AI has progressed from experimentation to real production use across enterprise systems. As Cockroach Labs’ recent report, The State of AI Infrastructure 2026, showed, 98% of global technology executives have reported at least one AI project moving from pilot to production in the past year. It’s clear that this shift is well underway, but not without introducing new risk.
Unlike previous technology booms, AI workloads aren’t following human usage patterns. AI brings automated, constantly running, machine-driven demand that is likely set to — or already — overwhelm infrastructure originally designed for human speeds. In this era, the issue goes beyond deploying AI and focuses on whether infrastructure can survive the scale of AI demands.
Infrastructures are already breaking under the weight of human activity
Running AI workloads successfully introduces a new set of challenges across the enterprise that haven’t been handled previously. Even prior to the AI era, Cockroach Labs’ research shows that enterprises are close to hitting an architectural breaking point.
The latest State of Resilience report found that 100% of leaders across practically all industries are experiencing outages, averaging 86 outages per year. 83% of leaders believe their data infrastructure will reach its limits for supporting AI growth within two years, with 34% saying it won’t even hold up for the next 11 months. And all of this is before AI demand accelerates further.
AI workloads operate at machine speed 24 hours a day, 7 days a week. The rapid emergence of these autonomous tools will see limits hit before we know it. As tech leaders’ faith in current infrastructure is already flailing, it’s clear that they aren’t even remotely prepared to keep up with continuously operating AI agents. At this stage, modernizing infrastructure is no longer a nice-to-have, but a necessity to keep enterprises afloat.
Without upgrades, financial and reputational costs are on the line
Failure to modernize foundations risks high operational, financial, and reputational costs when failures occur. Data shows that over half (57%) of organizations have estimated that a single hour of AI-related downtime would cost USD100,000 or more. Even if systems were down 0.1% of the year, that would still result in ~9 hours per year, potentially costing USD900,000 or more, depending on the organization’s size. The bigger the organization, the bigger the cost. This hits especially hard, given it’s something most companies don’t budget for.
The cost is steeper for outages that occur during peak times, too, such as e-commerce events like Ticketmaster pre-sales and Amazon Prime Day, and sports betting around popular events like the FIFA World Cup. The potential loss from an outage is one they’ll never get back, along with losing customer trust in the reliability of their operations during the time when excitement and expectations for their product were the highest.
Despite almost half of consumers understanding and accepting occasional website slowdowns, over a quarter (27%) expect seamless operations even during peak periods, setting a high bar for infrastructure to meet. Additionally, 48% of consumers will consider switching brands if they experience repeated technical errors, putting pressure on tech leaders to modernize their infrastructure to support the high demands of AI workloads while meeting consumer expectations and minimizing financial losses.
Setting up for success: Redesigning operations, governance and infrastructure in one
To ensure success as AI-driven workloads increase, organizations must take action now, prioritizing three key elements to future-proof infrastructure:
Architecture designed for continuous support demands: Legacy systems will not thrive in the AI era. Tech leaders must adopt modern architecture, such as a distributed SQL database, to give enterprises the elastic scaling needed to evolve alongside AI workloads and detect failures without human intervention, mitigating the rising costs of outages. Data layer resilience: Resilience must be at the core of infrastructure operations to survive AI demands. It’s all about ensuring operational demands can be sustained despite the high stress on data architecture, forcing leaders to invest in modernized techniques now. To ensure a resilient data layer, leaders must move beyond systems built only for human activity, implementing architectures with built-in, distributed, multi-regional data layers to maintain consistency even under stress.Stress testing and downtime modeling: Even the most advanced foundational systems can slip under the immense pressure of AI, making benchmarking a critical component to see how infrastructure performs under all circumstances (even the most unlikely ones). This level of understanding starts with benchmarking measurements, such as performance under adverse conditions. Performance under adversity not only measures throughput under normal conditions, but also adds real-world stressors that test the database through the kinds of outages that keep operators awake at night. Extensive testing, even under the most extreme conditions, is the only way to truly know whether your infrastructure will survive a major outage.
Taking all of these components into consideration when modernizing for AI-scale is the secret sauce that will allow organizations to build a strong foundation that’s ready for both the near and long-term future.
Being among the winning group in the enterprise
The next era of AI adoption will divide companies into two groups: those who successfully scale AI, and those who struggle to stay afloat. While 63% of tech leaders already say their teams are underestimating how quickly AI demands will outpace existing data infrastructure, none are actively preventing mass failures. And with 74% of CIOs saying their roles will be at risk if AI ROI isn’t achieved within the next two years, now is the time to pay attention.
The message is clear for those who want to successfully scale AI in 2026 and beyond: you must start with the database layer, keeping resilience and continuous demand top of mind. It’s nonnegotiable. Limit financial and reputational disasters before it’s too late.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/rudall30