These three priorities offer platform providers more of an advantage than any individual type of channel partner.

Three core criteria are dominating the reasons IT and CX leaders select a service provider—and decide when it’s time to change their providers, according to Metrigy’s Buying Strategies for Communications Services 2026-27 global study of 203 companies.

The three factors — integration capabilities, pricing, and AI expertise — are causing more to lean toward platform providers over any individual type of channel partner. In the past, factors such as managed services, existing relationships, and geographical reach were much more significant.

The research findings document that all types of providers must excel in these areas, but many are not. For example, platform providers often treat integration as a professional-services line item instead of delivering it from within the product itself, while channel partners leave AI expertise to the platform providers because, after all, it’s complicated. At the same time, enterprise decision-makers must re-evaluate their provider to ensure aggressive pricing to accompany increasing demands for integrated platforms, systems, and applications, along with AI expertise.

The value gap nobody’s talking about

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Companies are already reaping the benefits of agents that can autonomously handle multi-step processes. Learn their unique benefits, technical requirements and challenges.

AI has long served customer experience (CX) management, but the enterprise landscape is undergoing a fundamental shift as organizations transition from static conversational tools to autonomous systems. These autonomous systems, known as agentic AI, differ from earlier generations of the AI used in CX by pairing large language models (LLMs) with tool use, memory and iterative planning. As such, agentic AI marks a shift from single-turn reactive tools toward goal-driven systems that can autonomously execute multi-step workflows with minimal human oversight.

Market adoption of agentic technology is accelerating rapidly across global enterprises. My firm, Metrigy, conducted CX research in 2025 showing that enterprise familiarity with the term agentics grew from 52% to 61% in just six months, with 54% of companies having agentic AI-focused CX transformation in the works by the third quarter. Furthermore, 68% of CX leaders reported that they expect agentic systems to fully resolve more than a quarter of their total customer interactions within two years.

For early adopters, agentic AI is delivering measurable business value, including the following:

• Accelerated response times. Sixty-nine percent of CX leaders identified these as a primary value driver enabling rapid issue resolution.
• Enhanced customer satisfaction. While 51% of organizations highlighted this as a direct benefit, in Metrigy’s benchmark Success Group, which is determined by measured business improvements, CSAT gains exceeded 25%.
• Elevated employee satisfaction. Cited by 60% of organizations as AI agents offload routine inquiry queues, the most successful companies achieved significant employee satisfaction boosts.
• Measurable revenue growth. Reported by 53% of organizations, the Success Group achieved revenue growth exceeding 19%.
• Workforce efficiency. While 37% of companies reduced new-hire needs, the Success Group reduced costs by more than 15% and achieved efficiency boosts of over 22%.

The survey results provide convincing evidence that agentic AI ultimately unifies customer journeys into outcome-driven experiences that accelerate business performance.

What agentic AI means for customer experience

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Technology buyers face complex choices when selecting customer experience platforms and service partners. Recent data from Metrigy’s global study of communications purchasing strategies reveals significant shifts in how enterprise IT and CX leaders evaluate vendor ecosystems. Rather than relying solely on traditional procurement channels, organizations now re-examine how platform vendors and channel partners deliver long-term value.

The Direct Versus Channel Shift

Over the past five years, 39.4% of enterprise buyers shifted toward purchasing directly from platform providers instead of buying through traditional third-party channels. Organization size heavily influences this purchasing decision. Smaller companies, with a median headcount of 188 employees, regularly procure contact center seats directly from platform providers. Conversely, larger enterprises, with a median headcount of 500 to 773 employees, rely on managed service providers (MSPs), value-added resellers (VARs), or hybrid models that combine direct platform purchases with partner services.

When enterprise buyers evaluate individual provider categories, platform vendors receive 32.4% of the vote for delivering strong business value. However, when we aggregate all channel partner options—including technology services distributors, service providers, MSPs, systems integrators, cloud marketplaces, independent software vendors, and VARs—the collective channel secures 67.6% of the value vote. This split demonstrates that while direct platform relationships gain traction, specialized channel partners retain a substantial role in complex deployments.

