ServiceNow was established in 2004 by software architect Fred Luddy, aiming to enhance the work experience for individuals.

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 largest brands worldwide to facilitate digital transformation across enterprises.

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

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

This issue is increasingly on the minds of executives: having fully invested in AI, they are now focused on the returns. 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 it’s not about having an AI strategy; it’s about having a business strategy.”

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

This is where Damien steps in, operating at the intersection of strategy and reality—managing analyst briefings, conducting customer advisory boards, and bridging the gap between market expectations and practical outcomes.

He observes the disparity 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 not only see the best of ServiceNow but also experience it,” he remarks.

ServiceNow’s AI strategy: Native, not bolted on

As the world has evolved with AI, so has ServiceNow.

“ServiceNow has developed as an organization alongside the evolution 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 the question now isn’t ‘what can ServiceNow do?’ It’s ‘how quickly can AI help us convert potential into performance?’”

What began as an IT service management platform has transformed into a more ambitious offering. 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 sector has been rushing to integrate AI capabilities into existing platforms, ServiceNow has been embedding AI directly into its core workflows since 2017.

This timing is significant—ServiceNow began utilizing machine learning (ML) to predict incident categories and assignment groups for IT support tickets long before ChatGPT made its impact.

By the time other companies were debating 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.”

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

ServiceNow also utilizes 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 now 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 industries that are effectively leveraging AI—such as financial services, government, and healthcare—where automation and governance are essential requirements.

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

Even Amazon has taken the stage to discuss how ServiceNow’s AI assists in driving automation within 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 outset.

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

This practical advice is essential for achieving business reinvention through AI.

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

The 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

What sets ServiceNow apart is that customer success is inherently integrated into the platform.

This manifests in three ways:

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

Guidance, accelerators, and insights are readily available, in context, without needing to exit the 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 enhance efficiency 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.

Additionally, ServiceNow’s partner ecosystem has expanded significantly, reflecting the company’s success and the complexities involved in deploying enterprise AI.

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

Together, the companies 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, merging 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 again—ServiceNow measures AI success using the same metrics applicable 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 criteria for measuring success remain unchanged 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 articulates this in terms of trust: “Trust is the ultimate human currency.”

Supporting Bill’s assertion, Damien remarks: “I’m not a deep technical expert, I’m not an engineer, and I don’t come from an engineering background—but my blend of platform and product knowledge with customer engagement has shaped my journey 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 view AI strategies as separate initiatives.

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

AI disaster recovery is a consideration for some companies, a current concern for others, and something that some may avoid altogether.

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

“If this system fails, what is 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 point do we want AI to make decisions that will ensure business continuity and maintain the safety and security of technology?”

“Wrapping this all together as an emerging AI trend is the concept of 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, “is 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 gain the upper hand—and they need to bring their teams along with them. 

“AI handles the repetitive and predictive tasks, while humans contribute judgment, creativity, and, most importantly, empathy,” Damien explains. “The best outcomes arise when both elements are combined.”

Damien acknowledges a common oversight in many AI discussions: the technology is most effective when it complements human capabilities rather than replaces them.

Organizations experiencing genuine 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 impacts revenue, cost, or risk, demonstrate its value, and then scale the approach,” he suggests.

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

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