Jyoti Shah is a Director of Applications Development, a GenAI tech leader, mentor, innovation advocate and Women In Tech advisor at ADP.
When I first started leading enterprise software projects, the first step was always the same: a whiteboard full of sticky notes, half-finished user stories and weeks of questions before anyone wrote a single line of code.
That process is very different now that AI is at the table with us. I don’t mean a prototype for a lab in the future. I mean real, production-ready systems that help my teams turn business goals into working software faster, smarter and with insights we didn’t expect.
Over the last few years, I’ve used AI to change the path from user story to deployment. It has changed how we deliver value, speed up release cycles and get more out of our investments.
It starts right at the whiteboard. During discovery, I feed raw user stories into a GenAI model trained on our domain language. The AI instantly clarifies vague ideas, fills in missing acceptance criteria and maps dependencies. A note like, “As a manager, I want better dashboards” becomes, “As a regional sales manager, I want AI-generated dashboards showing weekly revenue, churn and forecast accuracy.” That kind of precision saves entire sprints and keeps business and engineering perfectly aligned.
Once stories are refined, AI copilots turn intent into architecture. If I describe a service that collects key performance indicators (KPIs), caches data hourly and serves React APIs, the system immediately produces YAML configurations, class structures and cloud resources. What once took a week of meetings now happens in minutes.
In coding, AI becomes a true collaborator—comparing trade-offs, optimizing performance and improving readability. The result is faster delivery, cleaner code and fewer bugs, all while freeing developers to focus on the big picture.
Testing and documentation have also evolved. Large language models now generate test cases, spot likely regressions and even write “living” documentation tied to every commit. I can ask, “Explain our caching strategy,” and I’ll get a clear, connected summary on demand. That means fewer production issues, faster onboarding and better compliance without the usual maintenance overhead.
In short, AI has turned software development into a real-time conversation between people and machines. It accelerates delivery, improves quality and makes innovation the default pace of work.
Governance is more important than ever now that things are moving so quickly. We’ve made it clear how and where my teams can use generative tools. A person checks every output that AI helped. Scrubbing prompts keep data safe, and usage logs make sure people are responsible.
These protections don’t slow us down; they make us trust each other more. They show that compliance and innovation can happen at the same time and that governance isn’t a barrier to creativity but a way to keep things moving quickly. When you have the right controls in place, AI stops being a threat and starts being a way to get ahead of the competition.
Strong governance also means figuring out who owns what early on. We made “AI champions” in engineering, product and security who work together to set best practices and teach others. That makes experimentation a group effort rather than a separate project. We don’t just look at faster delivery as a sign of success; we also look at how it affects the business, such as better release quality, less rework and measurable productivity increases.
Equally important is the cultural shift. Teams need to feel empowered, not replaced. We emphasize that AI is here to accelerate human creativity, not automate it away. Engineers are encouraged to question AI output, refine it and make it their own. That dialogue between human intuition and machine precision is where the real innovation happens.
Finally, leadership must model the behavior they expect by using AI tools themselves, sharing lessons learned and being transparent about failures as well as wins. When governance, culture and accountability align, GenAI becomes a business transformation strategy that scales trust as fast as it scales code.
Using AI in software development is about changing the way teams work together, try new things and figure out what success looks like. The first step is to start small but with a purpose. Find a few areas that slow down delivery and can be automated, such as gathering requirements, generating tests or writing documentation. Then, test AI use cases with clear success metrics. You should start by using GenAI to improve user stories and translate architecture. The early successes can give teams confidence and make them see AI as a helpful partner instead of a threat.
Once pilot results are in, scale deliberately with governance at the center. Build an AI policy that defines acceptable use, data handling and review processes. Every prompt, model and output should have a clear audit trail. We learned that this structure can accelerate innovation by giving teams confidence that they’re experimenting responsibly. Establishing cross-functional “AI champions” across engineering, product and security ensures adoption spreads horizontally, not hierarchically, creating a culture of shared accountability.
Next, work on building your skills. People won’t use an AI strategy if they don’t feel ready to. Put money into programs that teach them how to make prompts, evaluate models and use data in an ethical way. Put senior engineers with developers who are interested in AI to help them learn by doing. Recognize and celebrate people who come up with new ways to use AI to help with human judgment. This shows that everyone has a role to play in innovation, not just data scientists.
Finally, measure what matters. Beyond productivity gains, track how AI impacts software quality, decision velocity and employee satisfaction. The goal isn’t just to code faster—it’s to build smarter, safer and more sustainable systems.
In my experience, organizations that treat AI adoption as both a technical evolution and a cultural transformation see the most lasting impact. The key is balance; combine the precision of machines with the discernment of people, and you can turn AI from a trend into a durable advantage.
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