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The Microsoft New Future of Work Report 2025 has a problem: none of the workplace experts who would actually implement its central concept can consistently define it.
The report, published in December 2025, argues that collective productivity — how teams, organizations and communities "get better together" — represents the next strategic frontier after individual AI productivity gains. AI will bridge "gaps of time, distance and scale,” so distributed teams pursue shared goals even when separated by geography or organizational silos. Microsoft Chief Scientist Jaime Teevan frames this as the natural evolution of five years of research tracking remote work, hybrid arrangements, large language models and individual productivity gains.
But ask workplace experts what collective productivity means in practice, and the answers reveal fundamental disagreement, not just about implementation, but about the definition of the concept itself.
Defining 'Collective Productivity' — or Trying to
The clearest operational definition comes from Steve Osler, chief executive of Wildix: "Collective productivity is direction, not just speed,” he said. “In practice, it's when AI and collaboration systems reduce handoff friction, improve team decisions and compound knowledge so the group gets better together, not just each person gets faster."
That sounds straightforward. However, Matt Adams, organizational development director at Pluralsight, describes something different. "Collective productivity is best described as a technological evolution of a classic social ambition," he explained. What's changed is that AI agents have made team structure "elastic and no longer fixed by human headcount,” so teams "assign a fleet of digital agents to bridge capacity gaps and scale outcomes at a velocity that was previously unattainable.”
For Pranav Dalal, chief executive and founder of Office Beacon, both definitions fall short. "It's not about everyone doing more work together,” he said. “It's about removing the friction that forces smart people to operate in isolation." The result is fewer handoffs that stall decisions, fewer tools competing for attention and fewer incentives rewarding personal wins at the expense of team outcomes.
The chief architect with JAMS Pathways, Richard Birke, takes yet another angle. Collective productivity "suggests that people's compensation and benefits are tied in some significant measure to the overall health of the organization — that individual efforts are all seen as connected to something that transcends the work of any individual.”
Then there's Jason Wild, former vice president of growth and innovation at Microsoft, who questions whether productivity should be the conversation at all. "If we tell people the future of work is about productivity, we're essentially saying the future is a more efficient version of the present," he argues. "That's optimization. Where's the vision?"
Drawing on his time at IBM, where CEO Ginni Rometty observed that productivity was becoming "table stakes,” Wild distinguishes between "shoulds" — performance management, hitting targets, improving efficiency — and "coulds": the organizational capability to identify and close opportunity gaps that represent growth. If collective productivity simply means everyone being more efficient together, it's "a modest ambition,” he said. “True transformation means building "the collective capacity to do things you couldn't do before.”
So collective productivity is simultaneously about reducing friction, scaling capacity with AI agents, restructuring compensation and pursuing transformational growth rather than incremental optimization. If the people who would implement collective productivity can't define it consistently, what are organizations being asked to pursue?
The Metrics Problem Nobody's Solved
The problem thickens when the conversation turns to measurement. How do you track collective productivity when you can't agree what it is?
"Measuring an individual's output is like trying to determine which drop of water made a bucket overflow," Adams said. His solution involves continuous 360-degree feedback focused on "orchestration quality" — identifying who created prompt libraries or designed time-saving agent workflows. "Performance metrics will have to shift from looking for star players to identifying star architects."
But this assumes collective productivity can be measured through individual contributions to collective capability, which Wild argues misses the larger structural problem. "Most organizations still recognize individuals as heroes," he said. "Annual awards, performance rankings, bonus structures — they all reinforce the idea, intentionally or unintentionally, that success is personal."
Leading organizations need to "recognize teams as heroes" instead, which requires leaders to become architects, bridgers and catalysts, Wild said. Or, as Osler put it, "Organizations need to stop scoring solos and start scoring the song." Activity-based metrics favor people with more visibility or better tooling when AI assistance is distributed unevenly.
This goes back to grading team projects back in school, Birke said. Students consistently complained about "free riders" and inadequate recognition for individual contribution. "We're used to individual efforts and individual rewards,” he said. “Even in a team sport like basketball, pay is different between players based on individual accomplishment."
Dalal feels the problem is more fundamental. "I've seen teams lose days debating who needed to respond, when the real issue was that no one owned the decision,” he said. For him, the metrics conversation is premature. "When ownership and systems are clear, productivity compounds instead of fragmenting."
How Hybrid Work Exposes the Cracks
Microsoft positions AI as bridging time and distance, moving organizations from "How can AI help me?" to "How can AI help us?" But this assumes collective productivity means the same thing whether teams are collocated or distributed, an assumption experts reject.
