As agencies race to adopt artificial intelligence, they face a growing challenge: Moving at AI speed without sacrificing the quality, security and compliance their missions demand from critical systems. AI-assisted development can help teams generate and release code faster than ever, but that acceleration also raises the stakes. Without robust testing, governance and oversight, software defects and vulnerabilities are more likely to reach the systems citizens, public servants and mission teams rely on.
That risk is already becoming apparent. Tricentis research found that 60% of global organizations are deploying untested code as AI accelerates software delivery. This underscores the critical challenge for public sector technology leaders to capture the speed and efficiency of AI without compromising the integrity of the software that powers government missions.
AI success depends on software quality, which in today’s fast-paced world depends on automated testing. As AI reshapes modern software development lifecycles, agencies need to modernize their testing practices alongside it. Automated and continuous testing provides the foundation agencies need to accelerate innovation while maintaining the reliability, security and compliance essential to government systems.
Agencies need a scalable foundation for AI
Many government organizations recognize the need to adopt AI but still struggle with how to do so safely and at scale. Staffing shortages remain one of the biggest obstacles. A recent Government Accountability Office report identified skills gaps as an obstacle to federal AI adoption, finding that staffing reductions left the IRS without enough skilled employees to support or develop new AI tools.
Those challenges extend across the broader software development landscape. In fact, Tricentis’ Quality Transformation report cited above also found that 33% of organizations cited skills gaps as a barrier to achieving continuous software quality at scale. At the same time, many organizations still rely on manual testing or a patchwork of disconnected testing tools, putting additional pressure on already constrained teams as AI accelerates software development and release cycles.
As release cycles accelerate, testing backlogs inevitably grow. Critical validation steps may be delayed or skipped, increasing the likelihood that software reaches production before agencies fully have confidence in its quality, security or performance. Insufficient testing slows modernization by forcing agencies to spend more time fixing issues after deployment, hindering innovation. For most agencies, the speed of innovation requires new testing strategies.
This is why automation has become essential. Automation enables existing teams to keep pace with the speed of AI-driven software development while reducing operational risk and allows skilled personnel the time needed to ensure efficacy, security and mission-readiness.
Software quality is the foundation of trustworthy AI
One of the biggest misconceptions surrounding AI adoption is that agencies should begin with the AI tools themselves. In reality, successful AI initiatives begin, first and foremost, with software quality and reliable data.
Poor-quality data inevitably produces poor AI outcomes. For agencies operating in highly regulated or rapidly changing environments, inaccurate AI outputs can have significant consequences for mission execution, security, safety and public trust.
Automated testing provides the stable foundation agencies need before scaling AI initiatives. By validating applications, ensuring clean data and continuously testing software changes throughout the lifecycle and integrated environments, agencies can release software with greater confidence while reducing risk and friction as new AI capabilities are introduced.
Many organizations are also burdened by fragmented testing environments. Some agencies rely on multiple testing tools, each responsible for a different task. If one tool fails, the entire testing pipeline can stall, preventing organizations from moving at the speed that AI now demands. Consolidated, automated testing environments help eliminate these bottlenecks while improving reliability across the software development lifecycle.
Continuous testing enables speed, security and governance
As AI capabilities become standard across enterprise software, agencies need stronger governance, security and compliance controls to ensure AI is implemented consistently across the organization. This is especially important as just 23% of quality assurance (QA) and development operations (DevOps) professionals find their organization is “very prepared” to operationalize, govern and scale AI agents across the software development lifecycle (SDLC).
Without common standards, individual teams may adopt AI independently, creating fragmented environments with inconsistent security and quality practices and limited organizational visibility. That fragmentation introduces unnecessary operational, cybersecurity and mission risks.
Continuous testing plays an important role in addressing these challenges. By continuously validating applications and updates before and after deployment, agencies can move faster while maintaining compliance with evolving security and functional requirements. Automated testing also provides ongoing feedback about how systems are performing, giving organizations greater visibility into AI-enabled environments and helping leadership maintain governance as adoption expands.
In other words, continuous testing enables agencies to achieve what often feels like competing priorities: accelerating software delivery while preserving security, reliability and compliance.
In the age of agentic AI, automated testing becomes even more crucial
AI is transforming how software testing is done across sectors. Recent advances allow users to generate test scripts using simple natural language prompts, making sophisticated testing capabilities accessible to business users as well as technical teams. This democratization is particularly valuable for agencies operating with lean staffs, enabling more employees to participate in quality assurance without requiring deep technical expertise.
Looking ahead, AI will become increasingly more autonomous. Rather than simply responding to prompts, AI agents will proactively identify application performance issues, detect vulnerabilities and even suggest or generate code improvements. As AI takes on a greater role in software development, automated testing and the role of the skilled professionals that oversee it need to evolve alongside it.
That future will require AI systems specifically designed to validate AI-generated code, creating an automated feedback loop in which software is tested almost immediately after it is produced. Organizations that invest in automated testing today will be better positioned to safely adopt increasingly autonomous AI capabilities tomorrow.
Ultimately, the future of government software development will depend on more than AI itself. It will depend on the ability to verify that AI-generated applications are secure, reliable and ready for production. As AI becomes embedded throughout the SDLC, automated testing will become not simply a best practice, but an essential safeguard for delivering trusted digital services.
Ben Baldi is senior vice president of global public sector at Tricentis.
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