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The debate between remote and office work was supposed to be settled. Instead, artificial intelligence (AI) is exposing a harder truth: most organizations never redesigned work for hybrid teams. They just changed where people sat.
The same workflows that work for office-based teams fail remote workers, not because the technology differs, but because the underlying processes were never built for distributed work.
The gap between AI's promise of unified productivity and the reality of proximity bias is widening, forcing leaders to confront whether their hybrid evolution is transformation or window dressing.
Table of Contents
- AI Exposes Proximity Bias
- Governance, Not Tools
- The Invisible In-Office Safety Net
- How AI Is Failing in Performance Measurement With Hybrid Teams
- Redesign Hybrid Work Before AI Redesigns It For You
AI Exposes Proximity Bias
Poorly designed AI implementation first breaks decision-making, performance evaluation and informal coordination. “AI doesn't remove proximity bias. It automates it unless you design against it,” said Elika Dadsetan, founder and executive director at Enroot and former CEO of a global leadership and DEI organization.
Dadsetan’s analysis draws on decades working with institutions including the United Nations and World Bank. These systems focus on what's easiest to measure and most visible, which in many organizations still means the people in the building. "Without deliberate design, you end up with a digital version of 'face time culture,'" she warned.
The stakes are measurable. Research from the Workforce Institute found that organizations with high levels of proximity bias experienced 23% higher turnover rates among remote employees and 18% lower engagement scores across all employees, according to a 2024 study from workplace technology firm Monitask.
NEOM chief futurist Kate Barker sees this playing out in her client work. Organizations may claim to have moved beyond return-to-office mandates, yet in practice they've simply relabelled attendance rules with softer language.
"Presence is still being privileged over outcomes, synchronous meetings over deep work and compliance over trust," Barker said. "This creates the illusion of progress while preserving legacy control structures."
AI improves hybrid work where it reduces coordination costs and clarifies priorities, Barker said. "It adds noise where it is layered on top of broken processes, used as a proxy for visibility or deployed as an implicit monitoring mechanism,” she added.
Riccardo Soff, co-founder and CEO of eDev, supports distributed engineering teams across time zones. "Visibility should be designed around transparency and trust, not surveillance, because distributed teams only function when autonomy is preserved," he said.
Governance, Not Tools
Most organizations don't need different AI tools for remote and office workers, but different rules, norms and decision rights. "This is a governance problem, not a software problem," Dadsetan said.
Shared AI tools require shared reliable information but stronger governance around human overrides, said Mohamed Yousuf, CEO and co-founder of Smart Workforce AI. These overrides happen more easily in office settings when decisions get made quickly, creating structural advantages that compound over time.
Barker's assessment is pointed: Asynchronous work requires redesigning decision rights, escalation paths, performance metrics and leadership cadence. Without that change, async becomes a burden shifted onto employees, extending the workday rather than improving productivity.
Yousuf recommends requiring outputs such as decision memos, issue tickets and AI-generated summaries reviewed by humans, so decisions come from the work itself rather than who happened to be in the room. "The goal is to use AI in daily work processes where there won't be an effect whether the employee is on-site or remote," he explained.
Soff's experience reinforces this. Teams that succeed globally do it by redesigning work from the ground up because time zones make old habits impossible. "Allowing remote days without redesigning workflows often results in the same synchronous expectations layered on top of distributed teams," he said.
The Invisible In-Office Safety Net
Customer-facing and compliance-heavy processes fail first when workflows aren't differentiated by location, said Tim Mobley, president at Connext Global. "That's because AI output will often appear complete or correct, but it still needs human judgment, and in-office teams have a safety net that allows them to catch issues informally," he said. "Remote teams usually don't have that unless review steps are intentionally built into the workflow."
In collocated teams, micro-checks act as an invisible layer of quality control. Ambiguity gets resolved informally — people overhear conversations, ask quick follow-up questions or sense when an AI-generated recommendation doesn't fit, and correct it in the moment.
