By Lee McClendon, Chief Digital and Technology Officer, Tricentis
AI is transforming how software is developed, tested, and released – yet many teams are working to turn promise into measurable outcomes. Across the software development lifecycle (SDLC), AI introduces powerful capabilities. From accelerating coding and generating software quality checks and record keeping, generative AI tools are helping software development teams under immense pressure to deliver faster without compromising quality.
However, our research shows that while the vast majority (90 percent) of today’s CIOs and CTOs trust AI to make critical software release decisions, two-thirds believe it will be three years before AI meaningfully impacts business performance and costs.
The challenges facing today’s software development teams are no longer about technical readiness, but rather strategic integration of AI into current SDLCs. True ROI emerges when AI becomes woven into delivery processes as part of intelligent automation frameworks. These are structured systems that integrate AI with automation to make processes adaptive and measurable against both software speed and quality goals. For software leaders to fully realize AI’s potential, they must move beyond pilots and finally position AI as an essential driver of consistent, trusted, and high-performing software delivery at scale.
AI Aligned with Delivery Priorities
AI adoption is here to stay with nearly all (99.6 percent) organizations already using some form of AI in software testing, and 96 percent planning to increase their use in the future. Amidst this general adoption, the most successful AI initiatives focus on accelerating release cycles while ensuring quality – not just automating for automation’s sake. In software development and quality engineering, AI drives results when applied to activities like test case generation and maintenance, documentation automation, and developer onboarding.

When integrated into continuous testing and release cycles, AI reduces manual work, improves consistency, and empowers development and quality assurance teams to shift their focus to solving complex challenges and advancing product innovation. This shift turns AI from a helpful tool into a strategic asset.
Confidence and Oversight Unlock AI’s Full Potential
As AI-generated outputs increasingly influence release decisions, having confidence in their accuracy and reliability is essential. While confidence in AI is rising, with almost 90 percent of organizations claiming they can effectively measure GenAI ROI, success will ultimately depend on oversight and validation.
What does this look like in practice? Organizations must put safeguards in place, such as human-in-the-loop reviews, explainability and documentation standards, integration into CI/CD pipelines and continuous AI literacy development.
The most significant ROI emerges when speed and quality go hand in hand. Forward-thinking teams embed AI not only in coding and release stages, but also in testing, validation, and defect prevention – achieving higher consistency and long-term resilience.
Our research underscores this balance. Software developers and technology leaders expect AI to play a major role in streamlining quality assurance processes, with more than 70 percent believing AI will help improve defect leakage, test coverage, and maintainability. As a result, teams that align AI with both speed and quality can expect to see higher customer satisfaction and stronger confidence in their release processes.
Organizational Readiness Shapes AI’s Impact at Scale
Technology alone does not unlock ROI. Achieving repeatable success requires operational discipline and cultural alignment. We’re seeing more organizations establish clear policies when it comes to using specific AI tools, building AI fluency across engineering and QA teams, and implementing cross-functional feedback loops to refine how AI supports delivery. Our research reflects this reality: two-thirds of all organizations expect to suffer an outage or major disruption in the next year. Knowing that AI ROI could take several years to fully materialize, this timeline emphasizes the importance of aligning people, processes, and priorities to not just maximize returns, but positively impact the business’s SDLC.
AI ROI Is within Reach – and Accelerating
AI is no longer experimental. For many teams, intelligent automation has already improved efficiency, speed, and decision-making. The difference between isolated success and enterprise-wide impact lies in execution. Software development teams that thoughtfully integrate AI into continuous testing and quality assurance workflows, align its use to measurable outcomes, and foster confidence through clear oversight are already unlocking meaningful ROI. Those who treat AI as a peripheral tool or focus only on speed risk missing its broader potential.
For technology leaders, the mandate is clear: embed AI as a trusted force across software delivery, balancing rapid releases with rigorous quality to drive sustainable business impact. The organizations that achieve this equilibrium will shape the future of software innovation.
Lee McClendon is Chief Digital and Technology Officer at AI testing platform company Tricentis.




