GenAI enhances software development productivity
The impact of generative AI on software development is immediate, significant, and measurable. We’re seeing real-world gains in speed, consistency, and developer capacity thanks to tools that automate repetitive parts of the job. Developers using genAI aren’t just improving their own output, they’re fundamentally changing the arc of software delivery.
Most developers today already use genAI to some degree. According to BairesDev, 72% are actively using genAI tools, and nearly half, 48%—are using them daily. These tools help write code, flag potential bugs, and optimize existing functions. They also reduce the time spent on code reviews and testing. What this means for your business is clearer: less time in development, faster time to market, and more time building value.
This isn’t speculation, by the way. Field experiments conducted by Microsoft and Accenture show that developers using coding assistants completed 26% more tasks each week. Code commits increased by 13%, and successful code compilations rose by 38%. That’s not incremental improvement, that’s exponential acceleration.
It’s also not just about speed. According to the 2024 State of DevOps Report by DORA, a significant portion of developers report productivity increases, with 25% claiming “moderate” improvements and 10% calling the gains “extreme.” These aren’t just statistics, they’re clear signals that generative AI is solving real problems.
For C-suite leaders, the takeaway is direct: genAI boosts output without inflating headcount. It drives efficiency where it matters and frees up highly skilled talent to focus on strategic challenges instead of low-leverage tasks. The momentum is clear, and it’s not slowing down.
Requirements gathering becomes a new bottleneck in Agile development
There’s a shift happening in agile workflows. While product teams are moving faster than ever thanks to genAI tools, the process of collecting and defining requirements hasn’t kept pace. Requirements are now the hold-up, the slowest part of the cycle.
As coding becomes faster and more automated, the need for precise, well-structured requirements becomes more critical. Developers today rely on detailed user stories and defined acceptance criteria not just to build code, but to instruct AI tools to do it effectively. If your specifications are weak, your outcomes suffer, no matter how advanced the tools are.
David Brooks, SVP of Evangelism at Copado, put it clearly: “In a world where copilots are writing code, planning will take on a much more important role.” He’s right. You can no longer afford loosely defined requirements or vague documentation. The bar is higher now. AI agents need clarity to be effective, and your team needs strong direction to move fast without breaking things.
Business analysts and product owners are stepping into new territory. Their role is no longer limited to managing backlogs or marking down meeting minutes. They’re now creating the foundational data for AI-driven development. GenAI doesn’t replace their work, it amplifies it. When used right, genAI helps analyze meeting transcripts, prioritize features, and draft detailed, AI-ready documentation.
For executives, the message is straightforward: if your teams are investing in productivity tools, you must also invest in the systems that feed them. That means improving how you gather, prioritize, and structure requirements. Planning and documentation can’t be afterthoughts anymore. They are now mission-critical components of agile velocity.
Transformation of requirements gathering into a collaborative process
Generative AI isn’t just a tool for writing code, it’s an engine for collaboration. Organizations that understand this are already seeing another advantage: better communication between business and technical stakeholders. This is especially evident in how requirements are gathered and refined.
Traditionally, requirements gathering has looked like an isolated documentation phase. But now, with genAI, product owners and analysts can pull data directly from conversations, interview transcripts, and meeting recordings, and use it to create early drafts of requirements. This process shifts the focus away from manual note-taking and toward iterative refinement. Teams can validate inputs faster, adapt specs in real-time, and involve more stakeholders without slowing down.
Chris Mahl, CEO of Pryon, explains this shift clearly. He says, “Product owners now use AI to generate initial requirement drafts from stakeholder interviews, then refine them through feedback cycles.” He adds that business analysts are evolving into “AI orchestrators”, a new role that combines prompt engineering, strategic framing of problems, and validation of AI-generated results. It’s not about excess automation. It’s about applying AI where it works and ensuring humans stay accountable for accuracy and oversight.
This new approach is especially valuable for complex architectures like microservices, integrations, and data pipelines. These systems often require non-functional specifications and synthetic testing data that standard project documentation overlooks. With genAI assisting, analysts can surface edge cases, test assumptions, and verify solution alignment earlier in the process.
For C-suite leaders, the strategic insight is clear. You’re no longer managing just people, you’re managing the way your people interact with intelligent systems. Making sure your teams are equipped to work this way, where AI supports collaboration, not just efficiency, is key to both short- and long-term competitiveness.
