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.

How does your team stay ahead of emerging technology trends while scaling fast?

We stay ahead by maintaining a “redshift” mindset, a commitment to high-velocity analysis and decisive action. A cornerstone of this is our weekly Redshift Forum, where we bring together top AI leaders to deep dive into the latest technical shifts. New technologies arrive in weekly waves, so it’s impossible for any one individual to see the full picture. These sessions allow us to collaboratively peer around corners and whiteboard proposals to build conviction about which trends are truly transformational.

Once that conviction is formed, we move instantly. We have built a technical environment specifically designed for rapid experimentation, allowing us to test new ideas as “probes” that can fail fast without systemic risk. By fostering a culture that encourages trying ideas with minimal friction and a heavy focus on high-level strategy over heavy preparation, we ensure our team isn’t just riding the AI wave but actively navigating it to deliver agentic solutions at scale.

What recent product or feature are you most proud of — and what impact has it had?

We’ve bridged the gap between information and action by unifying our Enterprise Search and Agentic Platform into a single system of intelligence. While Enterprise Search surfaces the right context from fragmented business software, the Agentic Platform provides the reasoning brain to act on it. Together, they allow our AI to not only understand complex enterprise needs but also execute multi-step workflows autonomously across the entire organization.

The addition of Agent Studio takes this further, allowing developers to extend our ecosystem with custom business logic. This transforms our AI into a programmable framework that enables developers to plug their systems into our reasoning engine.

How do you create a culture where innovation and experimentation are encouraged daily?

Innovation isn’t about having a perfect map of the future; it’s about the agility to navigate it. We recognize that it is impossible to always bet correctly in the next direction in such a volatile landscape. Therefore, our priority is building a technical foundation that enables rapid iteration and a culture where people feel safe to fail fast.

AI is fundamentally changing how software is built, from the user experience down to the back-end systems. To meet this shift, we function as agile, virtual teams comprising product managers, designers, front-end, AI and back-end engineers. This cross-functional structure allows us to break down silos and make critical decisions quickly. By pairing this organizational speed with a flexible architecture, we ensure teams are positioned to build conviction and move from experiment to production-ready innovation.

Machine learning teams drive platform agility by building contextually precise small language models engineered for specific enterprise tasks. By fine-tuning these systems on high-quality, diverse datasets, the organization achieves frontier-level capabilities while significantly optimizing speed and costs. 

"They can achieve performance levels comparable to large frontier models across a wide range of domain-specific tasks while delivering faster response times and substantially lower costs." —Ranga Prasad C., Director of Machine Learning Engineering

The technical environment prevents operational friction by encouraging software engineers to analyze system-wide cross-dependencies across multiple database and application layers. This holistic design baseline helps development teams safely scale custom platform plug-ins. 

"You need to adjust your focus to clearly see, then stand back and consider how the changes might affect the other layers." —Adriana F., Senior Software Engineer