Modernizing at the speed of AI: How state and local governments can keep quality in check

Key takeaways:

• AI is now the top priority for state CIOs, and agencies must modernize quickly without sacrificing quality, compliance, or public trust.
• Embedding agentic quality engineering practices throughout the development lifecycle lets agencies move fast while staying reliable and accountable.

For the first time ever, artificial intelligence has topped NASCIO’s annual ranking of state CIO priorities, ending cybersecurity’s 12-year reign at number one. It’s easy to see why: public agencies – like their private sector counterparts – see real potential in AI to modernize operations, accelerate service delivery, and help lean teams do more with fewer resources. But the government faces a challenge the private sector often doesn’t: it has to innovate while maintaining a high degree of public trust and compliance.

That’s the tension at the heart of today’s AI landscape. Innovation is accelerating faster than governance, testing, and compliance frameworks can adapt. As AI-assisted development speeds up how software is built and updated, traditional QA can’t keep pace. When validation becomes the bottleneck, teams may rush or skip it to meet deadlines. In systems that deliver benefits, process payments, and serve constituents, this poses enormous risk – even small defects can cause agency-wide failures.

Watch now: How to embed quality across the agentic SDLC

How agencies can reshape QA without adding risk

The answer for QA teams isn’t to slow down, but to embed quality engineering across the entire software development lifecycle. Here are four steps state and local agencies are taking to modernize at speed without losing control.

1. Shift left. Move quality engineering and validation earlier in development. Catching defects, integration issues, and policy gaps before production keeps problems from becoming costly and disruptive downstream — and lets agencies adopt AI while reducing operational risk.
2. Validate continuously. Agencies need to evaluate changes without slowing modernization. Intelligent, continuous regression testing helps validate updates, integrations, and infrastructure changes as they happen. This keeps teams confident in releases, even as systems grow more interconnected and AI-assisted.
3. Standardize testing. Reusable, model-based testing assets can improve efficiency and consistency. Standardizing validation across agencies, systems, and workflows reduces duplicated work. It also simplifies maintenance and creates practices that can scale over time.
4. Integrate validation and DevSecOps. Embedding validation into DevSecOps and CI/CD pipelines makes quality part of delivery instead of a final check. It also improves traceability, governance, and real-time visibility into operational risk throughout development and deployment.

Agentic AI will help public sector QA teams keep pace

As agencies embed quality throughout the lifecycle, many are turning to agentic AI to coordinate it all. Unlike generic, generative AI tools that can only assist with one-off tasks like code or script generation, agentic AI acts as a digital coworker that can analyze application changes, assess risk, orchestrate testing, and flag downstream impacts across interconnected systems, all under human oversight. Generic AI helps automate tasks; agentic AI helps agencies manage complexity. Importantly, agencies must establish the appropriate human oversight and governance for how these agents operate. Agentic AI doesn’t remove human accountability — agencies remain responsible for the systems they deploy, and AI agents simply extend limited workforce capacity so teams can focus on governance and judgment.

Read now: The agentic quality engineering maturity model

The payoff can be significant. Agencies can improve efficiency, prioritize testing based on risk, scale across different systems, and validate changes continuously. In short, they can modernize faster while maintaining reliability, governance, and operational control.

Speed alone isn’t enough in government. Every release, workflow, and AI-enabled capability still has to be secure, accessible, compliant, auditable, and reliable. In modern government, reliability is infrastructure.

Tricentis meets that challenge with an Agentic Quality Engineering Platform that unifies agentic test creation, test automation, quality intelligence, and performance testing under a single control plane, AI Workspace, supporting more than 190 technologies across SaaS, legacy, API, cloud, and citizen-facing systems. Recognized as a leader by Gartner and Forrester, Tricentis helps public sector organizations modernize with confidence.

Want to learn more? Read the white paper: A guide to AI-powered testing for state and local government. Contact our public sector experts at publicsector@tricentis.com or explore what’s possible.