Evaluating Selection Drivers: Integration, AI, and Pricing

Three distinct criteria guide vendor selection in modern communications environments: integration capabilities, pricing structures, and AI expertise. Integration capabilities influence provider selection for 44.4% of organizations. Furthermore, 59.8% of buyers expect platform providers to play a larger integration role over the next two years, while 62.0% anticipate an expanded integration role for channel partners.

AI also reshapes vendor criteria. In fact, 50.7% of organizations say they’ve modified their vendor selection requirements specifically due to AI advancements. Among companies that changed their purchasing methods, 65.7% cited the pursuit of AI expertise as a driving factor, outranking integration capabilities at 50.5% and pricing at 45.7%. Currently, 53.5% of companies rely on platform providers for AI technology, compared to 19.0% that rely on channel partners and 24.6% that utilize internal IT teams.

Pricing remains a critical evaluation factor for 35.2% of buyers overall and reaches 63.4% for global voice services. Enterprise buyers scrutinize total cost of ownership, looking beyond software licenses to incorporate AI usage fees, implementation charges, custom integration, and ongoing support services.

Importantly, platform providers and channel partners fulfill complementary operational roles. Enterprise buyers look to platform vendors to explain product capabilities (61.4%), integrate applications (59.1%), and manage initial implementations (55.1%). In contrast, buyers depend on channel partners for ongoing technical support (63.5%), price negotiation (59.5%), and vendor identification (55.4%).

Strategic Action Plan for CX Leaders

To optimize vendor selection, IT and CX leaders can execute several targeted actions:

• First, audit existing partner relationships to verify that third-party integrators possess actual AI and technical capabilities rather than basic transactional capabilities.
• Second, demand prebuilt integrations from platform providers to reduce custom professional services fees and streamline system architecture.
• Third, structure hybrid procurement models that leverage direct platform purchasing for core licenses alongside specialized partners for complex vertical requirements or specialized managed services.

By aligning procurement methods with technical requirements, enterprise leaders build resilient, cost-effective CX operations.

Next-generation AI platforms in CX won’t compete on model size or speed. Rather, they’ll compete on how much context they can assemble, understand, and act upon in real-time and historically. Contextual AI pulls from multiple data sources and platforms, and it affects everything from workflows to analytics to personalization.

Research and advisory firm Metrigy recently studied business plans for contextual AI. During this session, Metrigy CEO Robin Gareiss will address the measurable benefits companies have uncovered and steps they’re taking to find continuous improvement.

CX Excellence Summit 2026 – 5 Steps to Leverage Contextual AI, the Next Frontier of Successful CX

Presented by: Robin Gareiss, CEO and Principal Analyst, Metrigy

Date and Time: September 22, 2026, 1:00 PM EDT

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For years, consumers approached AI chatbots with a healthy dose of skepticism—and honestly, who could blame them? Early bots were clunky, frustrating, and often made problems worse instead of better.

That era is ending. Metrigy’s 2Q AI Consumer Experience Index, conducted in partnership with NiCE, tracks 1,000 U.S. consumers and reveals a real turning point. The overall CX index climbed from a baseline of 100 to 104.6 in a single quarter, with AI satisfaction jumping 11.8% and trust in AI up 7.6%—the two strongest gains of any metric tracked.

The aggregate numbers are interesting, but also informative is what happens when you break consumers into four distinct personas. We call them the AI superstars, human loyalists, silent multitaskers, and tolerant-but-trapped middle. 

• AI superstars – These are consumers aged 35 to 44, and they’ve crossed a threshold that hasn’t been seen before in Metrigy’s tracking: A majority (55.7%) now say the technology experience matters more than human interaction when it comes to a positive customer experience. This doesn’t mean they’ve fallen in love with bots over people. It means AI has consistently helped them resolve issues faster, kept context intact across channels, and made human agents more effective when they do get involved.
• Human loyalists – By contrast, these are consumers 65 and older who haven’t budged in their desire to work with humans first. Nine out of 10 still believe people matter more than technology, and nearly 95% would pick a human agent even if an AI agent guaranteed a resolution. Companies that bury the option to reach a person risk losing this segment entirely.
• Silent multitaskers – These are men aged 18 to 24 who prefer text over voice. Force them into a phone call to resolve an issue, and you’ve likely created a frustrated customer on the spot.
• Tolerant-but-trapped middle –On the surface, this looks like a dream segment: These consumers stick around through an average of 4.88 bad experiences before walking away. But that patience isn’t loyalty—it’s inertia. Most in this group don’t feel companies show them any real empathy, and they’re staying largely because switching feels like a hassle. The moment a competitor makes it easy to leave, especially with a lower price, they’re gone. Businesses that mistake this tolerance for genuine satisfaction are sitting on churn they can’t see coming.

