In pursuit of the customer Holy Grail - why organizations must finally tackle the cost of dis-connected CX

Some 87% of contact center leaders believe that a true customer 360 is unattainable - and it’s not hard to see why, according to the latest research we conducted at Valoir.

The industry has been talking about the “360-degree customer view” for decades, but at this point, it’s less a goal and more a punchline – and it’s hindering both our contact center productivity and our ability to truly unlock the value of AI.

Valoir’s latest research finds that the average organization is juggling 20 different integrations in a desperate attempt to stitch together customer data, yet only 58 percent of that data is actually accessible in a single system. That gap isn’t just an inconvenience: it’s the root cause of rising costs, stalled AI initiatives, and underperforming customer experiences.

On paper, most contact centers look modern. They’ve adopted CRM platforms, layered in Contact Centers-as-a-Service (CCaaS), added new digital channels, and started experimenting with AI. 

But under the hood, they’re running on fragmented architectures that were never designed to work together. Marketing bought one system, Sales another, Service a third, and then onto that was piled chat, SMS, social, and WhatsApp. What made sense in a voice-first, on-premise world has become a liability in a multi-channel, AI-driven one.

The cost of fragmentation is mostly hidden.

Organizations are spending nearly $1,000 per user annually on CRM, CCaaS, infrastructure, and integrations, with over a quarter (26%) of that going just to integration technologies.

But the real drain isn’t software—it’s people. The average organization allocates 0.07 support full-time equivalent (FTE) per service rep, and those teams are drowning in data work. Forty-one percent of their time goes to data quality, synchronization, and governance, while another third (33%) is spent maintaining integrations. That’s nearly three-quarters of their effort just keeping the lights on.

And we haven’t even gotten to the productivity hit. Service reps today have to navigate nine different applications just to do their jobs. Every swivel-chair moment – every tab switch, every duplicate lookup – is friction. It slows resolution times, increases error rates, and erodes both employee and customer experience. Leaders know this. They estimate that a unified view of customer data could boost productivity by nearly a quarter (24%). 

Yet instead of simplifying, many organizations are doubling down on complexity in the name of AI. This is where the story takes a turn. AI has become the top priority for modernizing customer service, but it’s also exposing just how broken the underlying data layer really is. You can’t train, ground, or trust AI on incomplete, inconsistent, or conflicting data. And right now, most organizations are trying to do exactly that.

In fact, 30% of service leaders say data integration is the single biggest barrier to deploying AI, ahead of cost, technology limitations, and even trust. We don’t have an AI problem, we have a data problem.

And it’s an expensive one to fix. Leaders estimate they would need to spend an additional $1.1 million, or $2,659 per service rep, to achieve a true 360-degree view . In an environment where budgets are tightening and ROI scrutiny is intensifying, that kind of spending is a hard sell.

At an inflection point

The next phase of AI in customer service won’t be defined by better models or flashier demos. It will be defined by who gets their data house in order, and who doesn’t.

We’re already seeing the early signs of a shift. Leading organizations are moving away from brittle, integration-heavy architectures toward platform consolidation, unified data models, and shared data layers. They’re prioritizing real-time access to consistent, contextual data across the enterprise. Our report notes:

Organizations that get data right will drive consistent and repeatable AI results, and those that don’t will find it increasingly difficult to deploy AI at scale and achieve AI -related business benefits . We expect the market to shift toward platform consolidation, unified data models, and AI-native analytics architectures that bring operational and interaction data together in real time. Rather than stitching together dozens of applications with brittle int egrations, leading organizations will prioritize standards-based shared data layers and integrated customer platforms that enable AI to access reliable, contextual information across the enterprise.

Overall, organizations are  recognizing that the fastest path to AI value isn’t adding more tools, it’s eliminating the friction between the ones they already have.

Because when you reduce the number of systems, clean up the data, and give AI something reliable to work with, everything changes. AI stops hallucinating and starts helping. Agents stop hunting for information and start resolving issues. And customers stop repeating themselves and start getting answers.

As our report observes:

When contact center and service teams can spend less time on day -to-day data quality and integration efforts, and service agents can spend less time context -switching between systems, everyone – including customers – win.

My take

It turns out the 360-degree customer view wasn’t a myth; it was just buried under two decades of technical debt.