Although consumers are becoming more positive about interacting with businesses using AI agents, business leaders think their customers want to use the technology more than they actually do. With adoption, capabilities, and preferences changing rapidly, it’s important for business leaders to narrow the gap between their expectations and reality.

For example, IT/CX/AI leaders say 42.7% of their customers prefer interacting with AI agents and 10.3% avoid them. In reality, only 21.7% of consumers say they prefer using AI agents and 32.7% avoid them, according to Metrigy’s AI’s Role in Customer Experience 2026-27 global study of 759 companies and Metrigy’s 2Q AI Consumer Experience Index, a study in partnership with NiCE, with 1,000 U.S. consumers. 

That does not mean consumers are always avoiding AI agents–or even that they’re having bad experiences. Overall, the Metrigy AI Consumer Experience Index increased quarter-over-quarter, and the AI Satisfaction sub-index saw the largest increase, of 11.8%. The tides are turning, albeit slowly, so business leaders have a balancing act to follow: Don’t roll out AI agents too aggressively without the option to escalate to humans, and don’t be too far behind to frustrate customers who know AI agents’ value.   Business enthusiasm for AI isn’t a sound CX strategy, and assuming customer comfort is a fast way to erode the trust you’re trying to build.

Companies Think AI Can Stretch Further—Customers Aren’t So Sure

The gap widens when we ask companies how far AI can go. Nearly 73% of business leaders say AI agents are capable of doing more than customers are comfortable with them handling, and that figure rises to 81.2% among Metrigy’s research success group (the companies achieving above-average business improvements from AI).

The most successful companies aren’t claiming customers are holding them back out of ignorance. They’re acknowledging a comfort ceiling, and that’s the right instinct. Companies that push past that ceiling—automating interactions customers would rather not hand to AI—don’t save money in the long run. They generate callbacks, complaints, and churn.

Why does the perception gap exist at all? A few reasons:

Where AI Actually Wins, and Where It Doesn’t

So where’s the line? Our research participants are fairly clear-eyed about it. CX leaders say AI agents are more effective than humans for most routine issues. But humans still win for refunds, complaints, and selling products or services.

That split makes sense. Refunds and complaints are emotional and often require judgment or an exception to policy. Selling requires reading a person and adjusting on the fly. Those are human strengths, and they happen to be the interactions where getting it wrong costs the most. AI is the way to go for high-volume, low-emotion contacts. It is not the way to go for the moments that decide whether a customer stays–at least for now. AI has the technical capability to handle some of these more complex issues, but businesses first must earn their customers’ trust in the use of AI. For customer-facing AI, take it a little slower and be more methodical.

Consumers tell us the same thing when we ask what they’ll accept on the phone. Nearly 60% are comfortable with accepting a call from an AI voice agent for issues such as appointment reminders, delivery status, fraud alerts, billing and payment reminders, and service renewals. Notice the pattern: these are informational and transactional. None of them ask the customer to negotiate or vent. Build your voice AI around that list, and you’re working with customer preference instead of against it.

AI Triage: The Front Door Most Customers Now Walk Through

Credit where it’s due: companies are getting triage right. Two-thirds (66%) now open at least some customer interactions with an AI triage agent. On average, 36.3% of customer interactions start with an AI triage agent, which replace the old touch-tone IVR, letting callers say what they need in plain language instead of mashing numbers into a menu.

That’s a smart place to lead with AI, and the spending backs it up: 56.1% of companies increased their investment in AI triage in 2025. The catch is cost. Triage runs more expensive than IVR, which is why some CX leaders use a hybrid—IVR to offload high-volume or low-priority calls, triage for everything else. That’s a reasonable way to manage the bill without forcing customers back into a menu tree.

Why the Gap Matters

Step back, and the strategic risk comes into focus. If you believe 42.7% of your customers want AI when the real number is 21.7%, you’ll over-invest in customer-facing automation and under-invest in the AI that makes your human agents better. And the human-facing AI—agent assist, real-time coaching, post-interaction summarization— lifts performance on exactly the emotional, high-stakes contacts customers don’t want automated in the first place.

