ServiceNow Innovation & Technology Culture

I feel that being part of ServiceNow’s exciting GenAI journey and contributing to our vision of transforming the platform into an AI-native platform is a learning experience I’ll value for a long time.

ServiceNow sits at a unique intersection,” Jha said. “We're the system of action for the enterprise, which makes the platform an AI-native operating system for workflows. Models and data are governed centrally, embedded into real processes, and measured against business outcomes.

How does innovation show up in your company culture?

At ServiceNow, innovation isn’t a scheduled event — it’s the default operating mode. On my team, we’ve built a culture where curiosity is a job requirement, and the best ideas can come from anywhere in the organization. We treat customer friction as design inspiration and every failed experiment as a signal worth learning from. That mindset has to start at the top: If leaders aren’t modeling intellectual courage and psychological safety, teams won’t take the risks that real innovation demands.

What I’m most proud of is that we innovate with intention. We’re not chasing novelty for its own sake; we’re asking how AI can genuinely reduce the burden on employees and create enterprises that work smarter. That clarity of purpose is what separates interesting experiments from transformational products.

What’s one recent innovation that improved user or employee experience?

One of the most meaningful things we’ve built recently is the AI Control Tower — ServiceNow’s system of record for enterprise AI governance. As organizations deploy AI agents across their operations, they quickly run into a painful reality: No one has visibility into what’s running, what it’s doing or whether it can be trusted. Employees and leaders are left flying blind.

AI Control Tower changes that for our customers. It gives enterprises a single place to discover, monitor, and govern every AI model and agent in their environment, turning an anxiety-inducing black box into something auditable and controllable. The experience shift for the people responsible for enterprise AI is profound: from reactive and overwhelmed to confident and in control. That’s the kind of innovation worth building.

How do you balance experimentation with stability?

The honest answer: It’s a constant, productive tension, and I’d be skeptical of anyone who says they’ve fully solved it. My framework is to experiment aggressively at the edges while protecting the core. New AI capabilities can move fast; the foundational workflows that thousands of employees rely on daily cannot afford instability.

In practice, that means tight feedback loops, clear plans and an iterative mindset baked into how we build from day one. The teams that do this well aren’t the ones that slow down; they’re the ones that have earned enough trust to keep accelerating. Speed and stability aren’t opposites — they’re a contract you build with your users over time.