2026 Prediction #1: Enterprises Will Shift from AI-First to AI-Smart

If 2025 was the year AI got real, 2026 will be the year enterprises get disciplined and scale AI through business-critical use cases that drive measurable outcomes. Over the past twelve months, I’ve seen many organizations adopt an “AI-first” mindset, launching experiments, spinning up pilots, and rushing to embed models anywhere that seemed promising. That enthusiasm isn’t going away, nor should it. But it does need to mature in order to translate AI adoption into productivity gains, faster decision-making, and  revenue growth. 

In the coming years, enterprises will shift from AI-first to what I call AI-smart.

AI-smart means treating AI not as a side project or an innovation experiment, but as a core, mission-critical capability that must be engineered, protected, and operated with the same rigor as every other enterprise-grade system. This shift isn’t theoretical, it’s a necessity. While developers tend to focus on how quickly they can deploy something new, enterprise IT is responsible for how reliably AI runs on day 200, not just day one.

Becoming AI-smart requires getting three things right: resiliency, day-two operations, and integrated security. These aren’t new ideas, but they take on new meaning and importance in the context of AI workloads.

Resiliency: AI Must Be Able to Withstand Failure Like Any Business-Critical System

In the early days of enterprise databases and transactional systems, resiliency wasn’t optional, it was the definition of what made a workload “enterprise-ready.” Redundancy, failover, backup, consistency, and state protection were all essential.

The same must now be true for AI.

When I speak with IT leaders, the first thing I emphasize is that AI applications will become business-critical faster than any other application class we’ve seen. More people will use AI every day and quickly get accustomed to its responsiveness. Any slowdown, outage, or unpredictable behavior will be unacceptable. That means the platform running AI has to be able to tolerate failures, absorb changes, and keep applications online across distributed environments.

This is particularly challenging because the pace of change underneath AI stacks is accelerating. New GPUs arrive constantly. CPUs now have built-in accelerators. ARM architectures are moving into the mainstream. DPUs are reshaping how data flows. Meanwhile, AI frameworks and model architectures are evolving just as quickly. A resilient platform must abstract this churn and make the underlying complexity invisible to the teams operating it.

In short, AI-smart starts with an enterprise-grade definition of resiliency.

Day-Two Operations: AI Has to Run Reliably Long After the First Deployment

Deploying an AI workload once is relatively easy. Keeping it running, patched, upgraded, monitored, and consistent as models evolve? That’s the hard part, and it’s exactly what separates innovation from operational excellence.

Enterprise IT teams have spent years mastering day-two discipline for traditional workloads. They know how to maintain uptime across patches. They know how to handle upgrades without service interruption. They know how to manage drift across distributed environments. They know compliance and governance are never static.

AI adds another layer of complexity to this operational reality. Now there are model versions to manage, vector databases to maintain, GPU scheduling challenges to handle, and rapidly changing container frameworks to support.

Developers often think in terms of building something new quickly. IT thinks in terms of sustaining something reliably over time. 

AI-smart means unifying those perspectives and creating tight coordination between IT and developers. It means adopting platforms and operating models that make day-two operations not just possible, but predictable across cloud, datacenter, and edge environments.

Integrated Security: AI Needs Consistent Protection Across Every Environment

AI introduces new surfaces that traditional security frameworks weren’t designed for, such as large, sensitive datasets used for training and fine-tuning; model weights and inference endpoints; vector databases and retrieval pipelines; and distributed deployments that span cloud, data centers, and sovereign edge sites. At the same time, regulatory pressures around data sovereignty and privacy are steadily increasing.

Given all these new surfaces,  an outdated, fragmented, and patchwork approach to AI security won’t work.

AI-smart requires integrated security that is built into the platform, consistently enforced, and adaptable to where AI runs. Whether a model is deployed in the public cloud, on-prem, at a sovereign edge site, or across all three, the security policies, access controls, and observability must all be uniform. Compliance boundaries must be respected everywhere.

This is one of the reasons I believe AI will actually accelerate the enterprise push toward unified platforms. You simply can’t secure AI effectively if each environment requires its own IT team and skill sets.

Why This Shift Matters

The bottom line is simple. AI can only deliver long-term value if it’s deployed with enterprise-grade discipline.

AI applications will become business-critical more quickly than any applications we’ve seen before. Users want those applications to simply work the way they should, while developers want to move fast (and often do). The moment AI becomes a core part of the business, however, all of those expectations around uptime, reliability, security, and governance change dramatically.

That’s why being AI-smart, not just AI-first, will define the leaders of the next decade.

Enterprises that master resiliency, day-two operations, and integrated security will be positioned to scale AI with confidence. Those that don’t will find themselves constantly reacting to outages, gaps, security risks, and operational bottlenecks.

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