Most organisations have AI somewhere in their stack. Budgets are growing. Boards are asking questions. And yet the productivity gains remain stubbornly out of reach for the majority.
McKinsey's 2025 State of AI report puts the numbers in context. 88% of organisations now use AI in at least one business function. But only 23% are actively scaling it across their enterprise. The rest are stuck in pilot mode.
Victoria Chin, Senior Director of Product Strategy for AI at Asana, has watched the pattern play out since the early days of the LLM boom.
"There was this big promise, all this hope to dramatically change things," Chin told UC Today. "What we've seen is that many AI tools have been really powerful for individual use cases. What we haven't seen is truly scaling AI across multiple teams or entire organisations."
The Coordination Tax Nobody Is Solving
Asana's Anatomy of Work research surveyed over 10,000 knowledge workers and found that 60% of working time goes on what Asana calls work about work. Status updates, chasing approvals, following up on tasks that should already be moving.
"When you think of the most strategic work that happens in an organisation, it's typically the work that requires an entire team or multiple teams to come together to execute on something that's actually going to move the needle," Chin said.
Copilots and personal assistants have not touched that problem. The overhead still belongs to humans.
Why Most AI Tools Are Built for the Wrong Unit
Most AI tools serve one user at a time. They do not see the team, understand the workflow, or retain context across multiple people and multiple projects.
Asana's own research studied 3,182 knowledge workers and 560 IT professionals. 67% of organisations have not scaled AI beyond isolated experiments. Only 29% say they are beyond the pilot phase.
The organisations that do scale successfully treat AI as infrastructure rather than a collection of separate tools. They measure adoption and productivity, not just cost savings. That is the problem Asana AI Teammates are built to solve.
Built for the Whole Team
Where most agents answer to one person, AI Teammates answer to everyone on a project.
"Anyone on an entire team or multiple teams can interact with them. You can redirect them. You can give them feedback right in the flow of work where your team is already working," Chin said.
Context makes that useful. Asana has built its work data model over 15 years, and AI Teammates inherit that foundation.
"They know your goals, they understand your timelines and dependencies, they are not starting from scratch every time," Chin said. "They have shared memory where an entire team can benefit, not just the single person who prompted them."
