Asynchronous collaboration tools can reduce costs, improve manageability and combat meeting fatigue. Learn how to choose and adopt the right tools for your distributed teams.

Even as employees return to the office, video meetings are still the primary way teams communicate and collaborate with each other, customers and partners. But a meeting-heavy culture can mask collaboration and operational inefficiencies and contribute to meeting fatigue. Asynchronous collaboration tools offer the potential to address these issues by bringing flexibility to employee engagement and work management.

What are asynchronous collaboration tools?

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Ask 10 companies who owns their AI strategy and, until recently, you likely would have gotten 10 different answers. That’s changing fast. Most companies now have, or plan to have, a single executive in charge of AI companywide, and that structure correlates directly with getting results.

The ownership decision extends to more than just AI strategy, and those involved carry significant influence. In fact, companies’ AI organizational structure directly relates to whether their investment in the technology pays off. The companies in Metrigy’s Research Success Group (those posting above-average business improvements from AI) are consistently the ones that have named an owner, built a team, and recognize the difference between who sets strategy and who funds the investment. .

The Rise of the AI Title

Go back a year and just 26.8% had a single executive in charge of AI strategy at all. Now, that number has reached 35.7% (the Success Group is already up to 48.1%), with another 37.7% still planning to add this year. Among those with a single executive in charge, 48.1% have specifically tapped AI-titled leaders, including Chief AI Officers, AI directors, AI managers, and AI strategists. 

Previously, the CEO or COO added AI strategy ownership to the already-full plate of CIOs or CTOs. That has now leveled off at 35.9% of companies who have single person leading AI strategy. As stated, the success group is further down this road, not only with more AI strategy leaders today, but with 41.2% more planned this year. Designated AI strategy leadership correlates with success.  A dedicated owner has the authority to kill weak projects, standardize tooling, and drive organizational excellence. Those are exactly the decisions that separate companies seeing solid ROI from companies burning budget on pilots that never ship.

There’s a quieter benefit, too. When one executive owns AI strategy companywide, employees know where to take an idea, a concern, or a failed pilot. Without that, promising proposals die in the gap between departments, and weak ones may sail through because no one has the standing to challenge them. A successful CAIO creates a clear decision path for AI projects, whether they’re in pilot or expansion mode.

Strategy vs. Technology Decisions

Naming a CAIO doesn’t mean handing that person the AI vendor decision-making. In most companies, the AI executive owns strategy while the CIO or CTO organization owns the technology purchasing decisions.The funding can come from either group, or a separate business unit.

That split can be beneficial, where the AI leader sets direction: what problems to solve, what’s in bounds, and what good looks like. The technologists who live with integration, security, and support handle selecting the right technology provider. Companies that blur the two roles tend to either buy tools nobody asked for or set strategy they can’t implement. Keep the strategist strategizing and the technologist selecting products, and you sidestep both traps.

Centers of Excellence: The Missing Muscle

If a single owner is the brain, a Center of Excellence (CoE) is the muscle, and most companies haven’t built it yet. Fewer than 30% have an AI CoE today, which is a thin showing for a capability this central to the business.

Here, the Success Group pulls ahead again, with 43.5% running a CoE, vs. 27.1% of the Non-Success Group. A CoE is a cross-functional team that supplies expertise, best practices, training, and governance—the connective tissue that keeps AI decisions consistent instead of every department reinventing the wheel. Companies without one aren’t doomed, but they become much less efficient and streamlined. They’re asking individual project leads to sort out governance, tooling, and skills on their own. That’s slow, and it’s wildly inconsistent.

What does a CoE do all day? In the companies that run one well, the CoE:

That work may be unglamorous, but it’s also the difference between AI that scales and AI that stays stuck in a few motivated pockets of the company.

The reporting line matters, too. Most commonly, the CoE reports to the CIO or CTO. That keeps it close to the technical resources it needs while the CAIO carries the strategic agenda.

Why Structure Shows Up in the Results

Skeptics may call this bureaucracy dressed up as progress, but the data says otherwise. Consider contextual awareness, which measures whether a company’s AI systems carry what they learn from one workflow or interaction into the next instead of starting cold every time. Only 35% of companies overall have that kind of full continuity. But 51.0% of companies with an AI CoE do, and 48.1% of those with full continuity have a single AI exec in charge of strategy. Metrigy’s research shows that this organizational discipline results in  AI that works better–because a team, backed by named leaders, are accountable for making it work better.

What CX and IT Leaders Should Do

If AI ownership at your company is still spread across three executives and four committees, you have a structure problem, and it’s probably costing you. Where I’d start:

•    Name a single owner. One executive accountable for AI strategy companywide, with the authority to set standards and stop projects.