None of these groups is wrong in how they perceive customer experience. They simply define success differently, and with this reality in mind, CX leaders should abandon the idea of a single, standardized customer journey. Successful companies will leverage AI analytics to serve different AI personas and multiple customer segments, each with different expectations, with the experience they want—be that fully, partially, or not at all AI-powered and automated. 

Artificial intelligence (AI) has moved past the experimentation stage in customer experience (CX): Customer service now ranks among the top two areas of AI investment anywhere in the enterprise. What CX and IT leaders are working through now is narrower and harder—where AI works best in their own operations, what it costs, and how quickly it pays for itself. 

This paper draws primarily from Metrigy’s global AI’s Role in Customer Experience: 2026-27 research study of 759 companies. It also incorporates findings from additional Metrigy studies, as noted. Throughout, we compare the Success Group—companies documenting the greatest improvements in revenue, costs, customer ratings, and employee efficiency as a result of their technology investments—with the Non-Success Group. That gap is the most useful signal in the data: Across every metric we track, the companies with the greatest use of AI are the companies getting the most out of it.

Download and read this research report at five9.com.

Technology and CX leaders need to coordinate systems to address application fragmentation.

IT and CX leaders are no strangers to technology projects aimed at streamlining operations and enhancing efficiency. Today, that means many are familiarizing themselves with the idea of AI orchestration — what it is, how to implement and how their organizations might benefit.

Conceptually, AI orchestration shouldn’t be too difficult to understand, in that “orchestration” essentially says it all. An AI orchestration layer coordinates multiple AI models, tools and systems — along with the data and workflows connecting them. Instead of relying on a single AI system in isolation, this back-end framework routes tasks, handles data flows, maintains context and ensures compliance. Since it doesn’t possess a native user interface, it quietly feeds structured data and decisions to other enterprise applications.

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Talk all you want about the value of this AI model or that one, but an enterprise AI initiative’s success more often hinges on another critical element: a solid and unified data foundation. This is clearly shown in Metrigy’s AI Technology Foundation & Strategy: 2026-27 global research study of 759 companies, the sharpest line between organizations getting measurable value from AI and those still stuck in pilots runs through their underlying data architecture. To achieve sustainable, high-impact results, organizations must establish a mature data framework that delivers clean, highly contextual information on demand.

Without this structured foundation, enterprises face severe operational hurdles, most notably data fragmentation—the scattering of information across disconnected systems, formats, and owners. As the number-one cause of AI project failure, fragmentation acts as a massive bottleneck. To successfully drive productivity and scale applications, technology leaders must prioritize unifying and organizing their data first. 

The Maturity Gap

Start with how enterprises store the data that feeds AI. Only 23.3% have adopted a layered storage approach—that is, a deliberate architecture combining combining a storage layer, such as a data lakehouse for scale; a context layer, relying on a knowledge graph for relationship logic; and a retrieval layer that uses a vector database for the semantic search that retrieval-augmented AI depends on. The rest are running on a single store or improvising.

That maturity gap tracks directly with results. Companies in Metrigy’s research Success Group (those achieving above-average business improvements from AI) are twice as likely to have a layered approach as their Non-Success Group peers. The encouraging news for the laggards is momentum, with 47.5% reporting a layered architecture on the roadmap. The watchpoint is that a roadmap is not a deployment; the companies already there are compounding their advantage now.

The Surprising Winner

The most common layered approach pairs a data lakehouse with a vector database, a sensible two-tier design. However, the configuration that correlates most strongly with success is not the most elaborate stack but the most consolidated one: a single multi-modal platform, meaning one system that handles tables, vectors, and graphs natively rather than stitching three specialized layers together.

