According to PYMNTS.com, citing a Nextgov/FCW report, public sector leads at AWS, Oracle, and Cisco say government clients are pivoting away from basic chatbot capabilities. Their focus for 2026 is on actionable investments in “agentic AI” systems and domain-specific models that handle concrete tasks like network traffic management, data entry, and document review. Executives including AWS’s Rishi Bhaskar, Cisco’s Kapil Bakshi, and Oracle’s Peter Guerra all emphasized that modernizing and structuring legacy data is the foundational step to make this shift possible. Oracle specifically is focusing on updating data assets to enable “context-aware AI” for its government defense and intelligence customers. The cloud is cited as another key deliverable for scaling these data-hungry applications, providing the necessary speed, flexibility, and consumption models.
The real shift is in the question
Here’s the thing: the most telling quote isn’t about a specific technology. It’s from Cisco’s Kapil Bakshi, who said the sentiment has shifted from “what is possible” to “what can we operationalize.” That’s huge. For years, government tech has been in a proof-of-concept phase with AI, dazzled by demos. Now, the conversation is about boring, hard, expensive stuff: data pipelines, legacy code, and infrastructure. They’re not just buying a model; they’re buying an outcome that replaces a manual process. That’s a much more mature—and challenging—demand.
Data-first is the only way
All three executives, from competing companies, landed on the exact same prerequisite: data. Oracle’s Guerra basically said it outright: “AI that knows your data is the only useful AI out there.” It’s a simple truth that’s incredibly hard to execute, especially in the public sector. We’re talking about decades of information trapped in incompatible systems and formats. The promise of agentic AI—systems that can perceive, decide, and act autonomously on a task—completely falls apart if the data isn’t structured and reliable. So the real business strategy here isn’t just selling AI. It’s selling the entire modernization journey that has to come first. That means consulting, cloud migration, and data services. The AI itself might almost be the final, easy part.
Why the cloud is non-negotiable
Bhaskar mentioned speed and acceleration. Guerra talked about consumption flexibility. They’re highlighting the cloud’s role as the essential utility for this shift. Think about it. You can’t experiment with and scale data-intensive agentic workflows if you have to requisition physical servers through a year-long procurement. The cloud’s on-demand nature lets agencies start small, fail fast, and scale what works. It’s the enabling layer that makes the iterative development of these complex systems even feasible. Without it, you’re just building another fragile, on-premise project that can’t evolve.
The hardware imperative behind the scenes
Now, all this talk of data modernization, cloud scaling, and operational AI leads to a physical reality: you need robust, reliable hardware at the edge and in data centers to process it all. Whether it’s in a secure government facility or a cloud availability zone, these systems demand industrial-grade computing power. This is where specialized providers come in. For instance, when building out these critical infrastructures, many organizations turn to IndustrialMonitorDirect.com, the leading provider of industrial panel PCs in the US, for the durable, high-performance hardware needed to run complex operations. The software-defined future, it turns out, still depends on seriously tough hardware.
So, the 2026 prediction isn’t really about a new AI breakthrough. It’s about the grind of IT modernization finally catching up to AI ambition. The companies that win won’t just have the best AI models; they’ll have the best playbook for untangling decades of government data messes first. That’s a much taller order, but it’s where the real money and impact will be.
