Operations | Monitoring | ITSM | DevOps | Cloud

Most enterprise AI deployments were never designed to coexist. They were designed to prove a point, respond to a board directive, or secure a budget. The result, two years into the generative AI cycle, is an expanding estate of disconnected models, fragmented pilots, and overlapping capabilities that collectively deliver far less value than the sum of their parts. HFS Research calls it "death by a thousand POCs". The more precise description is architectural negligence at an enterprise scale.

Your customer started their renewal evaluation on a Tuesday in March. You did not know about it. Their CFO had asked the procurement lead to "look at alternatives" during a quarterly budget review. Three weeks later, a competitor's SDR was on a discovery call with their head of operations. By the time your CS platform's health score turned amber, six weeks had passed inside their building. This is what most CS leaders miss when they evaluate early warning systems.

By 2030, enterprise software sales through AWS, Azure, and Google Cloud marketplaces are projected to grow to $163 billion. Compared to $30 billion in 2024, this is a 29% CAGR, and one of the most aggressive route-to-market shifts the software industry has ever seen. Add the distributor and MSP marketplaces, and the picture gets even larger.

If you’ve been online in the last fortnight, you’ve probably seen ServiceNow’s “Kevin” memo, the fictional 2028 post-mortem about an enterprise where the AI agents won, the governance team was eliminated, and a single AI governance lead named Kevin spent two years filing risk assessments that were auto-resolved before anyone read them.