Accelerating prototyping and delivery cycles with GenAI
One of the high-leverage areas for AI in software development is reducing time to prototype and ship functional solutions. AI isn’t just accelerating development, it’s helping product teams close the gap between stakeholder input and working software.
With genAI, product managers and analysts can now edit and iterate on user stories directly within integrated development environments (IDEs). And it’s not just about speed. The interaction between teams and AI extends across design, development, and testing, enabling live refinement of requirements based on continuous feedback.
Simon Margolis, Associate CTO at SADA, explains how this is changing roles across agile teams. In his words, genAI is “enabling [product owners and analysts] to prototype and iterate on requirements directly within their IDEs.” This improves cross-functional communication, reduces reliance on excessive documentation, and keeps the focus on real-time collaboration with end-users and stakeholders. The outcome? Shorter delivery cycles, faster experiments, and tighter alignment with customer needs.
Adding to this momentum are low-code platforms that are becoming more powerful thanks to genAI. Tools from Adobe, Appian, Pega, Quickbase, and SAP now integrate AI capabilities that translate prompts into functional micro-apps and processes. These platforms drastically lower the barrier to working prototypes, giving your teams more time to evaluate, refine, and scale the solutions that work.
The signal for executives is simple: time-to-validation is collapsing. Teams no longer need to wait for full development cycles before gathering feedback. Instead of thinking in terms of phases, your product and engineering units can perform validation more often and earlier, boosting delivery confidence without losing speed.
Shifting focus from routine tasks to strategic innovation
Generative AI is changing what it means to contribute meaningfully to product development. Tasks that once consumed hours, aligning user stories with templates, formatting documentation, or converting discussions into feature lists, can now be done in minutes. This shift opens up space for more critical thinking and innovation among product owners, analysts, and engineering leads.
AI tools handle formatting, structuring, and consistency checks with near-perfect accuracy. But they can’t replace the human ability to tune into customer sentiment, challenge assumptions, or make strategic bets on what to build next. The real value comes when teams use AI not to take over, but to amplify their creative and analytical strengths.
Ramprakash Ramamoorthy, Director of AI Research at ManageEngine, puts it well: “GenAI excels at aligning user stories and acceptance criteria with predefined specs and design guidelines, but the original spark of creativity still comes from humans.” His point is critical. You don’t delegate ownership of product direction to machines, you use machines to clear space for better decision-making.
This means product owners and business analysts must elevate their roles. Their time is better spent shaping product vision, identifying opportunities, and driving alignment between customer needs and technical execution. GenAI reduces the friction in day-to-day work, but strategy, creativity, and innovation remain human responsibilities.
For executives, this isn’t just about productivity gains, it’s about redefining team capabilities. Investments in AI tools are most valuable when paired with the right culture: one that values experimentation, empowers people to think beyond processes, and uses AI as infrastructure, not as a crutch. If your teams are freed from repetitive formats and checklists, your next priority should be guiding them toward higher-impact challenges. That’s where long-term competitive advantages are built.
Key highlights
- GenAI improves software development productivity: Generative AI tools are significantly increasing developer output by automating code writing, testing, and debugging. Leaders should invest in genAI to accelerate delivery cycles without expanding technical headcount.
- Requirements gathering is now the bottleneck: As AI speeds up development, poorly defined requirements can create delays. Executives must ensure planning processes are optimized to deliver high-quality, structured user stories that AI systems can act on effectively.
- Requirements gathering is becoming collaborative and AI-assisted: GenAI transforms requirements into a cross-functional workflow where product owners and analysts work with stakeholders and AI to capture actionable insights. Leaders should enable teams to develop prompt engineering skills and prioritize validation of AI-generated content.
- GenAI accelerates prototyping and time to value: Agile teams now use genAI within development environments to iterate on stories and experiences in real time, reducing delivery timelines. Leaders should evaluate low-code AI platforms to scale rapid prototyping and improve stakeholder feedback loops.
- Strategic roles are shifting from documentation to innovation: As AI handles standardization and formatting, product leaders and analysts must now focus on direction, creative problem-solving, and strategic differentiation. Decision-makers should empower teams to stop spending time on process overhead and redirect attention to high-value initiatives.