•    Separate strategy from technology decision-making. Let the AI leader set direction and the CIO or CTO own the provider decisions.

•    Stand up a Center of Excellence, even a small one, and give it responsibility for best practices, training, and governance across teams.

•    Report the CoE into the CIO or CTO, with dotted line to the CAIO or AI strategy leader, so it stays close to technical resources.

•    Measure whether it’s working continuously. Track context retention, project completion, and ROI, and hold the owner to them.

The companies winning with AI didn’t get there by buying more tools. They got there by deciding who’s in charge, then giving that person a team. If you’re still asking, “Who owns AI around here,” that’s your first project.

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Enterprises deploying artificial intelligence (AI) in their workplace collaboration (WC) platforms face an early architectural choice: focus on enabling the AI capabilities built into the collaboration app, or share collaboration data with an external frontier model and its tools such as Claude or ChatGPT. Metrigy’s AI in Workplace Collaboration: 2026-27 study of 759 organizations found that 57.6% are already using AI within their WC platforms, and a slight majority prefers the in-app approach over connecting to frontier models directly. That preference correlates with success including measurable business results, including cost savings, improved employee efficiency, and higher revenue.

AI in WC Adoption

As noted, 57.6% of all participants have adopted AI within their workplace collaboration apps. Successful companies have a 43.8% higher adoption rate. Company-wide deployment also grows with organization size, from 20.2% at companies under 250 employees to 31.6% at companies over 2,500. Despite AI hype, the largest share of companies remains in pilots or evaluation, which indicates that workplace collaboration AI is still moving from limited rollouts toward broad production use as companies get comfortable with capabilities, ensure security, governance, and compliance, and understand costs.

Using AI capabilities within the WC platform, rather than only sharing WC data with frontier models, correlates with success. These in-app capabilities typically include chat and meeting summarization along with an agent that can query WC data, and they vary by vendor to include IVR or virtual receptionist agents, workflow studios, task automation, and meeting facilitation.

Spending

Nearly 63% of organizations are increasing their AI in WC budgets in 2026, with an average increase of 23.4%. The share of companies increasing spend is 44.1% higher in the success group. Budget growth is lowest among the smallest companies and highest in the two larger size bands.

Improving customer satisfaction and increasing employee efficiency are the primary goals for AI in WC, and increasing sales closely correlates with success. Employee productivity is the most-measured key performance indicator (KPI).

Use Cases That Correlate With Success

Automating workflows is the largest use case for AI in WC, and it is also the strongest success correlation in the study. The success group reports more use cases overall than the non-success group. Workflow studios, which let individuals create custom agents and automations, are in use or planned at 68.1% of organizations, with adoption 50.1% higher in the success group.

The success group applies more resources to skills and knowledge than the non-success group. Successful companies train employees more and are more likely to establish communities for knowledge sharing.

Making the Decision

The data supports in-app AI as a starting point, but it does not point to a single approach for every use case. Companies standardized on Microsoft or Google for collaboration may find the in-app capabilities sufficient for summarization, querying, and common automations. Others, especially those in mixed-vendor collaboration environments, may decide that frontier-model workflow tools better meet requirements, especially as integration capabilities continue to rapidly improve.

Realizing measurable value from AI in workplace collaboration requires selecting capabilities that match documented use cases, provisioning budget against the goals the organization actually measures, and building the training and knowledge-sharing practices that separate the success group from the rest.

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Nefarious actors are harvesting encrypted data to decrypt it once quantum computing gives them the ability to do so. Identify data risks to reduce them.

Q-Day, the day when quantum computing will be able to break standard public-key encryption algorithms, is coming, perhaps sooner rather than later. Recent advances in quantum computing are accelerating the arrival of Q-Day, putting enterprise data, payment processing systems and even cryptocurrencies at risk.

Separating hype from threat

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Not all companies benefit from a one-size-fits-all unified communications deployment. Verticalized UC lets companies tailor platforms to fit their specific needs.

Even as unified communications platforms reach feature parity, enterprise IT leaders are shifting focus away from one-size-fits-all deployments to vertical-specific implementations that drive measurable outcomes.

Verticalized UC gives organizations the flexibility to deploy services designed to meet their specific needs, such as meeting regulatory requirements and optimizing communications for field workers. Metrigy’s research data show a wide variance in UC approaches across verticals.

Case in point: According to Metrigy’s “AI in Workplace Collaboration: 2026-27” study, use of AI tools within UC platforms is 23% higher among financial services firms than in the overall pool of participants. Healthcare organizations, on the other hand, are more likely to rely on voice communications, as well as wireless devices, in hospital settings than other types of companies.

