More AI, worse CX: the contact center consolidation problem nobody is solving

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AI adoption in contact centers rose 15% from 2023 to 2025. CX and EX ratings dropped 0.5 points. — Deloitte Digital, 2026 Global Contact Center Survey (600 organisations, 3,000 consumers)

Here is a number that should make every CX leader uncomfortable. Between 2023 and 2025, AI adoption in contact centers rose by 15%. Over the same period, customer experience and employee experience ratings dropped by an average of 0.5 points. That finding comes from Deloitte Digital’s 2026 Global Contact Center Survey, which collected data from 600 organisations and 3,000 consumers worldwide.

More investment. More tools. Worse outcomes.
That is not an AI problem. That is an infrastructure problem wearing an AI mask.

The instinct across the industry has been to treat AI as the solution to contact center complexity. But when you layer AI on top of a fragmented technology stack, you do not reduce complexity. You add to it. And the data from 2026 is starting to prove what many operations leaders suspected all along: the tech stack itself is the bottleneck, not the lack of AI.

The real shape of the problem

Deloitte’s 2026 survey paints a specific picture. On one hand, 64% of service leaders report higher agent productivity from AI, and 43% believe AI will cut contact center costs by 30% or more within three years. On the other hand, the actual experience scores—the numbers that tell you whether customers and agents are better off—went down.

What explains the gap between leaders’ optimism and measurable outcomes?

Look at how AI gets deployed in most contact centers today. The agent assist tool connects to the CRM. The voicebot runs on a separate conversational AI platform. The knowledge base sits in a third system. The telephony layer is managed by a fourth vendor. The quality management tool is a fifth. Each of these systems has its own data model, its own API conventions, and its own version of the customer record.

When an AI copilot tries to help an agent in real time, it has to pull context from across these disconnected systems. If the CRM says one thing and the ticketing tool says another, the AI either guesses, hallucinates, or serves up stale information. The agent then spends time verifying what the AI suggested instead of helping the customer. Productivity goes up on paper (the AI did produce a suggestion faster). Experience goes down in practice (the suggestion was wrong, or incomplete, or required manual verification).

Deloitte’s own 2024 survey of 600 senior contact center leaders found that more than three out of four reported their agents are overwhelmed by the number of systems and information they deal with during calls. Adding an AI layer on top of that did not fix the overwhelm. Even with generative AI deployed, 53% of agents still reported feeling overwhelmed by system complexity. Without AI, that number was 81%. A marginal improvement that still leaves the majority of your workforce struggling with the tools they have been given.

Why 48% matters more than you think

The Deloitte 2026 survey introduced a finding that deserves more attention than it has received: 48% of companies with mature service capabilities already use agentic AI, compared to just 24% of their lower-maturity peers.

48% of mature companies use agentic AI vs. 24% of low-maturity peers. The gap is not budget. It is infrastructure.

That 2x gap is not about budget or access to AI vendors. Mature organisations unified their infrastructure first, then deployed AI on a foundation that could support it. Lower-maturity organisations skipped ahead, bolting AI onto a fragmented stack and wondering why the results disappointed.

Metrigy’s 2025 research reinforces this pattern. Companies that use a single cloud-based platform for unified communications and contact center see revenue increase by 99.6%, costs decrease by 14.4%, CX ratings improve by 56.6%, and agent efficiency rise by 37.4%. Those are not incremental gains from a marginal improvement. Those are the kinds of numbers that separate companies pulling ahead from companies spending more to stay in place.

The pattern is consistent across every major analyst report from the past year: companies with unified data foundations are 3.4 times more likely to report satisfaction with their AI deployments, according to Metrigy. AI already powers one in five contact center interactions, with that number expected to reach four in ten by 2028. Generative AI is projected to resolve up to 75% of interactions in that timeframe. But those projections assume the data is connected. For fragmented stacks, the projection is pilot projects that never graduate to production.

The Frankenstack problem nobody admits

Gartner’s 2025 CCaaS analysis had a term for what most enterprise contact centers are running: “Frankenstacks.” Technology environments stitched together from multiple vendors, acquisitions, partner products, and point solutions that were never designed to work as a single system.

The CCaaS market’s response has been consolidation through acquisition. Based on publicly reported deals, major vendors raised over ₹12,500 crore in investments and completed acquisitions totalling over ₹1,400 crore across the top five global providers in 2024 and 2025 alone. The pitch from every major vendor is the same: consolidate onto us.

The catch is that most vendors making this pitch are themselves Frankenstacks. They have grown through acquisitions, bolting on capabilities from different companies with different architectures. Swapping your Frankenstack for their Frankenstack is not consolidation. It is a lateral move that costs you 12 to 24 months of migration risk, change management overhead, and the very real possibility that your agents cannot take calls properly during the transition.

