Damien Davis | Tech Journal

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

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

Today, ServiceNow boasts a global workforce of 28,000, trusted by some of the world’s largest brands to lead digital transformation across enterprises.

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

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

Executives are increasingly concerned about the return on their AI investments. When CFOs begin questioning return on investment, some boardroom discussions confront a stark reality.

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

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

This is where Damien steps in, bridging the gap between strategy and reality—managing analyst briefings, conducting customer advisory boards, and translating market desires into effective actions.

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 don’t just see the best of ServiceNow—they experience it,” he asserts.

ServiceNow’s AI strategy: Native, not bolted on

As the world adapts to AI, ServiceNow has evolved as well.

“ServiceNow has matured as an organization alongside the evolution of AI,” Damien remarks.

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

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

What began as an IT service management platform has transformed into a much broader ambition. 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 scrambles 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 an impact.

By the time other companies were debating whether they were falling behind, ServiceNow was already on its third generation of AI capabilities.

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

With AI built into the platform from the ground up, users avoid juggling multiple interfaces or dealing 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.

ServiceNow now highlights these internal implementations at conferences through its “Now on Now” program—demonstrating customer confidence by betting its own productivity on the technology it promotes.

Success stories from early adopters

Damien highlights enterprise organizations in highly regulated sectors that are effectively leveraging AI—financial services, government, and healthcare—where automation and governance are essential, not optional.

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 enhances automation in its operations centers.

The success stories share 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. Identify a business-critical use case, demonstrate its 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.

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’re 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 faster troubleshooting, smarter recommendations, and automated actions that reduce effort and expedite delivery.

Together, these elements drive quicker time to value—customers adopt innovations more rapidly, achieve outcomes sooner, and continuously enhance the Now Platform.

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

A notable partnership is with Fujitsu, the 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 collaboration allows 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 both offerings to maximize customer value.

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

Damien’s practical solutions shine through 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 using AI or any other technology software.”

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

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

Supporting Bill’s point, Damien states: “I’m not a deep dive techie, nor an engineer, and I don’t come from an engineering background—but my blend of platform and product knowledge, along 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 be woven 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 akin to IT continuity protocols; and the shift from task automation to AI-enabled decision-making.

AI disaster recovery is a concern for some companies, while others are nearing it or can avoid it altogether.

Damien uses 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’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 point do we want AI to make decisions that ensure business continuity and maintain technology safety and security?”

“This concept of human and AI collaboration is emerging as a trend. We refer to it as the bionic enterprise, where humans and AI enhance each other.”

“What distinguishes humans from AI?” he asks, “is curiosity, adaptability, and trust-building, as 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 engage their teams in the process.”

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

Damien recognizes a crucial point often overlooked in AI discussions: technology excels when it complements human capabilities rather than replaces them.

Companies achieving 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 mere technology trials.

“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. By blending these elements, we foster the evolving relationship between human expertise and AI.”

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