糖心原创

Every team succeeded. The customer still left.

Leading organizations are moving beyond signal collection toward something more valuable: a single layer of multi-signal intelligence that gives every decision, human or AI, a complete understanding of the customer. The best-run organizations aren’t asking whether they have enough data. They’re making sure every decision is informed by the right context.

You’ve lived a version of what happens when that connection is missing. It just didn’t have anything to do with AI yet.

A loyal customer tries to book a getaway in your app. The redemption fails. Marketing sees a bounce and logs it as low intent. He’s not uninterested. He’s frustrated.

He calls support to finish the booking. The agent gets him booked but has no visibility into the failed digital experience, so the response is generic instead of personal.

He asks for an early check-in. The request never reaches the hotel because it lives in a system the front desk can’t see.

He arrives to find his room isn’t ready.

Four teams. Four moments. One relationship, falling apart in real time. None of them could see what the others already knew. , 糖心原创’s CEO, puts it:

Marketing hit its funnel targets. Support met its SLA. Operations prepared the room on schedule. On paper, nothing failed.

In reality, that’s revenue leakage happening in plain sight. The signals existed all along. The connection did not.

And the gap between believing it’s solved and it actually being solved is where the money disappears.

Here’s the simplest way to quantify the opportunity:

That number is uncomfortable, not because it has the potential to be a sizable number, but because most organizations can’t calculate it at all. The strongest programs don’t estimate this once. They’ve built the infrastructure to know it continuously.

The fragmentation tax.

That story isn’t an outlier. It’s a pattern with a name: the fragmentation tax. Every disconnected customer moment represents value that was never recovered, and it shows up in three ways.

False confidence. One global software organization tracked its support satisfaction scores for years. They climbed steadily. Reps were friendly, fast, and well-reviewed. Renewals kept falling anyway. When someone finally read the chat transcripts instead of just the scores, the pattern became obvious: customers praised the support experience while writing three, four, even five times about the same unresolved product bug. The team wasn’t failing. Success in one metric masked risk somewhere else.

Leakage. A national insurer built a campaign to cross-sell home insurance to existing auto customers. The sign-up form required customers to re-enter information the company already had. Customers abandoned the flow and called support instead, where an agent manually completed what the digital experience should have done automatically. The campaign generated revenue. It also generated higher-than-average cart abandonment, unnecessary service costs, customer effort, and operational friction.

Symptom management. When redesigned its website, customers who couldn’t log in to pay their bill started calling in instead, and the frustrated ones escalated further to a live agent. Looking at digital, phone, and live support separately suggested three unrelated issues. Looking at them together revealed one customer problem, showing up three times as bad demand — more chats, more calls, more servicing costs, more customer effort — because the real root cause, digital friction, was never addressed. Fix the upstream issue, and every channel recovers.

says that many CX teams have become trapped in measurement without meaning, struggling to translate experience data into business improvement. The firm warns that as many as 15% risk entering what it calls a “death spiral” by continuing to invest in better dashboards instead of better connections and more context. Customers are already feeling the effects. Nearly 44% say they had to repeat information during their last interaction with a company.

AI raises the stakes.

Every AI system inherits the strengths and weaknesses of the information beneath it. Built on connected, multi-signal intelligence, AI can connect the dots, identify the linkages, and deliver faster, smarter, and more consistent decisions. Built on fragmented signals, it simply automates today’s blind spots at machine speed. What was once an operational inefficiency quickly becomes an enterprise-scale liability.

This is why the world’s largest enterprise software companies are investing so heavily in solving the problem. ServiceNow found that , citing it as the number one barrier underneath every AI failure.

has reached a similar conclusion from a different direction, describing enterprise data as “siloed and without meaning” while investing in a real-time context layer that gives AI the structured business understanding it needs to reason effectively.

When two of the world’s largest enterprise software companies independently invest in solving the same problem, it’s worth paying attention. They’re responding to where enterprise AI is actually creating value.

Data-rich. Context-poor.

Most organizations think of their experience program as a listening exercise. It’s actually something much more valuable: context. A governed, connected understanding of who a customer is, their relationship with your company, and what happened five minutes — and five departments — ago, built from every signal the business generates. It also means connecting that understanding to the operational measures, outcomes, costs, and revenue tied to each experience. That’s the foundation people and AI need to make better decisions.

Most enterprises have already achieved complete signal capture. Few have built the experience context? that makes those signals actionable. That’s the real intelligence gap.

As Sid Banerjee, 糖心原创’s Chief Strategy Officer, puts it:

Fabrice Martin, 糖心原创’s Chief Product Officer, agrees, and puts it more colorfully:

The data on who is already there, and what it is worth, makes the case even more concrete.

The ten percent already pulling ahead.

Only one in ten companies have turned experience context? into a competitive advantage. BCG found that fragmented customer data remains the biggest barrier to delivering truly personalized experiences. The organizations that overcome that barrier are growing revenue 10 percentage points faster annually and are positioned to see , according to BCG’s October 2024 research.

set out to stop guessing with fragmented data. Six survey touchpoints became 53 active listening programs spanning nearly 70 million customers. Today, four billion digital logins and three billion conversations with Erica — its virtual assistant — feed a single, connected view of the customer instead of a dozen disconnected ones. As the bank’s SVP of Client Experience put it:

The pattern appears in 糖心原创’s research as well. Organizations connecting ten or more signal sources are more than five times as likely to say they can calculate the ROI of their experience program than those working from five or fewer (92% versus 73%). Contextual intelligence doesn’t just create better experiences. It makes business impact measurable.

The agentic era won’t wait.

The signal set is expanding. Soon, some of the most important conversations about your customers won’t involve your customers at all. A calendar assistant will negotiate a delivery window with your logistics platform. An expense agent will dispute a charge with your billing system. Increasingly, software will be interacting with software on your customers’ behalf.

The organizations that succeed won’t treat those interactions differently. They’ll ensure every decision—made by a person or an AI agent—is guided by the same customer understanding and experience context?. Multi-signal intelligence becomes the connective tissue between people, AI, and the growing ecosystem of software acting on a customer’s behalf.

Building that intelligence is only the beginning. The organizations creating the most value don’t stop there. They operationalize that intelligence across every team, every workflow, and every AI interaction.

Five years from now, two kinds of organizations will exist.

One will have built a layer of multi-signal intelligence that grows smarter with every interaction. Their AI will create more value because it’s reasoning from experience context?, not fragments.

The other will still be stitching together disconnected systems, wondering why AI hasn’t delivered the business value they expected. They won’t fall behind because they failed to invest. They’ll fall behind because they never connected the data they had.

The next chapter of experience management won’t belong to the organizations deploying the most AI. It will belong to the ones with the right grounding and intelligence powering it.