ServiceNow is an AI platform that helps organizations automate and manage workflows across IT, HR, customer service, security, and other business functions through a unified digital platform.
You have had an impressive career, from Oracle to helping scale Google Cloud. What was it about ServiceNow that made you embark on this new journey?
I spent more than 30 years in enterprise software, and during my time at Google I worked extensively on AI infrastructure, models, and capabilities. I had followed ServiceNow’s journey for many years and saw an opportunity to take the technologies developed at the infrastructure layer and use them to transform workflows and applications in ways that deliver real business value, not just technology.
What attracted me was that ServiceNow sits at the heart of the enterprise. More than 90% of Fortune 500 companies use it, and it serves as an enterprise operating system that connects and automates systems across organizations. The culture also stood out to me. Despite its scale, it still has a startup mindset: customer-centric, fast-moving, humble, and hungry. It felt like the ideal place to apply everything I learned at Oracle and Google and help drive the next phase of growth.
Many business leaders say one of the biggest AI adoption challenges is the lack of a unified approach. Is that why you believe organizations need an AI platform underneath their AI agents?
One of the biggest mistakes companies make is trying to assemble AI capabilities from separate technology components. Building a prototype is one thing; turning it into a production-grade system is another. Enterprises need security, compliance, governance, auditing, and scalability. Those are often the hardest parts, yet they are critical to success.
A platform helps organizations manage complexity while focusing on outcomes rather than technology.
Many early AI projects failed not because AI failed, but because companies lacked the governance, visibility, and controls needed to deploy it effectively. Customers want results. They do not care which model or chip is underneath. They care whether employees become productive faster, whether processes are automated, and whether the business operates more efficiently.
Can you share examples of customers that have achieved measurable value through ServiceNow?
Organizations such as CVS Health have used ServiceNow to create a unified front door for employees, allowing them to access HR, IT, onboarding, and support services from one place. By consolidating onto a single platform, they reduced technical debt, cut live-agent chats by 50%, and now support more than one million AI-powered conversations.
We are seeing similar results across many industries. FedEx runs millions of workflows each month and has offloaded more than half of inbound IT work through AI. Hitachi Energy achieved a tenfold increase in employee self-service adoption, while Honeywell has deflected 80% of inbound requests. Internally, ServiceNow has generated more than $500 million in cumulative AI value, with 90% of IT support handled autonomously and support teams redeployed to higher-value work.
What is the biggest challenge or concern customers raise with you today?
The number one concern is trust and control. Customers want to know how AI agents will be governed, what access they have to data, and how organizations can ensure they do not take inappropriate actions. As AI agents become more autonomous and capable of spawning additional agents, visibility and lifecycle management become essential.
The second major concern is ROI. Organizations want to understand how they can measure value from their AI investments, including efficiency gains, cost savings, and customer satisfaction improvements. That is why we introduced AI Control Tower, which focuses on discovery, governance, compliance, identity management, and oversight across AI systems.
How do you make AI systems more predictable and trustworthy in enterprise environments?
One of the challenges with large language models is that they are inherently probabilistic. The same question can produce different answers, which makes it difficult to run critical business processes. Enterprises need predictable outcomes when it comes to payroll, procurement, compliance, and customer service.
That is where our Context Engine comes in. We process more than 100 billion workflows and seven trillion transactions annually, capturing the real operational knowledge of the enterprise. Instead of relying only on documentation, the Context Engine understands how work is actually done, including exceptions and historical decisions. That context helps AI deliver more accurate, consistent, and reliable outcomes.
Why is context so important when deploying AI in the enterprise?
AI without context often produces incomplete or inconsistent results. For example, onboarding a new employee may involve numerous steps and exceptions depending on the person’s role, department, and responsibilities. If AI lacks that context, the process may need to be repeated or corrected later.
When context is incorporated, employees can become productive much faster. The same principle applies across finance, customer service, HR, and other functions. Faster, more accurate outcomes improve productivity, customer satisfaction, and overall business performance. That is why we believe context is one of the key differentiators in enterprise AI.
Many companies feel pressure from boards and executive teams to demonstrate progress with AI. There is tremendous value in AI, but organizations need to focus on achieving successful outcomes rather than simply deploying technology for its own sake.
Enterprise systems have traditionally been built with security, compliance, scalability, and integration in mind. When organizations rush to assemble disconnected AI solutions, those fundamentals are often overlooked. The key is to adopt AI thoughtfully and strategically, ensuring it creates measurable business value.
What are the biggest security concerns organizations face, and how do you address them?
Many people do not realize that ServiceNow has a security business exceeding one billion dollars in annual revenue. We have become the gold standard for incident management and post-breach response, helping organizations triage, manage, and resolve security incidents quickly and effectively.
Today’s challenges extend beyond traditional cybersecurity to include AI agents, non-human identities, IoT devices, operational technology, and connected systems. We provide proactive monitoring, governance, and visibility into what AI systems can do, while also offering controls such as kill switches to prevent harmful actions. Our goal is to give organizations confidence that they can innovate safely while maintaining visibility, security, and control.
What are your top priorities for the next 12 months?
Our focus is on bringing workflows, AI, data, and security together into a unified platform. We want customers to adopt advanced technologies quickly while solving real business problems and improving both top-line growth and operational efficiency.
We are investing heavily in AI Control Tower, governance capabilities, and our Context Engine. We are also expanding AI specialists, which are digital workers capable of handling entire tasks autonomously. Whether in customer service, HR, security operations, or IT support, our goal is to deliver faster outcomes, measurable ROI, and production-ready AI that customers can trust.
