Australian executives & workers split on AI progress

Asana has found a wide gap between how Australian executives and workers assess AI progress in their organisations. The gap is larger in bigger businesses, according to a survey of 1,000 leaders and knowledge workers.

Executives gave their organisations 75.2 out of 100 for AI execution, while workers gave 58.2. The 17-point gap widened to about 23 points in organisations with 1,000 or more employees, compared with about 12 points in organisations with 10 to 999 employees.

The research also points to a divide over visibility into day-to-day AI use. Sixty-one per cent of executives said leadership has full visibility into how teams use AI, while 36 per cent of workers agreed.

"Leaders can see the AI investment and the strategy. Workers can see whether AI actually helps them get their jobs done," said Jo Gaines, General Manager, APAC, at Asana.

"That gap matters because making individual tasks faster doesn't automatically make the organisation more effective. When leaders' and workers' views of AI progress differ this much, it's a signal to find out what is happening on the ground before declaring success," Gaines said.

Large company gap

The survey found that larger organisations were more likely to report broad AI adoption, but less likely to score well on execution. Half of respondents from organisations with 1,000 or more employees said AI was used across the organisation or fully embedded, compared with 42 per cent in smaller businesses.

Yet larger organisations scored lower on Asana's AI Execution Index: 61.7 versus 64.9 for smaller businesses. They also scored lower across every measured category.

The biggest gaps were in AI Agent Readiness, where larger organisations scored 56.3 against 61.2 for smaller businesses, and Workflow Integration, where they scored 58.8 against 63.5. The report also found weaker results for large businesses in cross-team coordination and in turning isolated AI uses into repeatable changes across the organisation.

The findings suggest that more systems, teams and approval layers can make it harder for large organisations to turn AI use into broader operational gains.

Use and training

Across all respondents, 94 per cent reported meaningful AI use in their organisation and 91 per cent reported at least one measurable improvement from AI in the past year, including gains in speed or productivity. But respondents reported improvements in an average of only 3.1 of the 10 business outcomes measured in the survey.

Just 22 per cent met the report's definition of an AI Executor, which combines advanced AI use or readiness with measurable improvements in at least four outcome areas.

The data also suggests many employees are using AI tools without formal company approval. Seventy-two per cent of executives and about half of workers said staff use AI tools their organisations have not formally approved.

Training also appears uneven between organisations that are further ahead and those that are not. Among workers in organisations classified as AI Executors, 59 per cent reported comprehensive training in practical and responsible AI use. That figure fell to 29 per cent among workers in organisations classified as AI Adopters.

The findings come as policymakers and businesses increasingly present AI as a source of future productivity growth in Australia. The survey indicates that adoption alone does not guarantee companies are integrating the technology effectively into everyday work.

Asana surveyed 1,000 Australian respondents through Dynata's panel, including 300 senior decision-makers and 700 knowledge workers. The two groups were sampled independently rather than as matched managers and employees within the same organisations, and the scores reflect respondents' answers about conditions for putting AI to work rather than an external audit.

"Buying AI tools is the easy part," Gaines said.

"The harder question is whether people know how to use AI in their jobs, and whether that work connects to what the organisation is trying to achieve. AI needs the context around the work - the goals, people, workflows, dependencies and decisions - to understand what needs to be done and why it matters. To deliver measurable impact, leaders must ask workers the same questions they ask themselves about AI progress, then investigate where the answers differ," Gaines said.