Contact centers that invest in agent assist technology can help their agents become more productive and eliminate tedious tasks to make their jobs easier.

The integration of artificial intelligence into customer experience platforms is transforming how contact centers operate, sparking a pivotal question for CX leaders: Is AI’s role in CX primarily to replace human agents or to empower them? The answer for most companies today is that AI is a complement to the contact center’s human workforce.

Metrigy has seen this in recent AI and CX studies. For example, in Metrigy’s “Customer Experience Optimization: 2025-26” global research study, 85.6% of 656 participating companies said humans and AI are actively working together to serve customers. Agent assist technology is proving to be a common workhorse of today’s contact center — the second-most-used application for CX.

In fact, over 55% of companies were already using agent assist to support customer interactions when surveyed in the third quarter of 2025 and another 32.3% were actively planning to implement it. In “AI’s Role in Customer Experience: 2026-27,” a second quarter 2026 study of 759 companies, 56.7% said they’ve prioritized the use of AI to help human agents serve customers, compared to 43.3% that place their greater focus on AI for customer self-service.

As CX leaders develop their technology roadmaps, understanding the strategic value of agent assist is crucial. For forward-thinking organizations, this technology isn’t just an operational tool but a bridge technology that provides immediate measurable success while serving as a proving ground for fully autonomous, agentic AI use cases.

What agent assist means

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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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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.

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The CX strategy moving forward will be “intelligent choice” — data-informed choices determining which level of service is best for which customer.

For years, CX leaders have focused on efficiency in the contact center through handle time reduction, call deflection, and digital channels. With the growth of agentic and conversational AI agents, many businesses are doubling down on further efficiency gains to guide their customer interaction strategies.

But others are looking at AI’s value differently. With a growing number of channel and interaction options comes complexity, and leading CX leaders are uncovering an opportunity to not only differentiate service the service they provide but to monetize higher service levels.

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Automated customer sentiment evaluations can help organizations understand their customers better, replace manual tasks and boost revenue by uncovering buying signals.

Every day, businesses leave critical customer insights buried inside support tickets, live chats and phone transcripts. This sinkhole exists because organizations have long relied on post-interaction surveys to capture customer sentiment. These surveys have a key flaw: They lack adequate responses from all customers except from the most delighted or deeply frustrated outliers. Despite this deficiency, the post-interaction survey remains the go-to mechanism for gathering customer sentiment for most companies, according to Metrigy research.

However, forward-thinking contact center operations are tapping into the power of AI inferred sentiment analysis to get themselves out of the mire. Today, inferred sentiment analysis uses large language models and context-aware processing to discern emotional tone of customer interactions. Essentially, inferred sentiment delivers a customer sentiment score for every touchpoint without requiring the customer to fill out a single survey.

Since Metrigy began tracking inferred sentiment in early 2023, we’ve seen a big bump in use, from 15% to 45% in early 2025. But the real story is in the reported improvements in core operational metrics from its use.

The benefits of inferred sentiment analysis

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Ratings from 1,400+ business technology leaders globally define MetriStar program

May 6, 2026 – Research and advisory firm Metrigy announced winners of its 2026 Customer Experience MetriStar program in nine key technology areas that drive successful customer interactions. 

Metrigy determines these results based solely on insights from 1,437 CX, IT, and business leaders with decision-making, operational, and/or procurement responsibilities for specific technologies in our Customer Experience MetriCast 2026 global research study. 

“Business leaders are demanding more multi-dimensional products, and technology providers are responding with capabilities that resonate,” says Metrigy CEO Robin Gareiss. “Through feedback from these IT and CX leaders, we know that they are generally pleased with the state of the technology available, but they also want to see rapid technology innovation, more integrations, and more automation, among others.”

Unlike other award programs, the MetriStars give enterprise decision-makers unique insight into what really matters based on real-world usage and experience with the technology vendors. Metrigy asks enterprises to rate their providers on a variety of metrics, such as AI capabilities, security, integration, and value, making up the Customer Sentiment Score. In addition, Metrigy asks the research participants how the use of their products improves or increases customer satisfaction, revenue, employee efficiency, and operational costs—collectively comprising the Business Success Score.

Each report features three recognitions, as follows: 

CX, IT, and business leaders rated their vendors’ performance and provided associated business success across the CX technologies, resulting in 43 MetriStar Top Provider Awards, up from 32 providers last year. This helps enterprises develop short-lists for their technology evaluations.

This year’s MetriStar Top Provider winners, listed alphabetically, include:

Another 31 vendors won awards for either customer sentiment or business success. For more details and a full review of each category and its winners, please see our MetriSight podcast announcing the results. Metrigy clients have access to the reports, and they are available for purchase on our website at www.metrigy.com.

AI agents can contact customers, make decisions, act autonomously and close a sale with little or no human involvement. They can also renew orders and attempt upsells.

Remember the days when outbound sales teams were an army of people manually hammering away at phone keypads just to reach a busy signal or listen to the phone ring incessantly?

Businesses moved from that era of raw human labor into the world of predictive dialers, where math and algorithms took over to keep agents talking constantly. But even then, the effort was aimed at bringing a human agent to the customer.

Today, businesses are in the middle of the most significant shift yet — a move from simply connecting callers to having intelligent systems that can converse, decide and close a sale. Welcome to the era of agentic AI.

AI agents become outbound sales reps

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These essential tips assure AI agents function properly before and after they hit production.

The excitement surrounding generative AI in customer experience (CX) is undeniable. From ultra-responsive chatbots to autonomous “agentic” systems that can rebook flights or process refunds, the potential for scale and efficiency is transformative. However, as we move from scripted, deterministic logic to probabilistic large language models (LLMs), the traditional testing playbook isn’t enough any longer.

In a world where an AI agent might provide a different (yet correct) answer to the same prompt every time, or suddenly go haywire, how do you ensure it stays on brand, replies within the scope of company policy, and otherwise keeps out of trouble? As Metrigy has found in its CX research, new risks associated with the use of AI agents are driving many companies to adopt tools and processes aimed at assuring AI agents are performing as expected prior to deployment and that they keep doing so once implemented.

Drawing from insights shared in conversations with a host of industry experts, here are seven essential tips for assuring AI agents function properly before and after they hit production.

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