A new FranklinCovey study suggests enterprise AI adoption may be stalling not because of technology limitations, but because managers are failing to lead employees through the transition. The leadership consultancy found that 80% of individual contributors describe their managers’ approach to AI as “hands-off,” highlighting a widening disconnect between enterprise AI investment and workforce readiness as companies accelerate generative AI deployments.
As enterprises rush to operationalize artificial intelligence across departments, new research from FranklinCovey indicates that many organizations may be underestimating one of the biggest barriers to AI adoption: leadership.
According to FranklinCovey’s 2025 AI General Attitudes Survey and Global Leadership Survey, 80% of individual contributors say their managers take a largely “hands-off” approach to AI adoption. The findings suggest that while enterprises continue investing heavily in generative AI tools, automation platforms, and intelligent workflows, many employees are navigating the transformation without clear guidance, training, or governance structures.
The data reflects a growing challenge across enterprise technology environments.
Over the past two years, organizations worldwide have accelerated investments in AI copilots, workflow automation, enterprise large language models, and productivity tools from companies including Microsoft, Google, OpenAI, Salesforce, and Amazon.
Yet enterprise AI adoption remains uneven.
FranklinCovey’s research found that only 14% of workers report receiving formal AI training, while 40% say their managers do not even know how employees are using AI tools in their daily workflows. Meanwhile, 70% of respondents believe AI and technology are advancing faster than their workplace culture can adapt.
The findings reinforce a broader shift occurring across enterprise AI strategies.
Initially, many organizations treated AI adoption primarily as a technology deployment issue centered around infrastructure, software procurement, and automation tooling. Increasingly, however, analysts and enterprise leaders are recognizing that successful AI transformation depends heavily on organizational behavior, management practices, workforce trust, and operational alignment.
In practice, that means AI adoption is becoming as much a leadership challenge as a technical one.
“AI adoption isn’t just an IT problem. It’s a human problem,” FranklinCovey CEO Paul Walker said in the company’s announcement.
The research arrives at a critical moment for enterprise workforce transformation.
Companies across sectors including finance, healthcare, retail, manufacturing, and professional services are experimenting with AI-driven productivity gains while simultaneously confronting employee concerns around job security, performance monitoring, and changing workplace expectations.
Online discussions across management and enterprise AI communities increasingly reflect those tensions.
In several recent Reddit discussions among engineering managers, executives, and enterprise operators, professionals described AI adoption as fragmented, poorly governed, or driven primarily by executive pressure rather than operational clarity. Some managers reported struggling to define AI ownership structures, while others warned that organizations risk confusing automation hype with sustainable operational value.
The disconnect is becoming especially important as enterprises move beyond experimentation into operational AI deployment.
According to IDC, worldwide spending on AI-centric systems is expected to exceed $300 billion by 2027, while Gartner projects that generative AI-assisted workflows will become deeply integrated into enterprise operations over the next several years.
But large-scale deployment requires more than access to AI tools.
FranklinCovey argues that organizations need structured leadership frameworks capable of helping employees integrate AI into daily decision-making, collaboration, communication, and workflow management. The company says unmanaged experimentation can create inconsistent practices, governance risks, and reduced trust across teams.
To address the issue, FranklinCovey launched two AI-focused leadership and workforce training programs aimed at helping enterprises operationalize AI adoption more effectively.
The first, “Leading AI Adoption,” is designed for managers and focuses on helping leaders identify AI opportunities, establish team guidance, and support organizational change management. The second, “Working with AI,” focuses on practical workforce integration and what the company calls “hybrid intelligence” — combining human judgment with AI-driven productivity systems.
The emphasis on “hybrid intelligence” reflects a growing enterprise consensus that AI systems are unlikely to fully replace human decision-making in most business environments.
Instead, enterprises are increasingly pursuing augmentation models where AI handles repetitive analysis, workflow automation, and data synthesis while humans retain oversight, strategic thinking, creativity, and accountability.
That trend is already reshaping management expectations.
In online enterprise leadership discussions, managers increasingly describe AI as a tool for accelerating execution rather than eliminating human leadership entirely. Many report that AI is changing workflows, communication practices, and productivity expectations, but still requires strong managerial oversight and operational governance.
The FranklinCovey report ultimately highlights a deeper issue emerging across the enterprise AI market.
Organizations may be investing heavily in AI infrastructure while underinvesting in the human systems needed to make those technologies effective.
As generative AI becomes embedded across enterprise software ecosystems, leadership capability, workforce adaptability, and organizational trust could become some of the most important competitive differentiators in long-term AI transformation strategies.
