New white paper argues AI training could ease burnout and help workers regain control of their jobs

A fresh claim and why it matters now

The University of Phoenix College of Doctoral Studies has released a white paper that makes a clear, practical claim: giving workers the right kind of training in artificial intelligence tools could reduce job burnout and help people feel more in control of their work. The paper arrived at a time when many workers say they are worn out by long hours, frantic task-switching, and a sense that they have little say over how work gets done. The authors argue that smart, job-focused AI training can turn tools from a source of stress into a source of power.

Dr. Rheanna Reed, who led the report, summed up the idea plainly: “When people know how to use AI for the parts of work that drain them, they spend more time on the parts that matter to them.” The paper frames this as a workforce strategy you can act on now, not a distant theory.

What the research found: burnout, opportunity gaps and the AI-skilling link

The white paper paints a picture many readers will recognize: burnout at unusually high levels, and clear gaps in who gets access to useful training. The report says burnout shows up not just as tiredness but as lower confidence, fewer chances for promotion, and a loss of day-to-day control at work. It links those problems to uneven training and a lack of workplace support for learning new tools.

Crucially, the paper finds a consistent pattern: people and teams that had access to targeted AI-skilling programs reported better feelings about their work and more control over how tasks were completed. The authors describe this as a two-step benefit — workers get practical skills that speed or simplify routine tasks, and those gains free them to focus on higher-value, more meaningful work.

Headline evidence in the paper includes survey results and interview excerpts showing that access to applied AI training is tied to measurable improvements in worker autonomy and well-being. As Dr. Reed put it, “Training that is practical, job-centered and backed by employers changes how people experience their day-to-day work.” The paper stresses that training by itself is not a cure; it matters how training is designed and supported on the job.

How the analysis was done and how much weight to give the results

The authors base their conclusions on a mix of survey data and interviews carried out by the College of Doctoral Studies. The sample pulls from working adults across industries and includes both quantitative responses and qualitative stories. That mixed approach helps the paper show patterns and human impacts at the same time.

There are limits. The study relies on self-reported experience, which can reflect perception as much as objective change. It is also cross-sectional in places, meaning the results point to strong links rather than ironclad proof that training directly causes lower burnout. Still, the College of Doctoral Studies is a recognized arm of the University of Phoenix, and the paper is clear about its methods and caveats — which makes it a useful contribution, not definitive proof.

What this could mean for workers and workplaces: real opportunities, real barriers

For workers, the practical upside is straightforward. Applied AI training could cut time spent on repetitive work, reduce frantic task-switching, and restore focus on more meaningful responsibilities. That tends to improve job satisfaction and the sense of being in charge of one’s day.

For employers, the paper makes a business case: better-skilling programs can boost productivity and morale, and help retain staff who otherwise might leave out of frustration. But the paper warns that simply giving employees software is not enough. Training must be hands-on, tied to real tasks, and supported by managers who change workflows so newly learned skills can actually be used.

Policymakers get a dual message: public funding and incentives can widen access to useful training, but equity must be central. The paper flags the risk that AI skilling could deepen divides if only certain workers or sectors get support. Other barriers include training costs, time off for learning, uneven internet access, and managers’ reluctance to redesign jobs.

Recommendations, resources and a plain takeaway

The paper offers practical next steps: expand employer-supported, job-focused AI training programs; create public-private partnerships to fund access; measure outcomes that matter for workers, like time saved and shifts in task mix; and pilot programs that tailor learning to different roles. It highlights existing training models and calls for more employer buy-in so skills translate into real changes at work.

Readers looking for the full document can find the white paper on the University of Phoenix College of Doctoral Studies website. The short, neutral takeaway: AI-focused skilling looks promising as a tool to ease burnout and restore workplace control — but it works only when training is practical, widely available, and backed by changes in how work is organized.

Photo: Edward Jenner / Pexels

Sources

• prnewswire.com