Remote teams need clearer guardrails: explicit rules for when AI output can be accepted as-is, when it must be reviewed, what "review" means, fact-checking, domain validation, stakeholder alignment and who is ultimately accountable.
"Without that structure, remote teams either over-trust AI and miss misfits or they lose speed through second-guessing and rework after decisions are already in motion," Mobley warned.
"If your process relies on being in the room, your process is already broken for half your workforce,” Dadsetan said
Speed advantages become structural inequities. Collocated teams quickly get required signoffs, while remote workers often must wait for meetings. AI closes this gap by collecting inputs over time, offering suggestions, then allowing human review without requiring everyone's presence.
How AI Is Failing in Performance Measurement With Hybrid Teams
When visibility differs by location, organizations cannot pretend that output, effort or contribution get seen equally.
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"You have to move from measuring presence and responsiveness to measuring clarity of goals, quality of outcomes and reliability of delivery," Dadsetan explained. "Or more simply: if you're still rewarding who looks busy instead of what gets built, AI will make that problem worse, not better."
Performance should be measured by outcomes such as quality, error rates and resolution effectiveness rather than responsiveness or presence, Mobley said. AI creates the illusion of effort, but it doesn't prove impact.
The metrics challenge is particularly acute for technology leaders navigating hybrid teams. Research from Cisco's Global Hybrid Work Study shows that while 90% of employees say they're just as or more productive in hybrid work, managers report productivity confidence dropped from 79% in 2023 to 62% in 2024, revealing a persistent measurement gap that AI visibility systems often fail to bridge.
Barker cautions against equating progress with utilization rates or badge data. Where hybrid redesigns are genuine, organizations have measurable improvements in employee experience, decision velocity, burnout reduction and quality of output.
Soff's platform data reveals similar patterns. "Across supported teams, improvements show up when developers stay longer, ramp faster and deliver predictably, signalling that the system is working for people, not just leadership narratives," he said.
The backbone of fairness and scale for remote teams is asynchronous work, Dadsetan said. AI helps summarize, route and document, but only if the organization stops relying on real-time interaction as proof of engagement.
Redesign Hybrid Work Before AI Redesigns It For You
The future isn't one or two processes, Dadsetan said. "It's one set of principles expressed through different mechanics,” he said. “Equity does not mean sameness. It means designing for different conditions with the same standards."
For technology executives and HR leaders, without redesigned workflows and measurement frameworks, AI tools amplify existing inequities rather than solve them. Owl Labs' 2025 State of Hybrid Work report found that 80% of employees now use AI in the workplace, up from 72% in 2024, whilst CloudZero's survey of 500 engineering professionals revealed that organizations planning to invest more than $100,000 per month in AI tools will more than double from 20% in 2024 to 45% in 2025. As AI adoption and investment grow, the window to build fair governance structures is narrowing.
The question facing organizations isn't whether AI will unify hybrid work, but whether leaders will redesign work intentionally before AI redesigns it in ways that reinforce the inequities hybrid work was supposed to solve. "This is not a tooling moment," Dadsetan said. "It's an operating-model moment."
Hybrid work hasn't failed, but much of its execution has stalled, Barker said. Organizations that close the gap between rhetoric and reality will be those willing to measure what matters rather than what's easiest to see.
Editor's Note: Need tips on redesigning work for hybrid teams?
- Mapping the Hybrid Experience: An Exercise in Human-Centered Workplace Design — Gaining empathy for individual perspectives through mapping can help you create more successful employee experiences.
- CEOs Blame Work From Home for Company Failings. Here's Why They're Wrong — Remote work has become a scapegoat for all sorts of company shortcomings. Here's what those claims leave out.
- The Hybrid Employee Experience in 2025: Results From a Global Study — Organizations still struggle to set up effective hybrid work models for individuals and teams. A recent study found adopting 3 foundational practices can help.