If Customer Support Isn’t Proactive, What’s the Risk to the Customer?

When technology drives nearly every business outcome, reactive support isn’t just outdated, it’s dangerous. Waiting for systems to fail before helping customers is like waiting for a worn tire to blow out instead of replacing it before the worst happens. Yet this remains the status quo across much of the enterprise IT landscape.

We’ve entered a new era of expectations. As DevRev recently noted, 67% of customers now prefer proactive customer service—anticipating issues before they arise—over reactive problem resolution. 

The companies that still lean on reactive support models are quietly bleeding trust, loyalty, and operational resilience—or facing major migrations off their platform as savvy leaders grasp that switching vendors isn’t all that disruptive after all. 

The Cost of Waiting for the Alarm

Reactive models are easy to spot. They use aging systems that depend on administrators spotting a problem, logging a ticket, and waiting. Every delay compounds the impact: downtime, lost productivity, and reputational damage. 

As COPC highlighted earlier this year, “today’s customers value effortlessness more than recovery”—meaning even if the issue is resolved quickly, the friction has already degraded loyalty.​

Legacy storage vendors built empires on this reactive dynamic. Their 40‑year‑old architectures weren’t designed for real‑time visibility or telemetry‑driven automation. Instead, they rely on an elaborate patchwork of scripts, escalation hierarchies, and human heroics to keep predictable failures at bay. 

And while it may look impressive that “Bob in IT” wrote nightly monitor scripts, what happens when Bob’s not around? Institutional knowledge becomes institutional risk.

The False Comfort of “Good Enough”

NetApp’s AutoSupport, for example, has been around for more than two decades. It automatically transmits device telemetry to enable predictive support, detecting roughly 98% of critical issues before customers notice¹. By that measure, it’s remarkably good—but here’s the catch. The technology’s value depends entirely on whether customers turn it on, configure it right, and interpret its recommendations. That’s still a manual, reactive bridge between vendor and operator.​

Dell, meanwhile, takes a different tack. Dell EMC support remains heavily reliant on post‑warranty service programs to extend equipment life and defer forced refreshes. In theory, this helps customers “avoid premature upgrading.” In practice, it entrenches aging infrastructure, ensuring more service contracts, more dependencies, and more frustration for customers who simply want systems that manage themselves.​

Both examples highlight a fundamental truth: Automation for the vendor’s convenience is not the same as intelligence for the customer’s benefit.

Proactive Support Should Be a Design Philosophy

Proactive customer support isn’t a feature; it requires an infrastructure instrumented by telemetry, a data estate capable of pattern‑level detection, and an operating model that treats insight as the first line of defense.

Modern AI‑enabled support creates a two‑way, coordinated dialogue that helps identify and resolve issues early, minimizing disruptions. That’s what real partnership looks like—a loop of visibility, anticipation, and mutual action between vendor and customer.​

This may sound philosophical, but the companies that have invested in data‑driven, telemetry‑fueled ecosystems are widening the performance gap from those that haven’t. In some cases, they’re using machine learning not just to react faster, but to avoid having to react at all. 

“The goal is basically to say, ‘We have a bunch of data that we have in Pure1, and my goal is to try to use that data to provide value to the customer at the end of the day,’” says Farhan Abrol, Head of Machine Learning at Pure Storage. “In this case, the easiest way to really wrangle that data is to use machine learning.”

Data from Nextiva’s 2025 survey shows that organizations implementing predictive support models see a 47% reduction in downtime and 3X improvement in customer retention.​

Meanwhile, reactive infrastructures are becoming liabilities. Every manual monitoring workflow, every siloed toolchain, every “hero admin fix” is a tax on innovation. When hybrid or edge environments expand, the risk compounds. 

The question isn’t what it costs vendors to build proactive support—it’s what it costs customers when they don’t.

Proactive Support = “Predictive Trust”

Leaders across industries—from automotive to finance—are embracing this shift. Airlines working with predictive platforms report that predicting defects and maintenance needs ahead of time helps keep aircraft in service and flights running on schedule—a major passenger-experience benefit. Banks are deploying AI to resolve customer frustrations before complaints are filed. At Pure Storage, we use telemetry data from all connected arrays to improve customers’ arrays and performance. 

The through line is simple: Trust is built not by how companies fix problems, but by how few problems customers ever experience.

Coming Full Circle

In enterprise IT, the same truth applies. Storage vendors that harness data for predictive support will set the standard for resilience, empathy, and partnership. At Pure Storage, we believe customers shouldn’t have to live with an experience—they should love it. Because in the end, the real question isn’t whether your vendor can fix your systems. It’s whether they care enough to make sure you never have to.

¹https://www.netapp.com/media/99118-netapp_autosupport.pdf