The successful companies already understand this. In fact, 56.7% prioritize AI that helps human agents over pure self-service automation. They’re not choosing people or technology. They’re using technology to sharpen human performance on the interactions that matter, and using AI agents to clear the routine volume so those people have the time to do it.

This explains, in part, why the Metrigy AI Consumer Experience Index increased measurably from first to second quarter this year. Whether customer sentiment is high with either an AI or human agent, AI itself is helping to improve the experience. And the more companies embed AI in their entire CX platform (self-service, agent assist, workforce enablement management, analytics, etc.), the more the flywheel improvement continues. 

What CX Leaders Should Do

Don’t let your own enthusiasm for AI cloud your judgment on what your customers really want:

The companies that close this gap may not be automating customer service the fastest, but they will be the ones that align best with their customer preferences.

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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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As AI adoption rapidly grows, so too do concerns over security and compliance for AI conversations, actions, and generated content. Organizations that get security and compliance right achieve measurably better ROI for their AI investments than those that struggle to put the right policies and enforcement mechanisms in place. In this session, we’ll provide actionable guidance for ensuring that security and compliance are an AI enabler, and not an adoption barrier.

Attendees will learn:

Enterprise AI Security and Governance Summit 2026 – Ensuring AI Compliance

Presented by: Irwin Lazar, President and Principal Analyst, Metrigy

Date and Time: September 2, 2026, 11:00 AM EDT

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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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AI-driven triage agents, proactive outreach, agent assist, interaction analytics and automated coaching are five quick wins that organizations could implement now in CX.

Customer experience transformation often requires a massive, multi-year undertaking with prolonged time for realizing a return on investment. However, as Metrigy has learned in studying companies around the world, a large tech project is not required when implementing AI for CX.

By focusing on immediate, high-impact applications rather than an architectural overhaul, CX leaders can build momentum and prove rapid ROI.

Here are five AI-for-CX quick wins to implement right now.

1. Deploy AI triage agents to the front lines.

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If an organization doesn’t have a fast, secure way to pull the plug and switch AI platforms, it’s opening itself up to operational and financial vulnerabilities.

Any organization that has built customer experience (CX) and internal workflows around a specific third-party proprietary AI model should be fearful of vendor dependency. It’s too risky a position to be in without proper protections. What if the model degrades in quality? Raises its pricing? Suffers a security breach? Fails to meet the latest compliance mandates?

We know such concerns are on IT and CX leaders’ minds based on findings from our second-quarter 2026 AI Technology Foundation & Strategy: 2026-27 study of 759 companies globally. A primary concern for these leaders involves the challenges of changing technology providers: 60.2% of organizations worry about the technical difficulty of switching models, while an additional 39.8% worry about the legal risk of remaining stuck with a specific model.

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Customer service is now the top area for AI adoption across the enterprise. Follow these best practices to implement AI-driven self-service in your organization.

Customer self-service portals can be convenient and improve CX when they help consumers get what they need more quickly and easily than speaking to or chatting with a live agent. But when customers can’t get what they need from self-service tools or businesses have an upsell opportunity that requires human intervention, that benefit ends.

Determining whether to route customers toward or away from self-service portals is now less challenging with the arrival of generative AI agents powered by large language models (LLMs). No longer must customers browse categories, run keyword searches that spit out links to follow, or have stilted conversations with rule-based chatbots. Instead, they can engage in casual conversation with AI agents that understand their intent, tap into the company knowledge base, and deliver contextually relevant information that can resolve even the thorniest of problems.

Now, 52% of companies use AI text agents and 42% use AI voice agents for customers, according to a global Metrigy study of 759 companies that researched the role of AI in CX. AI is fundamentally changing the customer interaction model, to the point where more businesses now say technology matters more than people in delivering successful customer interactions.

In fact, most companies (79.5%) say AI will have a positive effect on their customer service in the next two years. In the coming years, they anticipate the percentage of customer interactions that AI fully resolves will double, from 29.2% today to 60% by 2030.

Customer-facing AI agents already are proving generally better at handling most customer issues than human agents, including confirming or changing deliveries, appointments and reservations, getting product information, and reaching out proactively. Human agents are better for refunds, complaints and selling products or services, according to the Metrigy study.

