Operations | Monitoring | ITSM | DevOps | Cloud

How governed AI agents inside a Databricks perimeter can turn customer context into revenue for Communications, Media, Entertainment, and Gaming (CMEG) industries.

Your Mean Time to Resolution (MTTR) has likely stayed flat for three or four quarters. The investment was real: scheduling tools, dispatch optimization, new training modules, and more technicians. Operations reviews still dissect response time, travel time, and wrench time. The metric still refuses to move. Most field service leaders measure MTTR from the start of the repair to the moment the asset returns to service.

When profits miss plan, marketing is the budget line most likely to be cut. The CMO Survey found that marketing expenses are cut 45.4% of the time in that scenario, more often than any other expense category. Most marketing teams have more data than ever. Finance still discounts much of it, because few of those numbers connect spend to business outcomes. The pressure is structural. Gartner’s 2026 CMO Spend Survey shows marketing budgets flat at 7.8% of company revenue.

A roadmap for the Telecom, Media, Entertainment, and Gaming industries.

Most technology distribution leaders can show stable renewal performance. That number rarely tells the full retention story. According to Forrester, current customers account for 61% of B2B revenue through renewal and expansion. Retention is a revenue-protection issue, not a dashboard metric reviewed after the quarter closes. Beneath those numbers, a quieter problem builds across the channel ecosystem.

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.