Operations | Monitoring | ITSM | DevOps | Cloud

A second service call rarely looks like a strategic failure on a dashboard. It appears as another work order, another technician assignment, or another customer follow-up. That accounting view makes repeat visits look operationally normal, even when they are financially destructive. The first visit carries the visible cost of dispatch, labor, travel time, and parts handling. The second visit carries all of that again, plus customer downtime, SLA pressure, escalation risk, and lost technician capacity.

Every major enterprise has placed its AI chip. McKinsey pegs the annual economic potential of generative AI at $2.6 to $4.4 trillion. HFS Research sizes the Services-as-Software market at $1.5 trillion by 2035. Gartner forecasts that 40% of enterprise applications will embed task-specific AI agents by the end of this year, up from under 5% in 2025. These are the field reports of a reordering already underway.

At its annual customer conference in June 2026, the vendor holding the largest revenue share in the global CCaaS market announced a full repositioning of its platform around agentic AI. Its stated reason: “the era of bolted-on AI is over” (CX Today, June 2026).

Ask most enterprises why their AI program hasn't moved past pilots, and you'll get an answer about the model. It's not accurate enough, not fast enough, not cheap enough yet. Srikanth Akkiraju, who has run transformation at Philips and now at ServiceNow, doesn't buy it. In a recent fireside conversation with iOPEX, he made the case that the model was never the problem. The problem is that most enterprises haven't decided what they actually want AI to change. Five questions came out of that conversation.

Every AI engagement runs on two clocks, and they no longer agree. The first is the intelligence clock, and it runs fast. The world it keeps time with re-renders every quarter. Models improve, inference costs fall, automation tightens, and the cost of producing a unit of work keeps dropping. This is the clock an enterprise believes it is buying when it invests in AI. The second is the contract clock, and it stopped years ago.

For two years, the enterprise's question about AI was which model to buy. That question is already settling. Frontier capability is becoming abundant - rentable by anyone, swappable in an afternoon, and roughly identical in your hands and your competitor's. An advantage everyone can buy is not an advantage. What can't be bought is the thing underneath it: a system that has learned how your business actually works - the intelligence your enterprise accumulates and no competitor can replicate.

Databricks built the most capable AI platform the enterprise has ever seen at Data and AI Summit 2026. The data on who actually earns a return from it tells a more sobering story. Here is what changed at the summit, and what it means for leaders already on the platform. Ten minutes into the Data + AI Summit 2026 keynote, Ali Ghodsi, CEO of Databricks, said something most enterprise leaders were not prepared to hear: AGI is already here.

A senior CSM managing 25 enterprise accounts can run effective QBRs, identify expansion signals, and execute renewal preparation with discipline. At 50 accounts, the function becomes reactive. Reactive customer success is the most reliable leading indicator of declining NRR (Net Revenue Retention).‍ The default response has been headcount. It no longer works.

At least 30 minutes disappear before the average field technician can begin the actual repair. That time goes into searching for part numbers, procedures, and documentation across fragmented systems. Across 1,000 technicians and 250 working days, even this conservative estimate adds up to at least 125,000 hours of productive capacity lost to lookup activity. Yet this loss rarely appears as a separate line item on any field service P&L. This is a critical information architecture problem.

ServiceNow's internal IT operations now resolve more than 90% of employee requests through autonomous agents. The platform that demonstrated this in Las Vegas earlier this month is the same platform sitting in your environment right now. So why isn't your operation running the same way? This is the question every CIO should have walked out of Knowledge 2026 with.