ServiceNow was established in 2004 by software architect Fred Luddy, aiming to enhance the world of work for people.

When Damien joined the company in 2011, it was still a pre-IPO startup with around 350 employees.

Today, ServiceNow has grown into a global leader with 28,000 employees, trusted by some of the world’s largest brands to facilitate digital transformation across enterprises.

After 14 years at ServiceNow, Damien Davis has witnessed the company’s evolution alongside AI and has become a vital part of ServiceNow’s strategic core.

As Senior Director in ServiceNow’s Customer Excellence Group—the human aspect of ServiceNow’s success—Damien plays a crucial role in shaping the company’s AI and customer success strategy.

This is a pressing concern for many executives; having fully invested in AI, they are now focused on the return. However, when CFOs begin to question return on investment, some boardroom discussions confront a stark reality.

“Almost every customer I speak with claims to have an AI strategy,” Damien states. “But what you really need is a business strategy, not just an AI strategy.”

“Many companies find AI exciting, but without a clear roadmap, adoption tends to slow down.”

This is where Damien steps in, at the crossroads of strategy and reality—managing analyst briefings, leading customer advisory boards, and bridging the gap between market expectations and practical solutions.

He observes the disconnect between how companies discuss AI and their actual implementations, then addresses it with ServiceNow’s effective strategies.

“My goal is to ensure that ServiceNow customers, partners, and analysts don’t just see the best of ServiceNow—they experience it,” he explains.

ServiceNow’s AI strategy: Native, not bolted on

As the world adapts to AI, so does ServiceNow.

“ServiceNow has evolved as an organization alongside the progression of AI,” Damien notes.

“While my role in customer and people engagement hasn’t changed significantly, the focus has shifted from features to outcomes.”

“So today, the question isn’t ‘what can ServiceNow do?’ but rather, ‘how quickly can AI help us transform potential into performance?’”

What began as an IT service management platform has evolved into a much more ambitious entity. Today, the company serves as the AI platform for business transformation, extending into HR, customer relationship management, security workflows, and any area where business processes exist.

While half of the enterprise software industry has been scrambling to integrate AI capabilities into existing platforms, ServiceNow has been embedding AI directly into its core workflows since 2017.

This timing is significant—ServiceNow started utilizing machine learning (ML) to predict incident categories and assignment groups for IT support tickets long before ChatGPT gained prominence.

By the time other companies were debating how to avoid being left behind, ServiceNow was already on its third generation of AI capabilities.

“We are AI native, not AI added on,” Damien clarifies. “This means our AI is integrated directly into the workflows. It’s not an add-on; it’s the engine.”

When AI is integrated into the platform from the outset, users don’t have to manage multiple interfaces or contend with cumbersome integrations.

ServiceNow also employs its own product internally. Its portal, My ServiceNow, combines its proprietary large language model (LLM) with external models like ChatGPT and Claude to offer personalized support for employees.

Damien notes that the company has experienced significant improvements in case resolution times and team productivity.

These internal implementations are showcased at conferences through the “Now on Now” program, demonstrating customer confidence by betting on the productivity of the technology it offers.

Success stories learned from early adopters

Damien highlights enterprise organizations in highly regulated sectors—such as financial services, government, and healthcare—that are effectively leveraging AI. In these areas, automation and governance are not optional but essential.

ServiceNow’s website features corporate giants it empowers to evolve: Uber, Delta Airlines, Kraft Heinz, and Visa.

Even Amazon has taken the stage to discuss how ServiceNow’s AI enhances automation in its operations centers.

The success stories reveal common themes, and Damien has distilled three key lessons from observing early adopters.

First, successful organizations begin small rather than attempting a complete transformation.

Second, these companies concentrate on business-critical use cases that deliver clear value.

Third, they prioritize governance and change management from the very start.

“Start small, scale fast,” he advises. “Organizations that succeed don’t wait for perfection. Select a business-critical use case, demonstrate its value, and then scale responsibly.”

This is practical guidance for achieving business reinvention through AI.

What’s becoming evident is that success is more closely linked to organizational readiness than to technical sophistication.

Companies thriving with AI typically have mature data governance, well-documented processes, and leadership committed to effective change management.

