Our Blog

The Productivity Illusion: Why Individual Gains Don’t Equal Business Success

The numbers initially look promising. Daily AI usage has nearly doubled in just one year, with workers reporting an average 33% productivity boost and saving approximately 1.3 hours per day. The share of people who consider AI useless has plummeted by 78%, while those viewing AI as a strategic partner jumped 27%. Leaders have embraced this shift too—74% of knowledge workers say their leaders now foster safe environments for AI experimentation, up from 60% in 2024.​

But here’s where the story takes a troubling turn. Despite these impressive individual gains, 96% of companies have not seen dramatic improvements in organizational efficiency, innovation, or work quality. Only 3% of executives report transformational improvements in organizational efficiency, a mere 2% see dramatic work quality improvements, and just 4% have achieved innovation breakthroughs. Zero IT leaders—not one—report that work quality across all teams has dramatically improved.​

The Fatal Flaw: Why AI-Powered Productivity Without Coordination Fails

The root cause? An overemphasis on AI-enabled personal productivity at the expense of coordination. A staggering 76% of executives view increased employee productivity as the primary indicator of AI ROI. But research reveals this focus is fundamentally misguided. Organizations hyper-focused on personal productivity as the main AI outcome are 16% less likely to drive organization-wide innovation compared to those focused on coordination.​

When coordination isn’t prioritized, individual productivity gains can actually worsen existing problems. 37% of executives admit AI has wasted their teams’ time or led them in the wrong direction. The disconnect is stark: 42% of workers admit to trusting AI outputs without verifying accuracy due to time pressures, despite only one in three people fully trusting AI. Perhaps most concerning, one in three knowledge workers use unapproved AI tools for work tasks—and the true number is likely higher. When teams work with disconnected AI tools, silos deepen and security risks multiply.

​

The Elite 4%: Three Strategies That Separate Winners from Losers

So what separates the 4% of organizations achieving transformational benefits from everyone else? Companies focused on AI-enabled coordination are nearly twice as likely to report that AI has significantly transformed organization-wide efficiency. These elite performers do three things differently:

​

1. Build a Connected, Company-Wide Knowledge Base

The transformational companies understand that 79% of knowledge workers would use AI more if it could access the right data and information. They’ve fundamentally changed how teams work by making knowledge available to AI at the core of everything they do.​

These organizations adopt “AI-first collaboration practices”—brainstorming in digital whiteboards, collaborating in shared pages, and including AI notetakers in live meetings to give AI context without additional work. At Atlassian, for example, they start each project with a clear “project poster” documenting the challenge and intended impact, enabling AI to better steer teams in the right direction.​

Crucially, they give AI accurate context by documenting high-quality information with clear owners, tags, and statuses. By marking pages as “draft” or “verified,” teams help AI—and other teams—know what information should and shouldn’t be shared across the organization.

​

2. Set Up the Right Systems to Enable AI-Powered Coordination

Winning organizations document 3-5 clear goals per team within a centralized platform, laddering each one up to department- and organization-wide milestones. When AI knows every goal, it can drive teamwork in the right direction, quickly flag duplicative work, and connect the right people, projects, and knowledge.​

They adopt integrated systems of work, recognizing that silos limit AI’s ability to provide insights and direction. As one Fortune 500 SVP explains: “To deploy enterprise AI at scale, you need to have the plumbing set up to get the data flowing through the systems properly”.​

These companies also establish clear policies to accelerate confidence in AI. They create transparent guidelines and smaller community spaces where people feel safe asking questions, avoiding overly punitive approaches that stifle experimentation.

​

3. Make AI Part of the Team

Transformational organizations empower every team—not just technical teams—to experiment with AI. Companies that give all teams freedom to use AI, even if their strategy isn’t fully defined, are twice as likely to make innovation gains than slower adopters.​

But here’s the counterintuitive finding: formal training and self-serve knowledge hubs are among the least effective ways to spark strategic AI collaboration, despite being the most common approaches (offered by 69% and 57% of organizations, respectively). Instead, AI learning happens best in small, active communities organized around shared problems—champion-led workshops with live demos or hackathons where teams integrate AI into specific workflows.​

The data reveals that managers are pivotal to driving effective adoption. Knowledge workers who have seen their manager model AI are 4x more likely to consistently experiment with AI and 3x more likely to be strategic AI collaborators.