If you have been trying to figure out AI mostly on your own, new research suggests the reason has less to do with you and more to do with the silence above you.
On May 21, 2026, FranklinCovey (NYSE: FC), the Salt Lake City training company behind frameworks like the 7 Habits, released findings on how AI is actually showing up inside organizations. The headline number is hard to read past. 80% of individual contributors describe their manager's approach to AI as "hands-off." Not hostile. Not all-in. Just absent.
That single figure puts a different read on a feeling a lot of professionals carry quietly. The sense of being behind. The assumption that everyone else got a memo you missed. The research says something steadier: in four out of five teams, nobody is steering. If you feel like you are improvising, you are not the exception. You are the 80%.
This article walks through what FranklinCovey found, why hands-off leadership stalls adoption, and what a deliberate path looks like for the people and managers who want one.
What did FranklinCovey's research find?
FranklinCovey drew its findings from two of its own 2025 studies: the AI General Attitudes Survey and the Global Leadership Survey. The company released the results alongside two new training offerings. These are company-reported figures, so read them as one well-positioned firm's snapshot of the workplace rather than audited national data. Even with that caveat, the pattern is consistent.
Four numbers carry the story:
- 80% of individual contributors call their manager's approach to AI "hands-off."
- 14% of workers say they have received any AI training at all.
- 40% of workers say their manager does not know how, or whether, they are using AI in their work.
- 70% of workers say AI and technology are advancing faster than their culture can adapt.
of individual contributors describe their manager's approach to AI as hands-off. Not hostile, not all-in, just absent.
Source: FranklinCovey, 2025Sit with the second one for a second. 14%. That means roughly six in seven people have been handed a powerful, fast-moving set of tools with no instruction. They are learning in the dark, on their own time, with no shared standard for what good even looks like.
The third number is the quiet one. 40% of managers cannot say whether their own people use AI. Work is happening. Decisions are being made with these tools. And the person responsible for the team has no visibility into any of it.
Why does hands-off management stall AI adoption?
Here is the trap. Hands-off can feel like trust. Leaders tell themselves they are empowering smart adults to experiment. In practice, no direction reads as no priority. People hesitate, hide their experiments, or wait for permission that never comes.
That hesitation is expensive. When 70% of workers feel the technology is outrunning their culture, the missing layer is a plan. People are willing. They lack a map.
Paul Walker, FranklinCovey's CEO, called this a human challenge more than a technical one. He put it directly: "It's a human problem." That cuts against the common instinct to hand AI to a tools team and wait. The hard part was never the software. It is the judgment, the guardrails, and the shared habits that decide whether a tool helps or just adds clutter.
The hard part was never the software. It is the judgment, the guardrails, and the shared habits that decide whether a tool helps or just adds clutter.
What are the three leadership patterns that undermine AI adoption?
FranklinCovey's research names three ways leaders unintentionally stall adoption. Each one looks reasonable from the inside. Each one leaves teams worse off.
Encouraging use without guidance
A leader says "go try AI" and stops there. No examples, no boundaries, no review. The intent is good. The result is inconsistent improvising, where one person builds something useful and another quietly pastes confidential data into a chatbot. Without guardrails, the team cannot tell the two apart.
Treating AI mainly as a way to cut costs
When the message from the top treats AI mainly as a way to do more with fewer people, workers hear a threat. The research found that this approach breeds anxiety rather than engagement. People do not experiment freely when they suspect the tool is being sharpened for their own role. Fear is a poor teacher.
Moving so cautiously that a vacuum forms
The opposite error is paralysis. A leader waits for the perfect policy, the vetted vendor, the legal sign-off. Meanwhile, informal and unguided experimentation fills the empty space. People adopt AI anyway. They just do it without any of the structure caution was supposed to provide.
All three patterns share one root. The leader is not present in the work. Adoption is left to chance, and chance is not a strategy.
If you feel behind on AI, are you actually the problem?
For a lot of professionals, the honest answer is no.
When 80% of teams get no direction and only 14% get any training, the feeling of falling behind is mostly a feeling of being unsupported. That is worth naming clearly, because the two get confused all the time. Being behind implies a personal deficit. Being unsupported points at a system that never built a path.
There is an opening in that. In an environment where four out of five teams are improvising, the person who builds a deliberate approach stands out fast. You do not need a budget or a title to start. You need a small set of your own guardrails, a habit of weekly practice, and a way to measure your own progress.