ServiceNow’s key to measurable AI success

One aspect that sets ServiceNow apart is that customer success is inherently built into the platform.

This manifests in three ways:

In-product success—customers access the Impact Store App directly within the platform they’re already using.

Guidance, accelerators, and insights are readily available, in context, without requiring users to leave their workflow.

AI Agents and Automation—customers can immediately leverage the platform’s native AI and automation capabilities.

This results in quicker troubleshooting, smarter recommendations, and automated actions that reduce effort and expedite delivery.

Together, these elements drive faster time to value—customers adopt innovations more swiftly, achieve outcomes sooner, and continuously enhance their experience on the Now Platform.

Moreover, ServiceNow’s partner ecosystem has expanded significantly, reflecting the company’s success and the complexities of deploying enterprise AI.

A notable partnership is with Fujitsu, a leading Japanese technology services provider.

Together, they have developed a joint offering that positions Fujitsu Customer Advisory and Support Excellence (CASE) alongside ServiceNow IMPACT.

This partnership enables IMPACT to deliver AI-driven customer success products that provide insights, guidance, and value acceleration—while CASE offers Fujitsu’s expert advisory and implementation services, combining the two offerings to maximize customer value.

Damien has a personal connection here, having spent eight years at Fujitsu before joining ServiceNow.

Here, Damien’s practical solutions shine through again—ServiceNow measures AI success using the same metrics applied to any technology implementation: reduced costs, increased productivity, faster time to value, and improved employee and customer experiences.

“AI success is evaluated the same way as any technology success,” he asserts.

“It’s measured in outcomes. The method of measuring success doesn’t change whether you’re using AI or any other technology.”

No elaborate new KPIs, no obscure AI-specific metrics—just results.

ServiceNow’s CEO Bill McDermott encapsulates this in terms of trust: “Trust is the ultimate human currency.”

Supporting Bill’s assertion, Damien remarks: “I’m not a deep dive techie; I’m not an engineer, and I don’t come from an engineering background. However, my blend of platform and product knowledge, along with customer engagement, has shaped my path into the customer excellence group, where we scale our success globally.”

The future of human and AI collaboration

Looking ahead, Damien anticipates that companies will cease to treat AI strategies as separate initiatives.

Instead, AI will be integrated into every workflow and revenue stream. Corporate boards will transition from asking “what’s our AI strategy?” to “how is AI enhancing our core business functions?”

Three trends are emerging that business leaders should monitor: ethics, governance, and trust frameworks; AI disaster recovery (AIDR) planning akin to IT continuity protocols; and the shift from task automation to AI-enabled decision-making.

AI disaster recovery is a journey that some companies are currently navigating, some are nearing, and others can avoid.

Damien cites change management as an example—traditionally, IT teams require human approval for system changes based on risk assessment. As AI agents gain the ability to make autonomous decisions, he asks: “At what point do we want a checkpoint where a human must make a decision?”

“If this system fails, what’s the impact on the business? If the payroll system fails, is it catastrophic?” Damien inquires.

“We’ve applied that same analogy to AI. At what stage do we want AI to make decisions that ensure business continuity and maintain technology safety and security?”

“This emerging trend can be summarized as human and AI collaboration. We refer to it as the bionic enterprise, where people and AI enhance each other.”

“What distinguishes humans from AI?” he asks. “Curiosity, adaptability, and trust-building, because AI transformation is a journey.”

“Business leaders must ask the right questions and adapt swiftly; otherwise, their competitors will seize the advantage—and they need to bring their teams along.”

“AI handles the repetitive and predictive tasks, while humans contribute judgment, creativity, and, importantly, empathy,” Damien states. “The best outcomes arise from blending both.”

Damien acknowledges a point often overlooked in AI discussions: the technology is most effective when it complements human capabilities rather than replaces them.

Organizations achieving tangible success focus on enhancing human decision-making rather than eliminating human involvement entirely.

For enterprise leaders still in the experimentation phase, Damien advises anchoring AI initiatives to specific business challenges rather than treating them as mere technology experiments.

“Select one use case that affects revenue, cost, or risk, demonstrate its value, and then scale the approach,” he recommends.

“We utilize judgment, creativity, empathy, and curiosity. When you combine these elements, that’s how the relationship between human expertise and AI evolves.”