A new study from Asana’s Work Innovation Lab has revealed that two years on from AI’s rollout within workplaces, two thirds (67%) of companies are still failing to scale the technology across their organisation.

Asana’s latest study shows that whilst AI is being rolled out within organisations, its use is siloed in a ‘leadership bubble’ and the technology is built and used mostly for solo use cases rather than across teams which is preventing wide adoption across companies.

The study, which surveyed 3,182 workers at companies across the US and the UK, as well as 112,000 workers at over 350 organisations using Asana’s AI, reveals the key blockers that are prohibiting AI from ‘taking off’ within large organizations – and the productivity gains that can be made by resolving these issues.

The Leadership Bubble

According to the findings, AI adoption in many companies is happening in a ‘leadership bubble’ – executives are all-in, but employees are on the sidelines.

Senior leaders are 66% more likely to be early AI adopters than their employees, and managers are 38% more likely to use AI weekly than individual contributors. As a result, adoption becomes bloated at the leadership level without infiltrating down to individual contributors.

Some of the key reasons for this AI ‘leadership bubble’ include employee skepticism, which is still rife within organisations. Individual contributors are 39% more skeptical of AI than their company leaders.

Further, job security worries continue, as employees are about a third (32%) more worried about job security than company leaders.

The study did fine that AI’s delegation nature is better suited to executives. Workers who frequently delegate to humans are 1.9 times more likely to delegate to AI. But for more junior employees, delegation – whether to people of AI – often feels foreign.

These findings point to a lack of dialogue around AI between executives and employees. Whilst 59% of companies track AI’s financial return on investment (ROI), only 23% measure employee satisfaction around AI. By creating this dialogue with employees, companies can spot critical blind spots like areas of skepticism and concern about jobs.”

Dr Mark Hoffman, collaborative intelligence lead at Asana’s Work Innovation Lab commented: “If employees feel like AI is something that is happening to them, not for them, adoption will stall, and it won’t scale. That’s why companies that do track employee satisfaction and feelings around AI are 32% more likely to see AI adopted across job levels, not just at the leadership level.”

Who is AI Built For?

Another key blocker to scaling AI goes back to how AI workflows are built within companies. Most AI adoption starts as a solo experiment. These experiments matter, but they don’t scale.

According to Asana’s study, 49% of AI workflows are built for individual use, yet these individual-first workflows drive only 6% of downstream adoption by colleagues and peers. This is because they are not designed for the team first.

Additionally, many teams prohibit AI from scaling within their organisation due to challenges working across teams. Only 21% of employees say teams across their organisation work together effectively.

However, Asana’s study shows that employees who work across teams are much more likely to adopt AI. When employees collaborate with another employee using AI, they are 30% likely to adopt AI themselves.

However, when AI is embedded in cross-functional workflows, adoption takes off. In fact, users are an eye-popping 46% more likely to adopt AI when a cross-functional partner is already using it.

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According to Asana’s data, AI is creating partnerships between departments that do not usually work together.

One of the most common, yet unlikely pairings on AI workflows and projects is the IT and HR department. Marketing and IT, and Marketing and Finance are also more likely to work together using AI than other departments.

In addition, the cross-functional workflows that save the most time tend to connect business functions with other teams in the organisations.

Marketing and sales, for instance, reported saving over 124 seconds per workflow executed, and finance and marketing reported saving over 75 seconds per workflow.

“Before companies can truly scale AI within their organization, they must first examine how team work happens,” Hoffman said.

“If teams are operating in silos, workers are more likely to continue using AI for solo use rather than unlock AI use within teams – and crucially, across different team functions, where we are seeing the strongest impact from AI”