As companies develop and refine their customer self-service strategy, they must consider how and when AI will play a role.

AI self-service best practices

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Right now, if teams at your company are using AI, they’re most likely doing so for basics like handling routine queries, summarizing meetings, and speeding up ticket resolution. The results most certainly look good, and the AI implementation feels like a win. In fact, our recent AI Organizational Best Practices: 2026-27 research study of 759 companies globally confirms this sentiment. In the study, 95.7% of organizations report a positive overall impact from AI.

But here’s the hard truth: If your company’s strategy stops at automating existing workflows, you leave significant financial and operational value on the table.

The Automation Trap 

When IT and CX leaders first deploy AI, they typically look for low-hanging fruit. They find a slow, manual process and layer AI over it to make it faster. Our data reflects this reality. Currently, 53.7% of organizations use AI to automate their existing workflows, rather than redesign how the work gets done. Furthermore, 69.5% of respondents believe AI’s role in the workplace is to support existing work structures, rather than revising those processes.

This approach makes sense for quick wins. Automating existing workflows delivers a rapid return on investment (ROI). However, taking this route creates a ceiling on long-term success. It speeds up an old process, but it fails to ask if that process remains the best way to serve the customer or support the employee in an AI-driven world.

The Secret of the Success Group: Redesigning Work 

To understand how to maximize AI ROI, we must look at this study’s Success Group, determined by above-average baseline improvements across key business metrics directly from AI initiatives. Successful companies operate differently. Instead of just plugging AI into a legacy system, they tear the system down to the studs. They redesign how work gets done. They actively evaluate every touchpoint in a customer journey or IT workflow and deliberately decide whether AI, a human, or a combination of both should execute the task.

The numbers validate this strategy. Among the Success Group, 57.6% prioritize the redesign of work over simple automation. Conversely, less successful companies lean the opposite way, with 56.7% clinging to the basic automation of existing workflows.

The Hard ROI of Workflow Redesign 

For IT and CX decision-makers, the business case for redesigning work is undeniable. Redesigning how work gets done and how teams structure it acts as a superior long-term strategy. When organizations take the time to completely redesign workflows with AI, rather than just automating old ways, they unlock dramatically better business outcomes.

Specifically, compared to those companies that have just automated existing workflows, redesigning work has netted the Success Group, on average, a:

Think about what a 25.9% larger drop in future costs means for your operational budget. By redefining roles—such as having an AI agent handle triage and data collection while seamlessly passing the context to a human expert for empathy and resolution—you eliminate inefficiencies that basic automation leaves behind.

Taking Action in Your Organization

Mirroring the operational success of the Success Group requires a mindset shift. Stop asking, “How can AI make this existing contact center flow faster?” Instead, ask, “How do we best solve this customer problem now that we have AI capabilities?”

Evaluate your current processes. If you still force customers or employees through a rigid, 10-step legacy workflow that now just happens to feature an AI chatbot at step three, you need a redesign. Map out the journey from scratch. Intentionally assign tasks to the agent—human or AI—that handles them best.

By moving past basic automation and committing to a fundamental redesign of your workflows, you break through the ROI ceiling, optimize your costs, and deliver the elevated experiences that your customers and employees deserve.ts for the AI business. But the headline is simple enough: people are using AI more, liking it more in the moment, and still not sold on the ending.

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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In May and June 2026, Cresta introduced three products that help companies with their AI agent lifecycle. AI and CX leaders want fast development of AI agents to maintain a competitive edge. But this accelerating speed of innovation has resulted in companies deploying numerous AI agents without the appropriate testing or operational discipline. Cresta’s announcements address building, validating, deploying, and continuously improving AI agents at scale. 

First, Cresta launched Synthetic Customers, which creates realistic customer personas based on historical enterprise conversation data. These synthetic personas simulate customer interactions for AI agent testing and human agent training while also generating deeper customer behavior insights. Unlike traditional, often static personas built from surveys or CRM records, Synthetic Customers continuously evolve based on actual customer conversations.

Continue reading at cresta.com.