Artificial intelligence is already part of everyday work. It shows up in how people research, write, analyze information, manage schedules and interact with customers. What’s changing is how work gets done. Tasks are shifting, and expectations are being reset, prompting some workers to reassess which skills matter most.

Our recent Logica® Future of Money points to uneven adoption of AI at work. Some workers have access to AI tools and guidance built into their day-to-day roles. Others encounter it only occasionally, or not at all. Those differences influence how change is experienced and are closely tied to how organizations introduce and support these tools.

AI is changing tasks within roles

The most visible effects of AI so far show up in how work is performed, rather than in widespread changes to employment. Analysis from the World Economic Forum shows that AI adoption is concentrated at the task level, with tools applied selectively to activities such as research, documentation, data analysis, scheduling and so forth, while many core responsibilities still rely on human judgment and coordination.

A mix of employee reactions to AI at work is reflected in our data. Some people can see where AI makes their work easier right now, and many still feel uncertain about what it means longer term. Productivity gains and concern about change are not necessarily in conflict, but they are part of how people are making sense of a shift that is already underway.

How AI is being experienced across the workforce

Whether AI feels helpful rather than threatening often comes down to access and support. Our study shows that people in desk-based, full-time roles tend to encounter these tools more regularly. More than half of working Americans (56%) report that their employer uses AI, (most often through publicly available tools rather than proprietary systems) with usage significantly higher among desk-based workers (68%) than non-desk-based workers (41%).

And confidence around AI continues to vary across the workforce. Recent reporting on Gallup’s data shows AI use is rising and confidence in it among workers is not driven only by exposure. It is shaped by whether employers provide training, guidance and a clear sense of how these tools fit into the job. That context helps explain why reactions to AI often appear mixed.

Findings from the Logica Future of Money Study show that views on AI’s impact vary as well, with many workers believing AI will help them perform their job better (55%) and reporting that they want additional training to use it effectively (55%). A smaller group believes AI will partially replace tasks within their role (43%) or eventually replace their job altogether (24%). These perspectives are forming in real time, as people adapt to tools they may not have trained for and roles that are still taking shape. How supported workers feel during that adjustment helps shape how AI is perceived.

In environments with less support, AI is more likely to be experienced as a source of uncertainty. Employees who lack clarity about how tools will be evaluated or how skills will transfer across roles are more likely to associate AI with job insecurity rather than opportunity.

Broader labor-market research helps explain more about this. Evidence from the Yale Budget Lab suggests AI’s measurable effects so far are showing up as shifts in productivity, task composition and performance expectations rather than broad job loss. Research from Anthropic examining labor market impacts reinforces this pattern, finding no systematic increase in unemployment among workers in AI-exposed occupations since late 2022, though the study notes tentative evidence of slowed hiring for younger workers entering these fields. For workers navigating these changes, AI is one factor among many shaping employment decisions, alongside wages, commuting costs, flexibility and financial stress.

Education is shaping future expectations 

Some of these differences may shift as new workers enter the workforce with earlier exposure to AI. Reporting from St. John’s University shows that students are already using AI tools for research, writing, and study support, shaping how they think about productivity and task structure well before their first full-time role.

That early familiarity over time could change how new workers approach tasks, learning, and collaboration. As these differences take shape earlier, they place more weight on how employers respond once people enter the workforce.

Implications for employers and workers

As AI becomes embedded through everyday operational decisions, its impact is shaped by how organizations choose to implement it. Tool approval, training availability, performance measurement and role design all influence whether AI feels useful, confusing or disruptive. Research from McKinsey reinforces this point, showing that leaders increasingly see training and upskilling as one of the most important levers for helping employees adapt to AI at work.

The Logica Future of Money Study shows that workers place high importance on employer-provided programs that support financial stability. In that context, AI training and guidance function as part of broader workforce support rather than as a standalone technology initiative, reinforcing that how organizations implement these tools matters as much as the tools themselves.