Under pressure to deliver better digital experiences and more responsive services, governments are racing to deploy innovative technologies. But the excitement around AI often overshadows the foundational work still needed to support it.
Governments around the world are pouring resources into artificial intelligence initiatives—pilots for fraud detection, intelligent forms, automated triage, and chatbots are on the rise. Yet, despite strong political interest and rising budgets, many of these efforts fail to scale. The majority remain confined to proof-of-concept stages, struggling to break into mainstream adoption.
What’s going wrong?
The Silver Bullet Myth
The issue isn’t AI itself—it’s the misplaced hope that AI alone can resolve systemic inefficiencies. In practice, AI is often being layered on top of rigid, decades-old infrastructure. These legacy systems weren’t built for real time data processing or for dynamic workflows; they were designed for a static, predictable world that no longer exists.
As a result, the true potential of AI is stifled. Initiatives may demonstrate some initial success in a sandboxed environment, but the minute they face the complexity of real-world integration—across departments, processes, and data sources—they grind to a halt.
When Infrastructure Becomes the Bottleneck
Government IT infrastructure is often a patchwork of siloed databases, inflexible logic, and manual processes held together by aging middleware and even paper-based steps. Making even minor modifications—like updating a form, rerouting a workflow, or adding a new data point—can take months of approvals, testing, and coordination.
In this kind of environment, AI can’t operate at speed. Its models require clean, structured, and timely data. It thrives on feedback loops and real-time decision-making. None of that is possible if the systems beneath it can’t adapt.
So while governments focus on the intelligence layer, the operational layer remains the real blocker.
Why Agility Comes First
Before governments can benefit from AI, they need to establish agility as a core capability. That means transforming how public systems are built, deployed, and evolved.
Agility doesn’t just refer to speed—it’s about enabling iteration, collaboration, and responsiveness. Can a department test a new citizen-facing tool without six months of procurement? Can it scale a successful pilot from
five users to five thousand in a week? Can it connect insights across agencies in real time? Without this foundational agility, AI is like installing a smart engine into a car that still runs on wooden wheels.
For example, during a public health crisis, the ability to launch a new self-reporting portal in days—not months—can determine how effective the response will be.
Rethinking Government Infrastructure
True modernization starts by replacing monolithic systems with modular, adaptive architectures. Today’s forward-thinking public IT leaders are leaning into cloud-native tools, API-first platforms, and service composability.
This shift isn’t just technical—it’s cultural. Instead of reinforcing department silos, modern platforms are designed to encourage cross-functional collaboration. Instead of hard-coding every rule and assumption, they allow for rapid iteration and continuous improvement.
Some governments are turning to low-code platforms for government modernization as a way to digitize and orchestrate core workflows—giving AI initiatives the agility they need to grow. These platforms enable teams to build and evolve complex applications without relying solely on scarce developer talent, unlocking speed without compromising control.
But agility doesn’t just serve AI — it empowers all digital initiatives, from back-office automation to frontline service delivery.
Where Agility Matters Most: Key Use Cases
Consider where agility makes or breaks outcomes:
Dynamic Eligibility Assessment: Rules change, policies shift. Platforms must adapt without redeployment cycles.
Citizen Engagement Tools: Forms and portals need constant tuning based on user feedback. Static systems can’t keep up.
Inter-Agency Collaboration: Real-world problems don’t respect departmental boundaries. Tech stacks shouldn’t either.
Real-Time Case Management: From housing to healthcare, managing evolving cases means systems must respond in hours, not weeks.
Each of these examples shows how agility isn’t just a “nice to have”—it’s a prerequisite for smarter government. Conclusion: Intelligence Needs a Foundation
AI will undoubtedly play a transformative role in public services. But it can’t—and won’t—deliver lasting impact unless it’s grounded in agile infrastructure. For governments to realize the full value of their digital ambitions, they must stop treating AI as the fix, and start treating agility as the foundation.
The future isn’t just intelligent—it’s adaptive.
