The Growing AI Readiness Gap Between Leaders and Employees
Companies are moving quickly toward AI agents. Their employees are far less convinced that the organization is ready.
60% of AI decision-makers say their company is prepared to deploy AI agents. Only 36% of AI users agree.
That 24-point gap is one of the clearest findings in Notion’s The Great Renovation report. It shows how differently AI transformation can look from the boardroom and from the desk of the person expected to use the technology every day.
The confidence gap is just as sharp. 49% of decision-makers are confident in their organization’s ability to use AI, compared with only 23% of AI users.
The issue is becoming less about access to AI and more about whether companies are building the workflows, rules, systems, and support employees need to use it effectively.
Most Companies Are Still Early
Notion’s four-level maturity model helps explain the disconnect.
At Level 1, AI is mainly a thought partner for drafting, brainstorming, and analysis. 57% of respondents are here. Another 31% sit at Level 2, where AI acts more like an assistant connected to information and internal systems.
Only 10% have reached Level 3, where AI begins handling recurring workflows as a teammate. Just 2% are at Level 4, where autonomous agents can run complex processes end to end.
Put another way, 88% of organizations remain in the first two stages.
That matters because many companies are already discussing agents and end-to-end automation while most employees are still experiencing AI primarily as a personal productivity tool.
The strategy may be moving faster than the workplace itself.
The Surprising Part: The Gap Grows With Maturity
It would be reasonable to expect advanced organizations to feel more prepared. The report shows a different pattern.
The share of decision-makers who say their company is investing in AI faster than employees are prepared to use it rises steadily:
→ Level 1: 48%
→ Level 2: 55%
→ Level 3: 60%
→ Level 4: 68%
The companies furthest along in AI transformation are therefore also the most likely to acknowledge that workforce preparation is struggling to keep pace.
That changes what “AI readiness” means.
Rolling out more tools is only part of the job. As AI takes responsibility for larger parts of a workflow, employees need to understand where it fits, what it can access, when a person should intervene, and how problems are handled.
What More Mature Organizations Do Differently
Companies at Levels 3 and 4 are not simply using more AI. They are building more structure around it.
The report shows several clear differences:
→ AI integrated with existing systems: 55% vs. 37%
→ Governance and oversight: 42% vs. 26%
→ Formal impact measurement: 37% vs. 22%
→ Recurring AI workflows: 47% vs. 34%
→ AI connected to company knowledge: 50% vs. 40%
More mature organizations are also more likely to establish AI policies, provide company-wide training, and standardize approved tools.
The pattern is important. AI readiness is not only a training problem. It is an operating-model problem.
Employees need skills, but they also need AI connected to trusted information, clear policies around its use, and workflows that make its role understandable.
The Problems Change Once AI Becomes Operational
Early-stage organizations are more likely to struggle with basic adoption and trust.
As AI maturity increases, the problems shift.
Too many tools. Poor integrations. Unclear governance. Inconsistent model performance. Limited access to organizational knowledge. Difficulty proving real impact.
At the same time, low trust in AI outputs becomes less prominent at higher maturity levels.
The question changes from “Can we use AI?” to “Can we make all of these systems work together?”
That is harder because it reaches across technology, policy, management, and daily operations.
Mature Companies Measure More Than Time Saved
Measurement changes too.
More mature companies are less dependent on anecdotes about productivity and more likely to track whether AI improves actual business processes.
Among Level 3 and 4 organizations:
→ 54% track quality metrics
→ 50% track workflow performance
→ 44% measure financial impact
→ 37% track risk and compliance
→ 31% measure usage and adoption
→ 30% use controlled experiments
This is a meaningful shift.
Saving an employee an hour is useful. But when AI becomes part of a recurring workflow, stronger questions become possible: Did quality improve? Did cycle time fall? Did throughput increase? Did costs or risks change?
Those measures reveal whether AI is improving the business rather than simply increasing usage.
The Real Gap Is Between Strategy and Daily Work
The report does not suggest that leaders are simply overconfident or employees are resistant to change.
It shows that both groups experience AI transformation from different positions.
Leaders see investment plans, deployments, and future opportunities. Employees experience whether the tools work, whether company knowledge is accessible, whether the rules are clear, and whether AI makes their work easier or more complicated.
Both realities can exist at the same time.
A company can have an ambitious AI strategy and still be operationally unprepared.
The organizations moving furthest are building more than AI capability. They are building governance, integrations, workflows, knowledge access, and measurement around it.
The real measure of readiness may therefore have little to do with how many agents a company deploys.
It is whether employees know where AI belongs, what it is allowed to do, and how success will be measured.