From “AI Deployment” to “Governance” Perforce CTO Anjali Arora on the Next Big Question in Software Delivery

Most software leaders are still asking how fast they can adopt AI. Anjali Arora, Chief Technology Officer at Perforce, is more focused on what happens after adoption. Having led product, engineering, and security through large public companies and private-equity-backed businesses alike, including Oracle, CA Technologies, and Rocket Software, Arora has built a career on a simple conviction: technology should solve a real problem, and governance should be built in from the start rather than bolted on at the end.

That philosophy now sits at the center of Perforce’s strategy as the company positions itself as a governance layer for AI-native software delivery. In this conversation, Arora discusses why not every technology trend deserves a product strategy, how engineering teams can improve speed and reliability at the same time instead of trading one for the other, and why she believes the real inflection point for enterprises isn’t deploying AI agents, but governing what they do once they’re already part of the pipeline.

Your leadership journey has taken you through Oracle, CA Technologies, Rocket Software, and now Perforce. Which experiences have most shaped your approach to leading tech organizations and driving innovation?

I've had the opportunity to work across large public companies and private-equity-backed businesses, and while the technologies have changed, the fundamentals of leadership really haven't. The biggest lesson has been that innovation comes from creating strong engineering cultures where people are trusted, challenged, and encouraged to keep learning. Throughout my career, I've invested heavily in coaching and mentoring because I've seen first-hand how developing people creates stronger products and stronger organizations.

Every company has also reinforced the importance of staying close to customers. Technology should solve a real problem. Innovation for its own sake rarely creates lasting value. The best ideas come from understanding real business problems and then bringing together product management, engineering, and customers to solve them. That is the approach we're taking at Perforce as software delivery evolves into an AI-native world.

As CTO, you oversee product, engineering, security, and innovation. How do you translate fast-changing technology trends into a product strategy that balances customer needs, engineering priorities, and business goals?

Not every technology trend deserves a product strategy. We spend a lot of time separating what's interesting from what's actually going to change how our customers build and deliver software. AI is a good example. Rather than asking, "How do we add AI?", we ask, "How do we help enterprises adopt AI safely, efficiently, and at scale?" That means balancing innovation with governance, security, and how enterprises actually operate, particularly for customers building business-critical software.

The other key is bringing product, engineering, and security together from the beginning rather than treating them as separate disciplines and having governance built in from the beginning. When those teams work from a shared understanding of customer outcomes, you avoid building technology in isolation and instead create platforms that solve today's problems in a controlled way while remaining flexible enough for whatever comes next.

Perforce describes itself as the modern DevOps tech stack built for AI governance. How are your solutions helping customers innovate faster while maintaining the control and reliability enterprise software demands?

We're seeing customers move beyond experimenting with AI to applying it throughout the software delivery lifecycle. That creates enormous opportunities for productivity, but it also introduces new challenges around traceability, compliance, security, and accountability. Our focus is helping customers move faster without losing visibility into what AI is doing across the software delivery lifecycle.

Rather than asking customers to choose between speed and governance, we're embedding governance directly into the software delivery process. Whether it's version control, testing, infrastructure automation, or data management, we're embedding governance directly into the workflow instead of asking teams to bolt it on later.

Engineering leaders are constantly balancing speed, quality, security, and compliance. How is Perforce helping customers eliminate these trade-offs rather than simply manage them?

For many years, organizations accepted that moving faster meant accepting more risk. I don't think that's sustainable anymore, especially with AI accelerating software development. The most successful engineering teams are increasingly embedding quality, security, and compliance into their delivery pipelines instead of treating them as checkpoints at the end.

What we are seeing with customers is that organizations with mature DevOps and platform engineering practices are much better positioned to adopt AI successfully because governance is already embedded into how they work. By integrating policy, testing, infrastructure management, and compliance into everyday workflows, engineering teams can improve speed and reliability simultaneously, instead of treating them as competing priorities.

Enterprise customers increasingly want platforms that integrate with their existing toolchains instead of replacing them. How has that expectation shaped Perforce's product strategy and innovation roadmap?

Our customers have made significant investments in their software delivery ecosystems, so asking them to replace everything simply isn't realistic. Our philosophy has been to meet customers where they are and help them modernize progressively by integrating across existing toolchains while strengthening governance without disrupting the way they already work, automation, and developer productivity.

That's even more critical as we think about AI. Companies are rolling out new models, agents, and development tools rapidly. We want to provide a governance layer that spans these heterogeneous environments so customers can adopt new AI capabilities without losing consistency, visibility, or control.

You've long championed inclusive leadership and women in technology. From your experience, what separates organizations that genuinely build high-performing, diverse engineering teams from those that don't?

It starts with recognizing that diverse teams build better products. I have seen that consistently throughout my career. The strongest teams I've worked with are those where different perspectives are actively encouraged, people feel comfortable challenging ideas, and leaders invest time in developing future talent. Coaching and mentoring have always been important to me because some of the most rewarding moments in my career have been watching people grow from interns into engineering leaders and executives.

Enterprises also need to build a genuine pipeline of talent. That means supporting women from education through to leadership, creating visible role models, and ensuring people have access to mentors and sponsors. Diversity doesn't happen by accident: it happens because leaders consistently make it a priority.

Looking ahead, AI will increasingly write code while humans focus on governance and strategy. What role do you want Perforce to play in that future and what mindset shift should tech leaders make before it's too late?

AI will fundamentally change software engineering, but I don't believe it's about replacing engineers. Instead, it will elevate their roles from writing every line of code towards designing systems, defining intent, validating outcomes, and governing increasingly autonomous software delivery. The organizations that succeed will be those that treat governance as an enabler of innovation rather than a barrier to it.

Our ambition at Perforce is to become the control plane that allows enterprises to adopt AI with confidence. As AI agents become active participants in software delivery, enterprises will need visibility, traceability, policy enforcement, and continuous compliance built into every stage of the lifecycle. For years the question has been, "How do we deploy AI?" Over the next few years, I think the much bigger question becomes, "How do we govern AI once it's participating across the software delivery lifecycle?" The organizations that answer that well will be the ones that realize the greatest value from AI.