When executives talk about artificial intelligence, the conversation almost always centers on the front of the house: chatbots, recommendation engines, customer personalization, and creative generation.
These are the visible, headline-friendly use cases, but they’re often the least reliable sources of long-term ROI. According to Karl Pinto, a veteran enterprise leader who has spent nearly two decades in incident management and digital operations, the true transformation is unfolding quietly in the background.
The Hidden Layer of Enterprise Value
Pinto has built his career around the less glamorous side of enterprise technology; the reliability layer. From his years at Dell and Salesforce to his leadership role at PagerDuty, he has watched major organizations grapple with operational complexity while chasing innovation at the edge. His vantage point is clear: consumer problems on the surface almost always trace back to operational inefficiencies beneath them.
“When a customer can’t check out on an e-commerce site or a digital service lags, that’s rarely a front-end issue,” he explains. “It’s a symptom of something deeper: system saturation, unmonitored dependencies, or poor escalation processes. The real opportunity is in preventing those breakdowns before they reach the user.”
Pinto points out that the irony of AI adoption is that the areas most in need of automation and intelligence, such as incident response, observability, and workflow orchestration, are often considered too ‘back-office’ to excite leadership teams. Yet, these are precisely the areas where AI can deliver consistent, measurable financial impact. “In operations,” he says, “the impact of AI compounds. A 10% improvement in detection speed can ripple across the entire business.”
The Economics of Reliability
Consider a global logistics provider processing thousands of time-sensitive transactions each hour. A single outage can delay shipments, disrupt customer commitments, and cost millions in contractual penalties. Pinto has seen AI-driven monitoring systems that predict incident clusters based on anomaly detection reduce downtime by double-digit percentages. “That’s the kind of impact that doesn’t make headlines but absolutely changes a balance sheet,” he says.
At PagerDuty, Pinto worked with enterprises that used machine learning models to triage incidents automatically, grouping related alerts and prioritizing those most likely to escalate. “The AI didn’t replace the operations team,” he explains. “It gave them time back — hours every day that were previously spent sorting noise. Those hours translate directly into dollars.”
That experience underscores a broader truth: the economics of reliability are less about innovation theater and more about operational margin. While front-facing AI often drives perception, operational AI drives permanence. Pinto believes the most forward-thinking enterprises are the ones now shifting investment toward reliability as a strategic asset.
“Every organization wants AI that impresses customers,” he says. “But the most sophisticated leaders realize that stability is a product, too, and it’s one that pays dividends every quarter.”
Why Leaders Overlook the Boring Stuff
Pinto acknowledges that it’s easy to see why operations rarely headline transformation initiatives. “Reliability doesn’t photograph well,” he says with a faint smile. “It’s hard to make a marketing video about systems not crashing.” But this tendency to chase visible wins often leads companies to underestimate how fragile their infrastructure has become.
He describes a pattern he’s observed in enterprise boardrooms: enthusiasm for generative AI pilots paired with hesitation to fund the foundational layers that support them. “You’ll see companies budget millions for AI-powered customer interfaces but still rely on manual processes for critical incident response,” Pinto says. “That’s like building a skyscraper on sand.”
Part of the issue, he explains, is narrative bias. Executives are conditioned to equate innovation with visibility. “When something is behind the scenes, it feels like maintenance instead of progress,” he notes. “But the truth is, the more automated your back end becomes, the faster you can innovate everywhere else.”
The companies that get it right, he says, are those that embed operational AI early as a foundation for reliability and trust. He references clients who have used automation to enforce governance policies, correlate performance metrics with business KPIs, and even forecast customer-impact risk before it happens. “Those are the stories that rarely get told,” Pinto says, “but they’re the ones that will define how enterprises scale in the next decade.”
The Quiet Revolution
AI’s most transformative contributions are arriving where there’s the least spotlight: in the systems that make other innovations possible. Pinto believes this “quiet revolution” will shape how enterprises think about technology investment for years to come. “You can only build great customer experiences on great operations,” he says. “And AI is finally making those operations intelligent enough to anticipate failure before it happens.”
He views this evolution not as a shift in technology, but as a shift in mindset. “The companies I admire most don’t treat reliability as an afterthought,” he says. “They treat it as an enabler of ambition. When the back end is predictable, the front end can be bold.”
Pinto’s perspective carries the weight of experience: nearly two decades of seeing what happens when organizations underinvest in reliability and overinvest in perception. His conviction is steady: the next competitive advantage in enterprise AI won’t come from dazzling interfaces or conversational models; it will come from operational systems that simply don’t fail.
“The irony,” he says, “is that the most valuable AI in the enterprise might be the one no one ever notices.”
--
This article is published under HackerNoon’s Business Blogging program.