"If the process depends on being nearby, it's already broken,” Osler said. “AI will just break it faster." Casualties are "drive-by" workflows — quick approvals, impromptu coaching and alignment come from physical presence. His remedy centers on a simple principle: "In hybrid environments, the decision isn't real until it's readable."
Governance further complicates things. Osler insists on consistency: "We believe in one workforce, one rulebook. This can include different workflows, but not different rights." Location-based AI rules "quickly become inequitable and confusing in hybrid reality.”
But Dalal disagrees. While principles remain consistent, implementation cannot. "Remote teams need clearer expectations around documentation, transparency and how decisions move forward without real-time discussion,” he said. “In-office teams can often recover from ambiguity through conversation. Remote teams rarely can."
Proximity bias lurks beneath these disagreements. "AI can worsen proximity bias if in-room conversations generate more 'visible signal' — more meetings, more off-the-record alignment — than remote contributions,” Adam said. Process redesign helps, by publishing "agendas, AI notes, decisions and action items to the same shared channel by default,” Osler said.
But this as a design challenge, not a technology problem, Dalal said. "AI trained on biased inputs will reinforce biased outcomes,” he explained. “If promotions, recognition or influence still flow to those who are most visible, AI will optimize for that visibility." Instead, he recommends better process. "Make contributions traceable. Make outcomes measurable at the team level,” he said.
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The Unaddressed Expertise Erosion
A question largely sidestepped in Microsoft's report concerns what happens to human expertise when AI handles collective work.
"As teams lean into collective productivity, there is a risk that the individual 'muscles' of human expertise and skills begin to wither,” Adam warned. If AI agents handle reasoning and execution, humans lose "the deep domain knowledge required to critically audit those agents.” His solution is "designing for reflection" — intentionally maintaining human cognitive engagement to prevent organizational intelligence from becoming "a black box that no one truly understands or controls.”
But if collective productivity means AI agents handle execution while humans provide direction, what happens when humans lack expertise to evaluate AI output? Does collective productivity then become collective dependency?
Birke offers a historical perspective, citing Douglas MacGregor's 1966 article "The Human Side of Enterprise." MacGregor's concern is whether collective productivity initiatives foster connection to organizational mission or create new forms of alienation. Five decades of management theory about connected, mission-driven work hasn't prevented the problems Dalal describes — teams losing days debating ownership, smart people operating in isolation, incentives rewarding personal wins.
"Productivity can be a source of reinvestment into innovation and strategic projects,” Wild said. “When you free up capacity through efficiency, you create the fuel for growth." But he's equally clear about the limitation: "Productivity alone won't enable industry-level or world-level change. If all you're doing is optimizing, you're running in place."
"Asynchronous work shouldn't create slower communication,” Dalal said. “It should, however, create better thinking." When teams document decisions and use AI to summarize what matters, "work keeps moving without constant check-ins.” But this requires intentional design, not passive tolerance of new tools.
An Aspirational Vision Brought Down to Earth
The problem isn't that organizations are unprepared for collective productivity. It's that collective productivity remains undefined beyond Microsoft's aspirational framing of teams "getting better together.”
Microsoft's report positions collective productivity as the culmination of five years researching the digital evolution of collaboration, with organizational culture and leadership as enablers. But most companies haven’t begun to undertake the organizational changes required first: redesigned metrics, formalized decision-making, maintained expertise and equitable hybrid processes.
Perhaps that's the point. Collective productivity isn't a destination organizations reach by deploying the right AI tools, but an ongoing negotiation about what collective work means, how it gets measured, whom it serves and whether it represents transformation or merely optimization. Until those negotiations produce some consensus, collective productivity remains what it's always been: an aspirational buzzword searching for a definition.
Editor's Note: What does it take for teamwork to thrive — whether in office or hybrid?
- The Daily Behaviors That Turn Good Teams Into Great Ones — The path from functional to exceptional teams isn't mysterious. Most have the talent they need. What they lack are daily behaviors that create cohesion.
- AI Is Pushing Organizations to Finally Redesign Work for Hybrid Teams — Hybrid work wasn’t redesigned. It was relocated. AI is now surfacing the inequities leaders hoped would stay hidden.
- Why Nobody Agrees on AI Productivity Metrics — As AI reshapes work, companies want new productivity metrics. The problem? Nobody can clearly define or fairly measure the alternatives.