For years, conversations around workplace AI have focused on productivity gains, automation, and efficiency. A new report from the University of Phoenix suggests organizations may be overlooking a more immediate business risk: employee retention.
The University of Phoenix College of Doctoral Studies has released a new white paper, The Retention Mandate: Bridging the AI Fluency Gap to Secure the 2026 Workforce, arguing that workers are developing AI skills faster than many employers are building the frameworks needed to support them.
Authored by Wayne L. McCoy and published through the Center for Educational and Instructional Technology Research, the paper examines what it calls an “AI fluency gap”—a growing disconnect between employee readiness and organizational preparedness in the age of artificial intelligence.
The core message is straightforward: AI competency is no longer just a workforce development issue. It is becoming a talent retention issue.
Employees Are Learning AI Without Waiting for Employers
One of the report’s central findings is that employees are increasingly taking AI education into their own hands.
Across industries, workers are experimenting with generative AI tools, automating tasks, improving workflows, and developing practical AI skills outside formal corporate training programs. This self-directed approach has accelerated AI adoption at the individual level, but many organizations have struggled to keep pace.
According to McCoy, employees are not waiting for executive teams to establish AI strategies before embracing the technology.
“Many are already learning, experimenting and building confidence with AI tools,” McCoy noted in the report. The challenge for employers is creating the structure, governance, and career development opportunities that help employees apply those skills productively within the organization.
Without that support, workers may begin looking elsewhere for opportunities that better align with their evolving capabilities.
The Rise of the AI Fluency Gap
The report identifies a widening gap between workforce capability and organizational readiness.
Employees may be gaining AI proficiency, but many companies still lack updated job descriptions, formal AI policies, skills assessments, manager training programs, and career pathways that reflect how work is changing.
This mismatch creates uncertainty for workers attempting to understand how AI fits into their future roles.
The issue is becoming increasingly relevant as organizations invest heavily in AI technologies while often underinvesting in the people strategies required to support adoption.
Industry research has consistently shown that technology implementation alone does not guarantee business outcomes. Successful transformation depends on employee trust, leadership alignment, skills development, and organizational culture.
The University of Phoenix report argues that AI should be viewed through that broader lens.
Why Retention Is Emerging as the Bigger Risk
The paper draws on findings from the 2026 Career Optimism Index® and broader research on workplace psychology, technology readiness, and organizational governance.
Its conclusion: employees who feel confident using AI but uncertain about their future within an organization may become more likely to seek opportunities elsewhere.
That dynamic creates a paradox for employers.
Organizations encourage innovation and digital skill development, yet employees who successfully build those capabilities may become more mobile if companies fail to provide clear advancement opportunities.
In other words, investing in AI skills without creating AI-enabled career pathways could unintentionally increase turnover.
As competition for digital talent intensifies, retaining AI-fluent employees may become just as important as recruiting them.
Shadow Learning Is Reshaping Workforce Development
The report also highlights the growing phenomenon of “shadow learning.”
Similar to the concept of shadow IT, where employees adopt unauthorized technology tools, shadow learning refers to workers independently developing AI capabilities outside formal company programs.
This trend is becoming increasingly common as employees access public AI platforms, online courses, professional communities, and self-directed learning resources.
While shadow learning can accelerate innovation and skill acquisition, it can also create governance and consistency challenges.
Employees may adopt different tools, develop varying levels of expertise, or establish workflows that conflict with organizational policies.
The report suggests that employers should embrace employee-driven learning rather than resist it, while creating structures that ensure responsible and aligned AI adoption.
Manager Readiness May Determine Success
One of the report’s strongest themes is the critical role managers will play in AI transformation.
Historically, managers have served as the bridge between organizational strategy and employee execution. That role becomes even more important as AI changes workflows, responsibilities, and performance expectations.
According to the paper, manager capability is a major factor influencing employee confidence, trust, and retention during periods of technological change.
Employees often look to direct managers for guidance on how new technologies affect their work, career progression, and long-term value within the organization.
If managers lack AI knowledge or cannot effectively communicate expectations, uncertainty can quickly spread across teams.
The report argues that manager enablement should be treated as a foundational component of AI readiness strategies.
A Four-Step Roadmap for Employers
To help organizations close the AI fluency gap, the paper outlines a four-part framework for workforce readiness.
1. Define AI Career Pathways
Organizations should establish clear standards around AI-related roles and demonstrate how AI skills connect to career advancement opportunities.
Employees need visibility into how their growing capabilities contribute to long-term professional growth.
2. Build Skills Assessment Systems
Employers should create mechanisms for evaluating AI proficiency and identifying development opportunities across the workforce.
This helps organizations understand current readiness levels while informing future workforce planning.
3. Expand Structured Training and Enablement
Rather than relying on ad hoc learning, companies should provide formal training programs, tools, resources, and support systems that encourage responsible AI adoption.
4. Develop AI-Capable Managers
Managers must be equipped to lead teams through AI transformation, answer employee questions, and reinforce organizational AI strategies.
Without leadership readiness, broader workforce initiatives may struggle to gain traction.
AI Transformation Is About People, Not Just Technology
Perhaps the most important takeaway from the report is its framing of AI adoption as a socio-technical transformation rather than a technology deployment project.
While organizations often focus on software implementation, automation opportunities, and productivity metrics, the report argues that sustainable AI success requires equal attention to governance, workforce development, leadership practices, and employee experience.
This perspective aligns with a growing trend among HR and business leaders who increasingly view AI adoption as an organizational change challenge rather than purely a technical one.
As enterprises accelerate AI investments in 2026 and beyond, the companies that succeed may not be those deploying the most advanced tools, but those creating environments where employees can confidently learn, adapt, and grow alongside them.
The University of Phoenix report offers a timely reminder that in the AI era, retention may depend as much on career clarity and organizational readiness as it does on technology itself
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