Hybrid collaboration in 2026: How AI and the Collaboration Quotient enable hybrid equity

How AI and the Collaboration Quotient are reshaping equitable, outcome-led hybrid work

As organisations reassess how and where work gets done, hybrid models continue to evolve rather than setting into a single standard.

Hybrid work has moved from policy debate to performance reality. The question now is not whether teams are remote or in-office—it is whether every voice is captured, decisions move faster, and AI is improving outcomes rather than reinforcing inequity. What is increasingly clear is that sustaining hybrid work at scale requires more than policies or presence - it requires rethinking how collaboration actually functions in an AI-enabled environment.

While many organisations have invested heavily in collaboration technology, the hybrid gap persists. The reason is not only technical. It is also human—shaped by behaviour, bias, and how collaboration is recognised and rewarded. The opportunity now is to design meetings and workflows where technology reliably captures every contribution and AI helps rebalance participation and continuity across distributed teams.

Meet the contributors:

• Peter James, Vice President of Global Business Development, Shure Incorporated
• Mo Jamous, CIO, U.S. Bank
• Mollie Brentnall, IT Director, Crawford & Company
• Laura Prietula, CEO, Seldon Advisory Services

The hybrid gap and hidden cost of unequal experiences

One of the most persistent drivers of hybrid inequity is proximity bias–the tendency to value the people we see most often. It shows up subtly, but consistently, in who gets recognised, who is trusted, and whose work is remembered.

Mo Jamous, CIO at U.S. Bank, observes how many organisations underestimate how deeply ingrained this bias is:

“We’ve convinced ourselves that hybrid work is ‘fixed’ because we upgraded conference rooms and bought better video solutions. But the real issue isn’t technical, it’s human. We’re wired to value the people we see.”

This is compounded by how decisions and promotions actually happen. Despite formal processes, career progression is often influenced by informal moments, like hallway conversations, quick side chats, or casual exchanges before meetings. When those moments are still centred around the office, remote employees are structurally disadvantaged, even in organisations with the best intentions.

As Laura Prietula explains, the gap is not just about visibility, but about the nature of interaction itself: “There’s a lot of conversations that happen in the hallway… conversations that may be so private or… tenuous… that you can’t really have them remotely. You kind of have to go into an office together.”

Mollie Brentnall, IT Director at Crawford & Company, highlights how this disproportionately affects certain groups:

“Minority groups, especially those with caring responsibilities or who are disabled, are less likely to be present in offices, and this runs the risk that their work isn’t seen as impactful and their names aren’t mentioned as often in decision-making rooms.”

Prietula further highlights how fundamentally different collaboration dynamics are across environments:

“When you’re remote it’s one person speaking at one time versus when you’re in a conference room… you can have some of the most vivid conversations, everybody piling onto the subject.”

Without deliberate action, hybrid work can quietly reinforce the very inequalities it set out to solve.

Why technology alone doesn’t solve the problem

The first wave of hybrid work focused on access: enabling people to join meetings remotely, share documents, and stay connected. While necessary, access alone does not guarantee equitable participation.

In practice, moving from hybrid access to hybrid equity depends on the fundamentals: every voice captured clearly, every participant represented accurately, and every room experience consistent enough to scale. That means designing for real-world conditions (multiple speakers, cross-talk, background noise), ensuring platform interoperability, and making deployments manageable across an estate—not just equipping a handful of showcase rooms.

Prietula frames this as a design challenge rather than a tooling issue: “One of the biggest challenges is how do you humanise technology so that humans can be engaged and still be able to participate in multidisciplinary and multi-channel activities.”

According to Peter James, Vice President of Global Business Development at Shure Incorporated, the problem lies in assuming that all collaboration looks the same.

“Understanding how people come together and for what purpose helps you understand how to position technology to support them.”

Teams operate in fundamentally different ways. Some operate through structured, recurring meetings; others are fluid and project-driven. Applying a uniform set of tools across these distinct collaboration patterns can lead to uneven outcomes, even in hybrid settings.

This insight led to the development of the Collaboration Quotient (CQ)—a way of assessing how well people, processes, and technology align to create effective collaboration, and how that collaboration translates into business outcomes. In IDC research terms, collaboration becomes an ROI amplifier: when collaboration quality rises, other investments (including AI) deliver more value because decisions move faster, context is retained, and execution friction falls.

At Shure, Peter James describes Collaboration Quotient as a way of assessing whether tools support real collaboration—not just meeting attendance—and whether that support shows up in outcomes. Jamous offers a more operational lens, pointing to time-to-consensus as a critical measure. Hybrid meetings often spend significant time recapping context and aligning participants, slowing decision-making and increasing friction.

In identifying how teams collaborate (and where friction or imbalance exists) organisations can make more intentional decisions about technology placement and measurement. AI becomes valuable here not as an add-on, but as an adaptive layer that responds to context rather than forcing uniformity.

