RTInsights

When enterprise LLM use was mostly chat, its worst failure was a wrong answer that a human read, caught, and corrected. Embarrassing, occasionally costly, usually recoverable. The moment that system can call APIs, run queries, change configurations, and trigger workflows, the failure stops being a wrong answer and becomes a wrong action. It can also become many wrong actions at once.

Chatbots are great for drafting a function and wishing you luck, but not helpful when it comes to more nuanced requests. But imagine you had a software developer on permanent standby. Someone you could turn to and say, “Hey, I have a question about this,” or “Can you build this for me?” and they’d just do it. Now imagine that friend works ten times faster than a human, never sleeps, and can read any codebase you point them to.

A facility manager overseeing a hospital, airport, manufacturing plant, or commercial campus rarely worries about a lack of maintenance data. Modern facilities are already instrumented with sensors, building management systems, energy meters, and connected equipment that continuously generate operational information. Yet unplanned downtime remains one of the most persistent and expensive challenges in facility operations. The issue is not visibility.

Keeping pace with news and developments in the real-time analytics and AI market can be a daunting task. Fortunately, we have you covered with a summary of the items our staff comes across each week. And if you prefer it in your inbox, sign up here! MongoDB announced new capabilities that address the two reasons enterprise AI projects routinely stall before production: retrieval that isn’t accurate enough to trust and infrastructure that can’t meet compliance requirements.

For nearly a decade, enterprise IT leaders have followed a cloud-focused approach, leaning on massive public cloud infrastructure to power their workloads. However, as the tech industry matures, the problems with cloud infrastructure not only become more apparent but also more expensive. As a result, IT leaders are looking beyond these traditional set-ups for more robust solutions.

Most engineering leadership teams are grappling with the fact that reliability costs too much, teams are burnt out, and they feel something has to change. Typically, teams resort to optimization: smarter alerting, faster runbooks, better dashboards, and more training. Teams invest heavily, see modest gains, and then end up right back where they started when the next incident hits. Optimization doesn’t fail because of execution. It’s that the underlying model is broken.

Every engineering leader running an AI-accelerated simulation program has had some version of the same conversation. The board wants faster discovery cycles. The CFO wants a defensible ROI story. The platform team wants to ship. And somewhere in the middle, a senior researcher quietly mentions that the workflow has been broken for three weeks because a dependency upgrade silently changed the behavior of a pretrained model. This is the part of the cost structure that nobody put in the business case.

Adoption of AI stands at a couple of paradoxical inflection points. One, amidst the categorical reality that generative AI and agentic AI must move from experimentation to production, studies show that just about 30 percent of organizations have done so. If this percentage is an indication of companies’ cautiousness in rolling out AI, it does not reflect their commitment to governance—close to 48 percent of them do not monitor their production of AI systems for accuracy, drift, or misuse.

As enterprises invest aggressively in agentic AI, expectations for transformational business outcomes are rising just as quickly. Fortune Business Insights predicts agentic spending will surge by 25% in 2026. And in our own recent survey, 71% of senior leaders think agentic will deliver ROI faster than any previous wave of technology, including cloud, RPA, and early enterprise AI. Those are bold bets.

The most disruptive system failures are often not caused by a lack of capacity. They occur when systems are subjected to unexpected, peak loads that create bottlenecks in accessing shared storage. This dynamic plays out across ticketing platforms, retail flash sales, and live event coverage when millions of users converge on the same data at the same moment. As audiences become more connected through streaming, mobile apps, and e-commerce, the gap between average and peak demand keeps widening.