New Research: Work Gets Trapped in Single-Player AI. Executives Are Ready for Multiplayer

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What one person learns with AI rarely reaches the team

Despite the growing prevalence of AI agents, most AI use still happens one person at a time, and insights from each session tend to stay with that person.

• Sixty-eight percent of executives acknowledge that insights or outputs from someone's AI use don't get shared or reused by the rest of the team.
• This phenomenon creates siloed information, stalling company output.

Several survey respondents describe how easily a useful discovery can get stranded. "One person learns a prompt that can cut review in half and no one else knows and it stays in their AI workflow," one executive said.

Teams are solving the same problems twice

When AI-fueled discoveries stay private, colleagues often end up repeating the work.

• Sixty-six percent of executives say that two colleagues use AI to solve the same problem separately without realizing it, at least once a week.
• Costs can go beyond lost hours when separate AI-assisted efforts collide, because reconciling the disparate outputs can create additional work.

One IT director noted, "Engineers' time has been wasted when they were prompting AI to fix the exact same error because their separate chat histories prevented them from sharing the solution."

As another respondent put it: "Uncoordinated AI coding has caused duplicated work and major systems conflicts."

Work stalls when it's out of view

Duplicated efforts represent one potential downside of siloed AI use. A lack of shared visibility is another, especially in larger companies.

• Fifty-nine percent of executives say work stalls or gets delayed weekly because it's sitting in a single person's AI tool, where the rest of the team can't see it.
• The problem grows with company size: 73% of enterprise executives report weekly stalls, compared with 37% of small- and medium-sized business executives.

The math on this is simple: A larger number of employees means larger numbers of private chat histories and more places for work to go unseen. For technical teams, these blind spots can turn into time spent troubleshooting problems that otherwise might have been avoided. "Clear visibility into AI-generated code would have saved our developers hours of debugging," said one accounting director.

Few companies have connected AI across the business

The way companies have rolled out AI tools adds to some of the problems with usage and adoption. For example, most organizations are past the experimentation stage, yet few have tied their AI together.

• Forty-nine percent are deploying AI agents that complete tasks or workflows with some autonomy.
• Just 15% have embedded AI agents across multiple systems and interfaces so they work seamlessly across the organization.

Toggling between AI and other work tools costs about 22 business days a year

Gaps that result from a failure to coordinate AI efforts show up as lost time. Lots of it.

• Executives estimate employees lose an average of 3.6 hours a week switching between AI tools and the other systems they use to get work done. That's nearly 10% of every workweek.
• Over a 50-week year, this lost time adds up to about 181 hours, or roughly 22 business days per employee.
• Only 11% say their AI tools sit in one integrated ecosystem and cost no time at all.

Executives want AI that works across the team

When asked to imagine how their respective teams would use AI in an ideal world, executives gravitate toward collaboration and teamwork.

• Seventy-two percent say they would choose AI that works across the team with shared context so value compounds and builds on itself.
• Twenty-six percent note that they would have each person use AI on their own, with the value staying with that individual.

For some leaders, the appeal of transitioning to a more cooperative framework comes down to efficiency across functions.

"Shared team workspace with collaborative AI context prevents redundant prompt engineering and accelerates cross-functional project delivery," said one financial services director.

Slack GM Rob Seaman put it this way: "Share context, build on it together, and generate more context as you go. When an agent works in a shared space instead of a private chat, it picks up how your company actually operates - the projects, the debates, the decisions, all living in the conversations your team is already having. Every exchange with an agent out in the open becomes something the next person, and the next agent, can build on."

Explore further

• Read Rob Seaman's essay, Multiplayer AI: The Shift from Smarter Individuals to a Smarter Company

Methodology

Data are from a double-blind Salesforce Pulse Survey of 250 U.S. executives, director level and above, who have decision-making authority over AI investments at their organizations. Data was collected from September 14 through 22, 2026. Average weekly hours lost were calculated using the low end of each response range and annualized across 50 workweeks. Percentages are rounded and may not sum to 100%. SMBs were defined as companies with fewer than 250 employees; enterprise businesses were defined as those with 2,500 employees or more.