Trust as the foundation of hybrid collaboration

Trust sits at the centre of effective hybrid work. In an article for Harvard Business Review, Professor Tsedal Neeley argued that trust in employee autonomy is central to this: “All of the early data show that when people are forced into the office, they resent it. They say, you trusted me during the pandemic. Why don’t you trust me now?”

Hybrid meetings are one of the most visible trust pressure points. Even when everyone is technically “present,” experiences can vary widely depending on who is heard, who contributes and whose input shapes the overall outcomes.

James emphasises the role of voice:

“Voice is how we communicate. It’s central to every collaboration… voice has always been so powerful that in some ways it's been ignored and accepted as always present and always standard. And so whilst technology has improved and some things have become easier, I think the real power of how we manage voice content and what we can do with that, how AI can process that to much better outcomes and to much more business and commercial impact and others as well, is really quite special now.”

As AI increasingly transcribes, summarises and analyses meetings, audio quality takes on new importance. Voice is no longer only a communication medium—it becomes data, and the quality of that data influences how accurately contributions are captured, attributed and represented. When capture is inconsistent, AI summaries and action items can skew toward the loudest voice or the closest microphone, amplifying the very inequities hybrid work is trying to solve.

“Poor quality data is not so valuable. So great quality audio provides great quality data.”

In practical terms, this means that equitable hybrid collaboration depends on getting the fundamentals right. If AI is expected to support inclusion, insight, and continuity, it must be built on reliable, high-quality inputs.

Surveillance and responsible AI

While AI adoption is accelerating, trust remains fragile.

Employees want clarity on how AI is being used, what data is captured, and whether systems are designed to support them or monitor them. Jamous cautions about a common misstep: “One of the easiest traps to fall into with AI is to use the technology to watch your people instead of helping them.”

When AI is perceived as a surveillance tool, trust erodes quickly. High-performing hybrid teams depend on trust, and once that trust is broken, technology alone cannot repair it. James advised: “Be clear about what is going to do, how you're validating it, how the outcomes are validated, how people can believe that they can trust the AI tools that are being used.”

Brentnall and James both stress the importance of transparency and AI literacy. AI systems reflect the data and assumptions behind them, including existing biases. Without education, governance, and accountability, organisations risk automating inequity rather than addressing it. They reiterate that responsible AI use is not about slowing innovation. It is about ensuring alignment with organisational values and building confidence in how decisions are supported.

Measuring what really matters in hybrid collaboration

Measuring success in hybrid environments remains challenging for leaders. Productivity metrics alone offer an incomplete picture.

As previously mentioned, Collaboration Quotient provides one lens by linking collaboration effectiveness to outcomes rather than activity. Jamous adds a more operational measure: time-to-consensus. Hybrid meetings often lose time aligning context and repeating information, especially when meeting artefacts (notes, decisions, actions) are fragmented across tools or captured inconsistently. A report by Craft Docs surveyed 2,000 US remote and hybrid workers and found that 72 percent of respondents thought that at least one of the meetings could have been an email instead.

AI can reduce this friction by preserving context, summarising prior decisions and allowing meetings to focus on judgement rather than recap. James commented: “AI has the potential when you augment existing workflows and apply it properly to move towards this nirvana where no matter how you meet and in what format your meeting takes place you can leverage all of the tools in the same way.”

AI has the potential to fundamentally change this dynamic by reducing ramp-up time and focusing human attention on the decisions that truly require debate and judgment.

Brentnall highlights another important indicator: empowerment. At Crawford, employee surveys help capture whether people feel enabled and supported in how they work, a signal that technology is enhancing, rather than hindering, collaboration.

Additionally, in order to close the hybrid gap, organisations must understand their collaboration and meeting archetypes, position tools correctly, and measure outcomes deliberately.

As James advises, the payoff is clear: “Better meetings, better productivity, and ultimately the best commercial outcomes.”

Prietula points to AI’s emerging role not just as a productivity tool, but as a participation equaliser: “You can have an AI agent capturing what it’s listening to, summarising from the conversation and what the online participants might be experiencing.

“It’s about bringing the AI so that the people on the other side of the screen can actually be part of those conversations without waiting for their turn.”

From hybrid access to hybrid equity

AI alone will not close the hybrid gap. But when it is embedded into workflows, designed around real collaboration patterns, and governed with care, it can help organisations move from basic access to genuine equity of experience.

Looking ahead, Jamous believes the most successful organisations will stop trying to replicate the office online and instead design for asynchronous fluency. This ultimately means using AI to handle routine coordination and freeing people to focus on judgment, creativity, and relationships.

As Brentnall puts it, tools will continue to evolve, but success will always come down to putting people first and giving them what they need to do their best work.

Prietula stresses that success will depend not just on tools, but on mindset and education: “The most important thing has been—and continues to be—education and AI literacy. If you don’t understand what it is, you need to experience it in a safe environment.”

This article was created in partnership with Shure.

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