The enterprises that tried rip-and-replace learned this. Replacing a contact center platform is not like upgrading software. It is an operational transformation that touches every customer interaction, every agent workflow, and every compliance process. Meanwhile, your customers are still calling tomorrow morning.

The compliance dimension that multiplies the risk

If you operate in India’s BFSI sector, fragmentation is not just an operational headache. It is a regulatory liability.

The Digital Personal Data Protection (DPDP) Act requires organisations to maintain clear accountability for how personal data flows through their systems. When a customer’s data passes through multiple platforms, each with its own security posture, data retention policies, and access controls, demonstrating compliance becomes an exercise in documentation gymnastics.

Consider a real scenario: a customer calls your bank, gets transferred, follows up on WhatsApp, and later receives an outbound call from collections. If each of those interactions runs through a different platform with different logging and retention rules, your compliance team has to reconstruct the audit trail across all of them. Under PCI DSS requirements, that reconstruction is not optional. Under the DPDP Act, the customer has the right to know where their data lives and to request its deletion. Executing that request consistently across a fragmented stack is the kind of problem that keeps compliance officers up at night.

Every integration point between systems is a potential data governance gap. In a regulatory environment where TRAI and RBI are both tightening requirements around customer data handling, the cost of getting this wrong keeps going up.

A consolidated communication layer does not eliminate compliance obligations, but it gives you a single control plane to enforce them. One audit surface. One data governance framework. One place to respond when a regulator asks how customer data moves through your systems.

The pragmatic path: integration, not replacement

So if rip-and-replace creates more problems than it solves and the status quo is actively making your AI investments fail, what does work?

The answer that the market is slowly arriving at is an integration layer. Not another vendor promising to replace everything, but a platform that connects what you already run and fills the gaps between systems.

Here is how the three approaches compare:

The integration layer is not a compromise. It is a recognition that most organisations chose their CRM, their ticketing tool, and their telephony provider for good reasons. Those choices were not wrong. They were just never designed to talk to each other.

This is the approach that Exotel’s platform architecture was built around. As a CX ecosystem that unifies CPaaS, CCaaS, and conversational AI, Exotel works as the connective tissue between your existing investments. Your CRM stays. Your ticketing system stays. Your agents get a unified view through a single communication layer that sits across all of them.

What makes this work in practice is the API-first design. When your contact center infrastructure exposes clean APIs across voice, SMS, WhatsApp, and email, you do not need to rip out your CRM to get context flowing into your conversations. You connect them. The data moves. The agents see one screen instead of four. And critically, your AI finally has access to a complete, connected dataset instead of four partial ones.

For Indian enterprises, the UL-VNO licensing and telecom-grade infrastructure matter here. Running 70 million daily conversations across 7,100 businesses requires reliability that stitched-together integrations cannot deliver. A native platform that owns the full stack from telephony to AI gives you consolidation benefits without the consolidation risk.

The CCaaS market in India alone is projected to grow from ₹1,900 crore in 2024 to ₹6,800 crore by 2030, a 24.3% compound annual growth rate. That growth will not come from organisations buying yet another platform. It will come from organisations connecting the platforms they already have to the AI capabilities they need.

What the 0.5-point drop actually tells us

That 0.5-point decline in CX and EX ratings is not evidence that AI does not work. It is evidence that the industry has been solving the wrong problem.

For years, the assumption has been that contact centers need better AI. More capable models. Smarter copilots. Faster automation. The Deloitte 2026 data says otherwise. The AI is fine. The infrastructure underneath it is what is broken.

Companies that fixed their infrastructure first, the ones Deloitte identifies as “mature,” are twice as likely to be running agentic AI successfully. Companies that skipped infrastructure and went straight to AI are the ones dragging the average down.

The question for every CX leader reading this is straightforward: are you going to spend 2026 buying more AI tools to layer onto a broken stack? Or are you going to fix the stack first and let the AI you already have actually perform?

The companies that figure this out first will be the ones whose agents answer calls with full context, whose AI copilots actually reduce handle time, and whose compliance teams sleep at night.

Sources

• Deloitte Digital, “The Future of Service: The Age of Intelligent Experience,” 2026 Global Contact Center Survey (600 organisations, 3,000 consumers) Source
• Deloitte Digital, “2024 Global Contact Center Survey,” survey of 600 senior contact center leaders (conducted by Lawless Research) Source
• Metrigy, “Customer Experience MetriCast 2025” and “Single Platform for UC and Contact Center Expands Benefits,” 2025
  Source 1, Source 2
• Gartner, “Magic Quadrant for Contact Center as a Service (CCaaS),” 2025