The real gap is getting that intelligence to the people responsible for action

The conversation about agentic AI and customer support often comes back to the same place: efficiency, faster resolution, lower cost, and better deflection. But every time I hear this, something bothers me. It is not because those outcomes don’t matter, but because they are only the surface of a much deeper structural problem that many do not see.

What I now understand after years of working in this industry is that support teams are not short of intelligence. They are often the richest source of real-world signals. They often see product failures before engineering surfaces them, early warning signs of a difficult renewal weeks before account executives, and adoption failures – in which a new AI feature or workflow isn’t landing as intended – in customers’ feedback.

The intelligence is there and always has been.

“As per the latest Capgemini Research Institute (CRI) Customer Service Transformation report, customer service remains one of the most underleveraged intelligence engines in the enterprise: while it generates rich, real-time customer insights, only around half of organizations systematically integrate these signals into decision-making—despite clear evidence that doing so drives outcomes such as 67% improvement in product development and 63% higher customer retention. At the same time, the rapid scale-up of Gen AI marks a critical inflection point, with 86% of organizations already exploring or deploying it, yet fewer than half feel prepared to deliver AI-powered customer service at scale. This underscores a fundamental gap: without structurally connecting customer service intelligence into enterprise workflows, organizations risk limiting AI to incremental efficiency gains rather than unlocking its full potential as a strategic driver of growth, experience, and competitive advantage.”

Which makes the problem even more striking: it is not about generating intelligence, but about ensuring it reaches the people who can act on it.

What’s missing is a way to reach the people who need it, and for that message to prompt action.

Think about how that intelligence moves today: a support case is resolved, the customer hangs up, the agent moves to the next ticket. The insight – the specific reason the feature didn’t work or the fact this customer has now called four times about the same thing – is ignored in a summary field, in a case system, in a function that has no structural mechanism to translate it into anything actionable.

But the bigger problem is what happens to it on the rare occasion that it does move. It gets compressed, aggregated, and averaged. By the time it travels through a quarterly business review, an escalation path, and an NPS summary, the specificity is gone. The timeliness is gone. The thing that made it actionable in the first place – that it was happening to this customer, about this product – is gone. What arrives on a product manager’s desk three months later is, at best, a directional signal, a general trend. The product issue has already caused damage.

That gap between when intelligence is generated and when it reaches someone who can act is not a technology failure. It is a structural one. Nobody designed the path.

The PM who could fix the issue that generated 200 support cases this quarter has probably never seen those cases, because there was no structural mechanism to translate a support pattern into a PM backlog item with enough context and urgency.

The account executive managing a critical renewal has probably not seen the eight escalations from that customer in the last 30 days, because support and sales data live in different systems, with different rhythms and organizations, and no one is accountable for the connection.

The customer service manager who could have intervened three weeks earlier, before the customer’s frustration became a real problem, didn’t have the signal – because no one designed the path for that signal to travel.

Support doesn’t have an intelligence problem. It has a reach and connection problem.

If you think it’s an intelligence problem, you invest in better summaries, smarter classification, faster resolution — and you get better-organized intelligence that still goes nowhere. Fundamentally unchanged is what it can do for the business.

But approached as a reach and connection problem, the question becomes how do you build the path from intelligence to action? How do you ensure that what a support team knows on a Tuesday morning reaches the PM, the AE, the CSM who needs it — in a form they can actually use?

AI can deliver value here. Faster triage, better knowledge retrieval, and lower cost per case are not trivial gains. But efficiency-first tooling solves the problem within the case. It does not solve the problem beyond the case. And the problem beyond the case – the structural disconnect between the intelligence support generated and the decisions the rest of the organization needs to make – is where the much larger opportunity lies.

Think about what changes when you solve it the other way around.

The PM wakes up on a Wednesday morning with a prioritized view of the product issues generating the most case volume — not a quarterly report, not an NPS number, but a live feed of what customers are actually hitting, in their own language, with enough context to make it immediately actionable. That same morning, an AE gets an alert that a customer’s escalation pattern over the last three weeks suggests a renewal conversation will be hard. And a CSM, rather than discovering that a customer has been frustrated for a month when the renewal meeting goes badly, gets a heads-up three weeks before that conversation, with enough context to intervene before the relationship is lost.

That is not a hypothetical. The intelligence to make all of that happen exists in the support system today. The question is whether it has a path to travel.

I want to be clear that this is not a technology question, because the conversation tends to go there quickly and stay there. The infrastructure to build this exists today. Agentic AI enables continuous synthesis of support signals and routing them to the right person in near real time. The capability is mature enough to move from proof of concept to production in a matter of weeks.

The hard part is structural. And I mean that genuinely, not as a polite way of avoiding the technology conversation. Building this requires support, product, and sales to share data and take accountability in ways most large organizations are not currently set up to do. It requires someone at the executive level to own the outcome across all three functions – not just support alone – and to change what success looks like for the support organization, from tickets closed to intelligence delivered, from cost per case to reach and connection.

The decision-maker is not the technology team. It is the person who owns the outcomes across all three functions and has the authority to redefine what success looks like.

Some will say complexity of data governance makes this prohibitive, that you need to solve the intelligence layer first, and that the integration effort is too high before the business case is clearer. I understand those positions. I do not believe in them.

The data governance question is real — it deserves serious treatment. Sharing customer case data across support, product, and sales functions involves genuine privacy and compliance considerations. In some organizations, the legal and data architecture work is the longest part of the build. I am not minimizing that. But complexity is not the same as intractability. The organizations that are building this are not ignoring governance – they are treating it as a solvable design problem, not a reason to wait. The answer is not to put governance ahead of the concept. It is to design the concept based on governance requirements.

What I find genuinely compelling is the logic of compounding. Once the path from support intelligence to organizational action is built and running, every case processed makes the organization smarter. The product improves based on real-world signals. The revenue team gets ahead of risk. The customer success motion becomes proactive rather than reactive. And that institutional intelligence – the continuously improving, real-time understanding of customers – is genuinely hard to replicate quickly, because it takes time to build and let it run. The technology can be licensed. The intelligence that accumulates over time cannot.

Hence, the question is not how much faster you can close a ticket. The questions are: after the ticket closes, where does the knowledge go? Who receives it? In what form? And what do they do with it?

The answers to those questions are the actual opportunity. And most organizations haven’t asked them yet.

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