Somewhere in a boardroom right now, a CTO is greenlighting an AI initiative because the board asked about it. Not because the business case is airtight. Not because the data infrastructure is ready. But because nobody wants to be the one who "missed AI."
Sound familiar? It should. We watched the exact same movie play out with cloud and a lot of organizations are still cleaning up the mess.
Remember when "cloud first" was the mandate? Every workload, every application, every net new project… cloud was the default answer before anyone even asked the question. Analyst firms cheered. Vendors fueled the frenzy. Entire IT strategies were rewritten around a single premise: “get to the cloud as fast as possible.”
And then reality showed up.
Organizations discovered that moving everything to public cloud wasn't the cost optimization play they were promised. Hidden egress fees piled up. Performance for latency sensitive workloads degraded. Compliance teams scrambled to understand where regulated data actually lived.
Roughly 21% of enterprise cloud infrastructure spend in 2025 (around $44.5 billion) was wasted on underutilized resources. The majority of enterprises say they still haven't seen substantial value from their cloud investments.
The correction was painful but predictable. "Cloud first" gave way to "cloud smart" a more deliberate approach that asks “WHERE” a workload should run based on cost, performance, compliance, and operational fit rather than defaulting to public cloud for everything. Gartner estimated that 60% of organizations that adopted a cloud first posture would shift to cloud smart, and that prediction has largely played out. Hybrid and on premises infrastructure aren't signs of being behind the curve… they're signs of maturity.
The lesson was expensive but clear: enthusiasm without discipline creates technical debt, budget overruns, and strategic whiplash.
Now swap "cloud" for "AI" and watch history repeat itself.
Global AI spending is projected to reach $2.5 trillion in 2026, according to Gartner… a 44% jump from the prior year. Capital is flooding into infrastructure, models, and tooling at a pace that makes the early cloud rush look cautious. But the returns? They're not keeping up with the investment.
An MIT study found a staggering 95% failure rate for enterprise generative AI projects, defined as initiatives that hadn't produced measurable financial returns within six months. Meanwhile, 61% of senior business leaders say they feel more pressure to prove AI ROI now compared to a year ago, and more than half of investors expect to see positive returns within six months or less.
Here's the uncomfortable truth: most AI first strategies aren't strategies at all. They're reactions. They're driven by competitive anxiety, vendor hype, and board level FOMO… the same forces that pushed organizations into reckless cloud migrations a decade ago.
Gartner's own analysts have placed AI squarely in the "Trough of Disillusionment" throughout 2026. That's not a death sentence for AI… far from it. It's a signal that the market is transitioning from experimentation to accountability. And that transition demands a fundamentally different approach.
Being AI smart doesn't mean being AI skeptical. It means treating AI with the same rigor you'd apply to any enterprise grade capability. It means asking harder questions before writing the check.
The most successful AI deployments aren't the ones with the most sophisticated models. They're the ones that started with a clearly defined business outcome… reducing mean time to resolution in IT operations, automating invoice processing, improving threat detection accuracy… and then determined whether AI was the right tool. Sometimes it is. Sometimes an automation workflow or a simple rules engine does the job at a fraction of the cost and complexity.
AI doesn't run on enthusiasm. It runs on clean, accessible, well governed data. It runs on modern infrastructure that can support inference workloads where they need to happen in the cloud, at the edge, or on prem. Organizations that skipped the hard work of data modernization and infrastructure readiness are now discovering that their AI pilots can't scale. You have to have strategic data management, modernized compute, and cloud native foundations to take real advantage of AI.
Nutanix made this point well in a recent blog: AI applications will become business critical faster than any other application class we've seen. That means they need the same operational discipline (resiliency, day two operations, patching, monitoring, security) that we've spent years building for traditional enterprise workloads. The novelty of AI doesn't exempt it from the fundamentals.
AI introduces attack surfaces that traditional security frameworks weren't designed for: training data poisoning, model manipulation, inference endpoint exposure, and the sprawl of sensitive data across retrieval pipelines. A patchwork approach to AI security won't hold. The policies, access controls, and observability need to be consistent across every environment where AI runs… cloud, datacenter, and edge.
The number of AI pilots you've launched is not a KPI. The number of models you've deployed is not a KPI. What matters is whether those deployments are producing measurable, repeatable business value. If you can't draw a straight line from an AI initiative to a financial or operational outcome, it's a science experiment… and your board knows the difference.
Think about how the cloud smart correction played out. Organizations stopped asking "how do we get to the cloud faster?" and started asking "what's the right environment for this workload?" They built FinOps practices to get visibility into spend. They embraced hybrid architectures not as a compromise, but as a deliberate strategic choice. They matured.
AI needs the same course correction and the organizations that make it early will have a massive advantage over those that don't.
The companies that thrived in the cloud smart era weren't the ones that moved the fastest. They were the ones that moved with purpose. They understood that the value of cloud wasn't in migration volume; it was in aligning technology decisions with business outcomes.
The same will be true for AI. The winners won't be the companies that deployed the most models or spent the most on GPU infrastructure. They'll be the companies that deployed AI where it actually moved the needle and had the discipline to say "not yet" everywhere else.
At Arctiq, we've helped organizations navigate these transitions before. We were in the room when cloud first strategies needed to become cloud smart. We helped enterprises right size their hybrid environments, modernize their infrastructure, and build operational models that actually scaled.
Now we're doing the same with AI. Our approach is grounded in the same principles that guided our cloud work: start with business outcomes, assess readiness across data and infrastructure, build with operational discipline, and secure everything end-to-end. Whether that means helping you modernize the platform that AI will run on, integrating AI driven observability into your operations, or ensuring your security posture keeps pace with new attack surfaces… we meet you where you are.
We’re not here to sell you on AI hype. We’re here to help you be AI smart. If you’re ready to take a more deliberate approach to AI, contact us to start the conversation.