AI Success Depends on Software Quality, Tricentis Executive Says

AI success depends on software quality, Ben Baldi argues, as federal agencies adopt tools that can generate code faster. The Tricentis executive says automated testing can help agencies keep that pace while checking software for reliability, security and compliance.

Baldi, Tricentis’ senior vice president of global public sector, makes the case in commentary published by Federal News Network. He argues that agencies should strengthen testing practices alongside AI adoption, rather than treating AI tools as the starting point.

Why AI Success Depends on Reliable Software

The commentary cites Tricentis research finding that 60% of global organizations are deploying untested code as AI accelerates software delivery. That figure comes from the company’s research, rather than a government-wide measurement. In Baldi’s view, AI success depends on agencies checking code as delivery speeds up.

Baldi also points to a Government Accountability Office report that identified skills gaps as an obstacle to federal AI adoption. He says the report found staffing reductions left the IRS without enough skilled employees to support or develop new AI tools.

Tricentis’ Quality Transformation report found that 33% of organizations cited skills gaps as a barrier to achieving continuous software quality at scale. Baldi says manual testing and disconnected tools can add pressure for teams facing faster development and release cycles.

Automated Testing Can Support Agency Teams

Baldi argues that growing testing backlogs can delay validation or lead teams to skip checks before software reaches production. In his view, fixing issues after deployment can consume time that agencies might otherwise spend on modernization.

He presents automation as a way for existing teams to keep pace with AI-assisted development while leaving skilled staff time to assess security and mission readiness. His argument connects software quality to the wider changes in software development work, where employers are adjusting to AI demand.

The commentary also warns that fragmented testing environments can create bottlenecks. If one tool fails in a pipeline built from multiple products, Baldi says, the organization may struggle to move software through testing.

Governance Must Keep Pace With AI

Baldi cites a survey finding that 23% of quality assurance and development operations professionals consider their organizations “very prepared” to operationalize, govern and scale AI agents across the software development lifecycle. He argues that shared standards and continuous validation can help leaders maintain visibility as teams adopt AI.

The commentary’s recommendation is to validate applications and updates before and after deployment. Baldi’s case is that AI success depends on continuous checks as systems change. Baldi says that practice can help agencies address security and functional requirements as they change, while giving leaders feedback about AI-enabled systems.

That approach is related to other coverage of continuous improvement in industrial software and the rollout of hospital security technology, where software and technology changes also require attention to implementation.

AI Agents Raise Testing Demands

Baldi says newer tools can generate test scripts from natural-language prompts, potentially making testing more accessible to business users as well as technical teams. He describes future AI agents as systems that may identify application performance issues, detect vulnerabilities and suggest or generate code improvements.

Those capabilities are a forecast in the commentary, not a report that agencies have already adopted autonomous testing agents. Baldi argues that testing systems and skilled professionals will need to evolve as AI takes on more development work.

He anticipates a feedback loop in which AI-generated code is tested soon after it is produced. For agencies, the immediate point of his argument is that AI success depends on verification: software needs to be checked for security and reliability before it is ready for production.

Read Baldi’s commentary on agency AI.