Quantifying the ROI of vertical UC

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Metrigy has published its annual ranking of UCaaS providers as well as our MetriStar awards for UCaaS provider customer sentiment and business success. In this episode we’ll chat with MetriRank lead author Diane Myers, and MetriStar lead author Irwin Lazar about the results and what sets each provider apart.

Cloud-based communication tools continue to play a central role in how work gets done the world over, with more than 75% of businesses globally using unified communications as a service (UCaaS) solely or in a hybrid combination with an on-premises or private cloud hosted system, according to Metrigy’s soon-to-be-published global Workplace Collaboration MetriCast 2026 study.

UCaaS popularity stems from its ability to deliver cost-effective services with sophisticated product features for companies of all sizes and across verticals. Microsoft remains the undisputed leader on market share, but Cisco, No. 2 in market share, sits atop Metrigy’s UCaaS MetriRank 2026 ranking of the top 10 providers. It surpasses Microsoft in the MetriRank with high marks across other evaluation criteria, including the highest scores for market momentum and product mix, solid financials, and notable customer sentiment and business improvement scores. 

This latter criteria—customer sentiment and customer business success—serve as the basis for our annual MetriStar Award program, UCaaS among the product categories included. This week, in fact, we’re sharing the results of our 2026 UCaaS MetriStar Award program, which evaluates providers based on customer ratings and quantitative metrics that tie product usage to measurable business success. We drew this customer data from the above-mentioned Workplace Collaboration MetriCast study, this year with 1,437 participating companies globally.

In the study, participants report how their primary UCaaS providers impact business metrics like revenue, operational costs, and employee efficiency. Providers score an average of 199.3 points in our business success calculations. Improvements in employee efficiency account for 87.2 points of that average, making it an area where vendors perform exceptionally well. For example, 30.2% of companies report a significant increase in employee efficiency, and 43.6% report a modest increase. Additionally, 48.5% report a significant increase in revenue, while 58.7% report a modest increase, and nearly half report either a significant (13.2%) or modest (35.3%) decrease in costs. 

Participants also to rate providers on a 1 to 10 scale across various categories, such as AI capabilities, analytics, integrations, and reliability. The overall average customer sentiment score for UCaaS providers this year is 8.28. 

We group our MetriStar recognitions into three categories:

Congratulations to the 10 UCaaS providers recognized in this year’s program!

Enterprises have historically used UCaaS to reduce operating costs. No longer. Today, due in part to AI, companies view UCaaS as the most efficient way to boost collaboration.

There is no question that unified communications as a service now dominates the workplace collaboration landscape.

Consider: Almost 59% of companies surveyed in Metrigy’s “Workplace Collaboration MetriCast: 2025″global research study of 797 organizations labeled unified communications as a service (UCaaS) as their primary platform for calling, meetings and messaging. Of those using on-premises UC applications, 33% also used UCaaS or planned to do so by the end of 2025.

UCaaS adoption drivers have shifted. Reduced operating costs is still a primary consideration, but access to improved collaboration features, especially those driven by AI, is the primary reason why companies adopt UCaaS.

To that end, here are seven key UCaaS features, in no particular order, which will help boost productivity and employee efficiency within your organization.

1. AI virtual assistants

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Using AI-powered analytics tools to diagnose and resolve problems can boost pilot projects’ success.

Though originally applied to automotive manufacturing, Henry Ford once said, “Failure is simply the opportunity to begin again, this time more intelligently.”

Such wisdom applies to today’s AI-powered pilots, which, because of the intelligence of AI itself, tend to fail fast, improve and then progress to production quickly. This is especially true in the world of customer experience (CX), where 64.6% of companies now use AI to improve interactions and outcomes, according to Metrigy’s AI Organizational Best Practices 2026-27 global study of 756 companies.

Most (85%) of companies say fewer than 40% of their pilots do not continue into production, with the largest percentage (30.3%) stating that only 11% to 20% of their AI pilots fail. When considering AI’s ability to self-heal and self-improve, driven by pre-established success metrics and measured by continuous analytics, it’s no surprise that companies only discontinue 16.6% of AI projects once in production, according to the study.

As business and technology leaders become more comfortable and experienced with AI, they are finding more success with their pilots. But plenty still fail and understanding why will help improve the overall success rates.

Data leads the causes of AI project failure

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Enterprise collaboration hardware and platforms are adjusting to the newest tools for conferencing and digital work.

This week’s InfoComm conference in Las Vegas will draw tens of thousands of attendees from across the A/V spectrum. While the show covers a broad range of technologies, enterprise collaboration is poised to play a major role, with featured keynotes from both Cisco and Microsoft.

Heading into the event, here are the major trends and topics that I am watching:

Continued buildout of the Microsoft ecosystem

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