology.
Employees are more likely to benefit from workplace AI when they have a meaningful say in how it affects their jobs. For employers, successful AI adoption requires more than software and training. It requires managers who listen, explain what's changing and give employees opportunities to shape how the technology is used.
New research released October 21 by Charter makes that distinction particularly clear.
Its AI Pulse study surveyed more than 5,000 AI users and included over 90 interviews. One of its central findings is that employee agency—having influence over how AI fits into one's job—is a leading factor associated with benefiting from the technology.
Why Is Employee Experience Becoming an AI Problem?
For executives, AI often represents an opportunity to increase productivity, reduce costs and improve business performance.
Employees may see the same technology differently.
Will their responsibilities change? Will they be expected to accomplish more in less time? Will their professional judgment still matter? Could their jobs eventually disappear?
Those questions are not necessarily resistance to innovation.
They're reasonable concerns about how work is changing.
A separate October 2026 analysis from Workday illustrates the divide.
In its global AI-at-work research, 66 percent of senior and executive leaders said they trusted their employer's approach to AI, compared with just 32 percent of individual contributors.
Leaders were also much more likely to report that AI improved their daily working experience.
The findings suggest that the people making decisions about AI may be experiencing its benefits differently from the employees implementing those decisions.
What Happens When Managers Don't Involve Employees?
Charter's research identifies another gap.
Managers were approximately twice as likely to report providing helpful AI-related support as employees were to recognize receiving it.
Furthermore, 11 of the 14 leading factors associated with employees benefiting from AI involved management practices.
That suggests AI implementation is partly a management challenge.
Consider an operations employee whose organization introduces an AI system to generate reports.
The employee may initially save time producing a first draft.
But if the system regularly generates inaccurate information, the employee must spend additional time checking and correcting the output.
Workday's October research found that one-third of employees reported losing at least half the time AI saved them to rework.
A manager focused only on the speed of initial report generation might conclude that productivity improved.
The employee may have a very different experience.
Give Employees a Voice Before Changing Their Work
Organizations should involve employees in deciding where AI can provide genuine value.
That starts with asking practical questions.
Which tasks are repetitive but still require considerable time?
Where do employees encounter bottlenecks?
Which responsibilities depend heavily on professional judgment?
What would employees prefer to spend more time doing?
These conversations can help distinguish tasks that are suitable for automation from those where human involvement remains essential.
They can also reveal problems that technology vendors or senior executives may not see.
For example, a healthcare administrator may welcome AI assistance with routine documentation but be uncomfortable allowing it to influence sensitive patient-related decisions without appropriate safeguards.
An engineering professional may find AI useful for organizing technical information while insisting that qualified employees remain responsible for validating calculations.
Employee involvement helps organizations make those distinctions.
Managers Must Explain What Changes—and What Doesn't
Transparency is equally important.
Employees should understand how AI will affect performance expectations, accountability and professional development.
Will productivity targets change?
Who is responsible when an AI-generated result is wrong?
Are employees allowed to question or reject an AI recommendation?
Will time saved through automation create opportunities for more meaningful work, or simply additional assignments?
Managers should answer these questions before expecting widespread adoption.
And they should revisit them as employees gain experience with the technology.
Why This Matters
AI adoption and positive employee experience are not the same thing.
Organizations can successfully deploy new tools while leaving employees uncertain, frustrated or disconnected from decisions affecting their work.
The latest research suggests that employee agency and management support deserve as much attention as technical implementation.
The objective shouldn't simply be getting employees to use AI.
It should be helping employees do better work while retaining the judgment, influence and support they need to succeed.
Employee Experience Signal
The strongest AI implementation may not be the one with the highest adoption rate. It may be the one where employees can explain how the technology has improved their work.
CTA
Before your next AI rollout, ask employees three questions:
What part of your work would you most like AI to make easier?
What concerns do you have about how AI could change your responsibilities?
What decisions should always remain under human control?
Use their answers to shape the implementation plan and establish a follow-up review after 30 days.
How StrikeForce Can Help
StrikeForce helps organizations align workforce capabilities, management practices and evolving job requirements. As AI changes how work is performed, thoughtful workforce planning can help employers identify skills needs, support employee transitions and maintain a people-centered approach to organizational change.