The companies posting the strongest outcomes are, in many cases, reducing the number of moving parts rather than adding them, trading integration overhead for a unified store the AI can query without the engineering tax of keeping several systems in sync.

RAG Reality Check

No discussion of AI-ready data gets far without retrieval-augmented generation (RAG), the architecture that lets a model look up specific, current information from a company’s own sources before it answers rather than relying on what it absorbed in training. Adoption among this study’s respondents is early: Just 22.6% use RAG in production today.

The pipeline behind it, however, is full. Nearly 75% are planning or evaluating RAG, many for this year, and the Success Group is again out front, running it at nearly twice the rate of the Non-Success Group. The signal is hard to miss. RAG is moving from differentiator to table stakes, and the companies treating it as optional are the ones most likely to find themselves behind.

Unstructured Data as Fuel

Much of the information worth feeding an AI system (contracts, transcripts, tickets, knowledge-base articles) is unstructured, and putting it to work is paying off. Nearly 71% of companies report that allowing unstructured data into their AI systems has improved employee productivity.

The leaders, though, extract more from the same raw material. Success Group companies index a larger share of their unstructured content for retrieval, more of them landing in the 51%-to-75% range rather than stopping at half. That’s the upside. On the downside is governance: Unstructured data use gives rise to the risk of using whatever sensitive or stale material is buried within those sources, which is why 31.5% now regularly audit their vector databases and anonymize sensitive information before it reaches the AI. Indexing more without auditing more is how a productivity gain becomes a compliance incident.

The Failure Warning

If any single finding should trigger change, it’s around data fragmentation and its propensity to doom AI projects to failure. Eliminating data fragmentation closes the loop on everything above. The storage decision, the RAG rollout, the unstructured-data pipeline, and the auditing discipline are all, at bottom, answers to the same problem: getting the right data to the model, in usable form, without it falling through the cracks between systems.

What CX and IT Leaders Should Do

The lesson for decision makers is to invest in the foundation that determines whether any model can perform:

• Audit your storage architecture. Map what you have against a layered or unified multi-modal design, and treat a single-layer approach as the risk it is.
• Move RAG off the roadmap and into production, before it shifts fully from advantage to expectation.
• Index more of your unstructured data for retrieval, and pair every expansion with an audit-and-anonymize step so governance keeps pace.
• Treat data fragmentation as a first-order project risk, not an IT housekeeping detail.

Get the model wrong and you can swap it. Get the data architecture wrong and every model you try will underperform—which is precisely why the companies winning with AI are the ones that built the foundation first.

Want to more information? Download our complimentary report.

A major shift in customer service is happening. AI-driven proactive outreach has become a critical differentiator.

Companies going proactive are delighting customers, empowering agents, and driving ROI like never before.

Almost 70% of IT and CX leaders say proactive will overtake inbound interactions by 2027. Will your company be ready?

Read Metrigy’s exclusive new report, Driving Customer Engagement with AI and Proactive Outreach. Start building loyalty by engaging smarter and acting faster.

• Find out why almost 69% of consumers want proactive outreach
• See which contact method almost 74% of consumers prefer
• Stop issues before they happen with AI-powered predictions
• Deliver personalized offers that boost revenue instantly
• Empower agents to be more efficient with real-time AI insights

Turn proactive outreach into your competitive edge

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Metrigy’s research found that consumer preferences hinge on a variety of factors, so there is no one-size-fits-all deployment strategy.

Consumers have used AI for years, starting with basic (but let’s face it, not-so-great) chatbots. As more sophisticated AI agents have entered the scene in recent years, consumer skepticism was understandable, based on past performance.

But that’s changing. Metrigy’s 2Q AI Consumer Experience Index, a research project in partnership with NiCE, shows a profound shift in consumer AI adoption—and more broadly, improvements in customer experience overall.

The index tracks 1,000 U.S. consumers and documents a healthy improvement in CX overall, with an increase from the first quarter baseline of 100 up to 104.6 in the second quarter. The index comprises eight sub-indexes, including two that focus on AI, with AI satisfaction posting the strongest gain (11.8%) of any metric, followed by trust in AI (7.6%).

Demographic differences drive diverging sentiments

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