Murthy Malapaka works in autonomous IT operations, turning enterprise ticket intelligence into business performance outcomes.getty​There is a goldmine sitting inside every enterprise's IT service management platform, and almost nobody in the boardroom knows it exists.I've spent years analyzing IT operational data across industries—insurance carriers, manufacturing plants and wealth management firms—and the pattern is usually the same. Hundreds of thousands of incident tickets flow through the system every year. They get triaged, assigned, worked and closed. The IT team measures mean time to resolve. The CIO reports uptime percentages, and the CEO never connects any of it to the business outcomes keeping them up at night: why claims processing is slow, why production lines idle unexpectedly and why client onboarding takes too long.This disconnect is what I call AI-naive IT operations; it's not because these organizations lack AI ambitions—most have pilots and proof-of-concepts running somewhere—but because they haven't yet recognized that their IT ticket data is a real-time signal of business process health. And until they do, every conversation about AI-native IT operations will lack the economic foundation it needs.The Intelligence Hiding In Your TicketsWhen we applied contextual intelligence to an insurance carrier's ticket data, we found 193,000 tickets over a year. Of those, the vast majority lacked any business context. A ticket would say "switch offline" but never "switch serving the claims processing floor where 15 adjusters handle auto claims." The IT team treated a network outage in a storage closet with the same urgency as one shutting down policy underwriting.The moment we mapped infrastructure to business functions, the picture changed. We could see nearly 3,000 tickets directly impacting claims operations and over 12,000 touching policy administration, endorsement, underwriting and renewal systems. Each one represented delayed claims, missed regulatory deadlines and lost premium revenue. The carrier's FNOL-to-payment cycle had stretched beyond 44 days—well above the 30-day benchmark—and IT incidents were a hidden contributor that no one was measuring.​​The same pattern emerged across other industries. In manufacturing, disconnected IT and operational data meant organizations couldn't see how incidents affecting MES, SCADA or ERP systems translated into production delays, lower OEE and unnecessary engineering effort. In wealth management, the impact surfaced differently: Outages affecting trading, portfolio management and client reporting became client experience, compliance and trust issues rather than simply IT incidents. The operational details varied, but the underlying problem was the same—without business context, leaders could see technical events but not their business consequences.​​From Intelligence To Economics​Once business context is visible, organizations can move beyond measuring IT performance in isolation and start optimizing the business outcomes IT influences. The goal isn't simply to identify inefficiencies but also to understand where operational friction is limiting growth, productivity and resilience.This shift enables leaders to optimize three areas:​1. Prioritize the initiatives with the greatest business impact.Rather than ranking projects by technical urgency alone, you can identify which recurring operational issues create the largest downstream business costs. For example, if 140,000 tickets consist primarily of noise alerts, false positives or self-resolving events, the opportunity isn't simply to automate ticket handling—it's to remove unnecessary work that slows teams and distracts them from higher-value priorities.By focusing on the operational friction that affects revenue, customer experience or business continuity, technology investments become easier to prioritize.​2. Optimize how skilled talent spends its time.Once repetitive work becomes visible, organizations can determine where automation delivers the greatest value. Across many IT environments, engineers spend substantial time reviewing auto-resolved alerts, repeating standardized diagnostics or manually routing incidents that intelligent systems can handle consistently.Reducing this low-value effort isn't about replacing people; it's about ensuring experienced engineers spend more time solving complex problems, improving systems and driving innovation.3. Connect AI investments to measurable business outcomes.These insights allow AI initiatives to be evaluated using business metrics rather than IT metrics alone. Faster incident resolution and fewer escalations remain important, but they matter because they influence outcomes executives already measure.For an insurer, that could mean improving the combined ratio through fewer claims delays and reduced compliance risk. For a manufacturer, it may mean increasing OEE by minimizing unplanned downtime and accelerating MES or ERP recovery. For a wealth manager, stronger system reliability can contribute to higher client retention and greater regulatory confidence.​Redefining The Commercial ModelThis intelligence can also help change how IT services should be priced and contracted. Traditional managed services models charge per ticket, per device or per FTE, all of which incentivize volume rather than outcomes. When you can see exactly which tickets are to be eliminated, which are to be automated and which genuinely require human expertise, you can build commercial models around business outcomes instead.This creates an opportunity to measure and price IT services according to the business outcomes they enable, whether that's claims uptime instead of ticket resolution, production availability instead of incident count or an autonomous agent's contribution to OEE instead of the number of alerts it processes.​This is what AI-native IT economics should look like: The provider's revenue is aligned with the client's business performance because both parties can finally see the connection.The Board Conversation That Needs To HappenEvery enterprise sitting on years of IT operational data has the raw material for this transformation. The question is whether leadership recognizes it. CIOs need to bring this intelligence upward, not as an IT dashboard, but as a business process risk map. And boards need to ask a question they've never thought to ask: What is our IT ticket data telling us about the health of our business?The answer, in every case I've seen, is more than anyone expected.​​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?