That last piece is where a shared standard helps. The 7 Levels of AI Proficiency exists to give people and teams exactly that: a way to name where you are today and what the next step looks like. Level 1, the Cadet, is becoming AI aware and trying tools for the first time. Higher up, a Level 4 Commander is building context and systems that make AI reliable for an entire team. The framework is less about ranking people and more about replacing the vague dread of "behind" with a concrete picture: here, and here is next.
How does a shared standard fix a hands-off culture?
A measurable standard does for a manager what a map does for a road trip. It replaces a vague sense that the team should probably do something with AI with a concrete read: most of the team is early on the path, a few people are ready to build, and the next round of training can target everyone in between.
That plain picture also answers the cost-cutting trap. When a leader can show the team a path that ends in more capable people, not fewer people, the anxiety drains out of the room. AI stops being the thing coming for the job and becomes the thing that makes the job bigger.
Walker made the same argument about ownership. "There's no viable 'AI czar' model," he said. "Centralizing AI ownership creates bottlenecks, stalls innovation, and alienates the very people who need to adopt it. Ownership is distributed, which means every leader owns it." A single AI czar cannot watch every workflow. A shared language can, because it lets every manager hold the same standard without waiting on a central authority.
This is also why we keep saying the human stays in the loop. Walker's own words line up with it: "The organizations winning with AI are pairing human judgment, creativity, and accountability with AI's speed and scale, and they're investing in the leaders and teams who make that pairing work." The tool is fast. The judgment about when to trust it is human. Design the work first, then bring the tool in to serve it.
What can you do this week?
You do not have to fix your whole organization to get unstuck. Start small and start with yourself.
- Pick one task you do every week and try doing it with an AI tool you already have, like ChatGPT, Claude, or Copilot. Note what worked and what you had to correct.
- Write down two or three rules for yourself: what you will never paste into a tool, what you will always double-check, and where a human has to sign off. Those are your guardrails, even if nobody handed them to you.
- If you manage people, ask your team one honest question this week: how are you actually using AI right now? 40% of managers cannot answer that. Closing that one blind spot puts you ahead of the pattern.
- Find your starting point. The 7 Levels of AI Proficiency assessment takes about ten minutes and tells you where you are today, which makes the next step a lot less abstract.
The research is a little uncomfortable to read. It is also a relief. If the silence around AI made you feel slow, the data says the silence was the problem, not you. The good news is that a plan is a choice anyone can make, starting with the next thing on your list.
Related reading: Level 1: The Cadet.
Sources
- 80% of Individual Contributors Say Their Manager's Approach to AI is Hands-Off, FranklinCovey Research Finds (Business Wire)
- FranklinCovey research release coverage (StockTitan, NYSE: FC)
- 80% of Individual Contributors Say Their Manager's Approach to AI is Hands-Off (Las Vegas Sun syndication)
- Leading AI Adoption: Accelerate AI Impact Through Empathy and Action (FranklinCovey)
- Working With AI: Essentials for Working Smarter Together (FranklinCovey)
Frequently Asked Questions
Who ran this research?
FranklinCovey (NYSE: FC), a Salt Lake City leadership and training company. The findings come from its 2025 AI General Attitudes Survey and 2025 Global Leadership Survey and were released on May 21, 2026. The company did not publish respondent counts or methodology, so treat the figures as company-reported.
What does "hands-off" mean here?
It means a manager neither directs nor restricts how their team uses AI. According to the research, 80% of individual contributors describe their manager this way. It often feels like trust, but in practice it leaves people without guidance, guardrails, or training.
Is AI adoption an IT problem?
FranklinCovey's CEO Paul Walker argues it is mainly a human one. Tools are the easy part. The harder work is the judgment, training, and shared habits that decide whether a team uses AI well. His view is that ownership should be distributed across every leader, not handed to a single "AI czar."
What is the 7 Levels of AI Proficiency?
It is a framework that names AI capability as a measurable path, from Level 1, the Cadet who is just becoming AI aware, up through people who build systems and lead teams. It gives individuals and managers a shared standard for where they are and what the next step is.
Find your AI Proficiency level
The free 7 Levels assessment places you across seven stages of AI capability. Under ten minutes. Research-backed scoring.