Executives & staff split on AI readiness, Acorn finds

Acorn has released research showing a wide gap between how executives and employees assess workplace readiness for AI. The survey covered 1,224 professionals at organisations with more than 1,000 employees in the United States, Canada and Australia.

The findings point to a mismatch between investment in AI tools and the systems companies use to define, assess and develop AI skills. Many organisations are rolling out AI software without setting role-specific standards for what strong performance with those tools should look like.

One of the starkest divides concerns managers. While 77% of executives said managers are prepared to guide AI skills development, 91% of employees said their managers are not fully prepared to hold meaningful conversations about AI capability.

The same split appears in views of organisational readiness. Almost four in five executives said they were very confident their company's approach to AI competency would prepare the workforce for AI-driven role changes. By contrast, 41% of individual contributors said they had no confidence at all in that approach.

Measurement gap

The research suggests many employers still rely on blunt measures to judge whether learning efforts are working. Across learning more broadly, 77% of organisations treat training completion as evidence of capability, while 64% of respondents said they could not confidently say whether their company's approach to measuring learning could show if employees were getting better at their jobs.

A similar pattern appears in AI deployment. The findings show that 47% of companies have not included AI capability in formal performance reviews, 34% have not defined AI competencies at the role level, and 30% have no formal mechanism to assess and track AI capability at the individual employee level.

Acorn also found a persistent disconnect between reported and observed performance. According to the survey, 83% of respondents saw a disparity between what employees in their organisation report about their job capabilities and what they demonstrate in practice, while 88% said companies may be seeing the same problem in AI.

Efficiency gains appear limited so far. Three quarters of employees said AI had made them less than 25% more efficient, and 59% of individual contributors said the improvement was only slight, at less than 10%.

Manager strain

The data suggests the issue predates the current wave of AI adoption. Seventy-five per cent of individual contributors said their managers were only somewhat prepared or not prepared to have meaningful conversations about traditional skills, and 54% of managers agreed.

For AI, the divide becomes sharper. While 77% of executives thought managers were very prepared for AI-related discussions, only 34% of managers said they felt prepared, and just 9% of individual contributors agreed.

Blake Proberts, Chief Executive Officer and Founder of Acorn, said the findings showed a structural split in how AI deployment is experienced inside large organisations.

"What this research makes clear is that there are two workforces experiencing the same AI deployment from fundamentally different positions," said Proberts.

"The deficiency in manager preparedness highlights a measurement infrastructure problem. Managers can't guide development conversations they have no evidence to anchor on, and without that evidence, employees default to skepticism," he said.

The report argues that managers are being asked to support development without clear frameworks, evidence or role-level benchmarks. As a result, they cannot explain what strong AI use looks like in a given job or how to improve it.

Employee scepticism

The survey found employees were far less enthusiastic about AI than senior leaders. Among individual contributors, 58% said they were slightly sceptical and 28% said they were scared or disillusioned, compared with 82% of executives who said they were excited about AI.

Nearly 60% of employees said they were not confident they knew how to apply AI in their specific role. The report also found that 58% of companies had employees who were proficient with AI in general but struggled to apply it meaningfully to their jobs.

Keith Metcalfe, President of Acorn, said companies were spending money on AI without giving workers enough practical guidance.

"It is clear AI adoption has outpaced enablement," said Metcalfe.

"We see companies throwing budget at AI without giving their employees the guidance and support required to effectively use it in their roles. The result is an overly confident C-suite and a directionless employee base that is struggling to make sense of AI directives," he said.

The survey points to a broader concern about whether current training models are fit for purpose. Nearly six in ten organisations said the development programmes they run are insufficient, with 58% reporting that their plans are only somewhat effective, not very effective or not effective at all in improving performance and building capability.

Metcalfe said the central problem was that organisations were mistaking usage for readiness.

"The bottom line is organizations are instituting AI strategies without defining what AI competency looks like and subsequently building AI training programs without role-level targets," he said.

"The primary mechanism organizations are using to measure AI adoption is tracking usage. This gives a wholly incomplete picture of the state of AI readiness in the organization. Without the proper direction and training infrastructure, employees don't know what 'good' looks like for AI in their role, managers can't assess it and organizations are flying blind on one of the most consequential and unprecedented workforce transitions they'll face."