AI is no longer just a buzzword in software delivery, it’s the driving force of change across many organisations around the world; its influence growing across development, testing, and release processes. As teams adapt to new pressures, GenAI tools have begun to transform traditional workflows, enabling faster delivery. Yet, for those on the frontline, the real test is converting AI’s dazzling potential into measurable, everyday performance gains and ensuring quality. Our recent research reveals a striking gap between current expectations and achieved results: digital leaders trust AI with increasingly high-stakes software decisions, but more than two-thirds believe that big, bottom-line wins will take years to materialise.
So how do we close this gap and ensure AI delivers real, sustainable value not in years, but now?
Moving beyond technical readiness
Let’s be honest: the technology isn’t lacking. The real challenge for software teams is embedding AI into the heart of today’s software development lifecycle (SDLC) in a way that’s both strategic and practical. True ROI emerges only when AI isn’t just an add-on, but integrated into structured automation frameworks and operation systems, enabling us to deliver better software, faster, and with greater reliability. That means moving beyond isolated pilots and proofs of concept to position AI as a foundational driver of consistent, high-performance software delivery so that it becomes a cornerstone of delivery culture.
Aligning AI with delivery priorities
If you work in software delivery today, chances are you’re already using AI for testing or automation in some capacity. AI adoption in software testing is now nearly universal, with the vast majority of organisations telling us that they already use it and plan to increase future investments. What sets the most effective teams apart, though, is their refusal to automate for automation’s sake. The real value comes from deploying AI where it eliminates the pain points: tedious test case generation, endless documentation, and onboarding headaches, freeing up people to focus on creative problem-solving and innovation.
When AI becomes operational across your entire software pipeline, not just a clever bolt-on, it unlocks consistency and agility you can depend on, release after release. Transforming from a helpful tool to a strategic enterprise asset, it accelerates release cycles whilst maintaining uncompromised quality.
Confidence and oversight are essential
As we rely more on AI to make tough release calls, confidence becomes crucial. Our research shows that nearly 90% of organisations claim they can effectively measure ROI from GenAI, but longer-term success will depend on the quality of oversight. Human-in-the-loop reviews, clarity and transparency in documentation, integrating AI processes into CI/CD pipelines, and ongoing development of AI literacy within teams will all be essential ensuring AI outputs are actionable and accountable.
With clear safeguards and standards, organisations can move beyond experimentation, scaling AI responsibly and maximising its positive effects across the business. Well-governed, transparent processes underpin confidence and allow AI’s role to expand beyond early innovation and experimentation to everyday value.
Balancing speed and quality
Speed is often the headline benefit of AI in software delivery, but leaders who lean too hard on rapid releases at the expense of quality do so at their own risk (and that of their organisations). True ROI is realised when rapid development goes hand in hand with resilient quality. The most forward-thinking teams infuse AI throughout the coding, testing, and defect prevention stages, achieving both agility and long-term robustness. According to our survey of software delivery practitioners, over 70% believe AI will advance efforts to improve defect leakage, test coverage, and maintainability – all critical factors for reducing risk, accelerating releases, and making quality scalable.
Teams that successfully leverage AI to support both delivery velocity and quality standards see higher customer satisfaction and improved confidence in their release processes. By shifting the emphasis from repetitive, manual work to strategic engineering and innovation, AI delivers value with every release.
Preparing organisations for AI at scale
Let’s not fool ourselves: tools are only half the battle. Achieving repeatable, reliable ROI demands operational rigour and cultural alignment. That means clear policies for AI use, ongoing training to build expertise within engineering and QA teams, and honest, collaborative feedback loops to keep improving AI’s contribution to delivery workflows.
With two-thirds of organisations anticipating software outages or major disruptions in the year ahead according to our research, building a culture that’s adaptable to change is more urgent than ever. AI success is ultimately a people story: it’s about the teams who can iterate fast, learn from setbacks, and keep quality at the centre. Recognising that AI’s returns may take several years to mature, it’s crucial to align people, processes, and priorities not just for short-term gain but for lasting, positive impact on the software development lifecycle.
Accelerating towards meaningful AI ROI
The experimental days of AI are over in software delivery. Intelligent automation is already giving teams more time, sharper insights, and better release decisions. But moving from sporadic wins to organisation-wide impact takes discipline. Teams that embed AI into continuous testing and quality assurance, align efforts to real business metrics, and keep governance front and centre are unlocking ROI that actually moves the dial. If leaders treat AI as a trusted part of their overall strategy, not just a tool for quicker releases, they will start to unlock its true potential.
