Integrating Human Skills and AI for Business Achievement

ServiceNow was established in 2004 by software architect Fred Luddy, aiming to enhance the world of work 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 world’s largest brands to drive 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 go-to-market AI and customer success strategy.

This is a pressing issue for many executives: having fully invested in AI, they are now focused on the return. However, when CFOs begin questioning return on investment, some boardroom discussions confront a harsh 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.”

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

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 explains.

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 notes.

“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 turn potential into performance?’”

What began as an IT service management platform has transformed into a much more ambitious entity. Now, 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 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 discussing the risk of 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 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.

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

Success stories from early adopters

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

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

Even Amazon has taken the stage to discuss how ServiceNow’s AI aids 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 start small rather than attempting a complete transformation.

Second, these companies concentrate on business-critical use cases that provide 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.

What is 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, clear process documentation, and leadership committed to effective change management.

ServiceNow’s key to measurable AI success

One distinguishing feature of ServiceNow 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 are using.

Guidance, accelerators, and insights are readily available, in context, without leaving 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 improve on the Now Platform.

Moreover, 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 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 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 states.

“It’s measured in outcomes. The method of measuring success remains unchanged whether you’re using AI or any other software technology.”

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.”

Reinforcing Bill’s point, Damien adds: “I’m not a deep technical expert, nor do I have 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 treat AI strategies as separate initiatives.

Instead, AI will be integrated across 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 journey some companies are navigating, while others are close to it, and some may avoid it altogether.

Damien uses change management as an illustration—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 ensure business continuity and maintain technology safety and security?”

“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 one another.”

“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 win the race, and they need to bring their teams along with them.”

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

Damien recognizes a common oversight in many AI discussions: the 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 technology trials.

“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 the relationship between human expertise and AI evolves.”