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What does it take to put artificial intelligence (AI) in the hands of those doing the work that keeps everything running? That was the focus of our recent conversation with Bulent Cinarkaya, General Manager of Field Service Management at ServiceNow.
While most AI stories still revolve around automation in call centers or large-scale data crunching, ServiceNow focuses on something very different. It’s bringing agentic AI to technicians working onsite, often in complex or high-pressure environments, where the stakes are real, and mistakes can cost time, money, or customer relationships.
Deskless workers comprise 80% of the global workforce. But they’re often the last to benefit from enterprise technology. That gap is closing, and the timing couldn’t be better.
Key Takeaways
- AI now supports technicians onsite, not just in back-office workflows.
- Companies are already seeing measurable operational and productivity gains.
- Scheduling now factors in technician efficiency and job-specific cost insights.
- Explainable AI is building trust among technicians and easing resistance to change.
- If you are still stuck in AI proofs-of-concept, you’re not innovating. You’re stalling.
From Invisible Assistance to Real-Time Support
When asked how we got here, Cinarkaya described how field service teams have been using AI quietly for years.
“They’ve been using AI for a long time,” he said. “But behind the scenes. It’s about scheduling and dispatching, getting the right technician to the right place at the right time.” Until recently, that was where the role of AI ended. Once a technician arrived on-site, they were mainly on their own.
That is now changing. Cinarkaya explained:
“Agentic AI is right there with them. It’s helping summarize knowledge articles, contracts, and customer obligations. Even before getting in front of the customer, the technician sees briefing summaries. And when the job is done, AI wraps it up.”
It’s the kind of support that frees technicians from digging through documentation or retyping service reports. Instead, they stay focused on the task at hand.
This has a knock-on effect on customer experience. “The last thing a technician wants is to search for information or manually enter data under pressure,” Bulent said.
When the system does that part for them, they can focus entirely on solving the issue faster and with less stress.
Reactive, Proactive, Predictive
During our conversation, Bulent Cinarkaya broke down field service operations into three types.
- First, there’s reactive service. Something breaks, and someone has to fix it.
- Then, there’s proactive service, such as scheduled maintenance or actions based on trends across multiple customers.
- Finally, there’s predictive service, where AI identifies issues before they happen based on IoT data or historical patterns.
AI can help across all three. In reactive cases, it suggests resolutions. In proactive scenarios, it manages communication across many sites. It alerts teams to emerging issues in predictive situations and provides recommendations based on likely outcomes. This is where AI becomes less of a tool and more of a partner.
One of the more striking parts of our conversation was how AI is helping new technicians ramp up quickly. Cinarkaya noted:
“There’s a labor shortage. A lot of experienced workers are retiring, and new ones are joining with very different expectations.”
Younger employees, especially Gen Z, often treat field service as a short-term opportunity. That gives companies a very narrow window to retain them and get them productive.
To explain how AI fits into that challenge, Bulent described a concept from neuroscience:
“Your brain has two systems. System 1 is automatic and fast. System 2 is slower and more deliberate. Veteran technicians rely on instinctive patterns they’ve built over decades. New hires don’t have that. But AI can bridge that gap by providing system-level insights that help guide decisions.”
Real-World Results That Speak for Themselves
There are also measurable business results. Bulent pointed to several real-world examples:
“Bell Canada uses this technology to optimize scheduling for 11,000 technicians. Coursera has seen an 18 percent reduction in incident escalations. British Telecom cut mean time to resolve by 33 percent.”
Even internally, ServiceNow is applying these tools. He said:
“AI handles seventy-two percent of our customer support requests. Our HR department has saved three million hours. Developer productivity is up twenty percent.”
These are not experimental numbers. These are live, operational results. And they reinforce the idea that AI doesn’t just tweak performance when applied correctly. It reshapes what’s possible.
Bulent Cinarkaya, General Manager of Field Service Management at @ServiceNow shares how AI is transforming the frontlines of field service and tackles the AI ROI question on Tech Talks Daily. https://t.co/qB2tJbLTed pic.twitter.com/gweZaccELw
— Neil C. Hughes (@NeilCHughes) May 27, 2025
Bulent also described how scheduling has been reimagined by going beyond logistics and toward a focus on strategy.
“Every job has a cost and brings value,” he said. Rather than optimizing only for speed, the system now accounts for cost-effectiveness and technician efficiency.
If one technician can complete a task in ninety minutes and another in seventy, the platform considers that. That adds precision and clarity to scheduling while giving dispatchers room to make human decisions.
Appointment booking is being reworked too. Rather than offering arbitrary time slots, the system recommends windows based on the technician’s location and job type. “If similar jobs are nearby, we can bundle them,” Bulent said. This saves time, reduces fuel consumption, and improves the customer experience without requiring customers to understand its complexity.
Changing Mindsets With Explainable AI
Predictably, technician buy-in is still a challenge for some organizations. AI-driven scheduling is taking away autonomy, particularly for those who have been manually assigning jobs for years. ServiceNow is addressing that with what Bulent called “explainability.”
“If a dispatcher asks, ‘Why did Jane get this job and not Joe?’ the system can now answer,” he said. This helps shift the tone from resistance to understanding. Technicians and managers can ask questions, get direct responses, and even see recommendations to override the system.
That clarity helps, but so does reducing the hassle of post-job paperwork. Bulent shared one recent demo that drew a lot of interest.
After completing a task, a technician can photograph the final result. Agentic AI then fills out the job closure form, checks for any follow-ups, books additional appointments if needed, and even orders parts. All of this happens while the technician moves on to the next task.
Building a Platform That Connects Everyone
These tools are made possible by the architecture that underpins ServiceNow’s platform.
“This isn’t a bolt-on,” Bulent said. “The field service application is fully integrated. It shares the same data model, architecture, and platform as everything else.” That gives field workers the same access to tools and information as someone sitting in a call center or corporate office.
ServiceNow also builds its CRM capabilities, supporting field service, sales, and order management through a unified approach. This broader integration strategy allows organizations to coordinate services more cohesively and consistently.
Too many teams get stuck in endless AI pilots. Start with the right use case, don’t reinvent the wheel, and focus your IP where it matters. At Knowledge, we showed how out-of-the-box AI lets customers stop proof-of-concepts and start delivering. pic.twitter.com/jvghPFZITV
— Neil C. Hughes (@NeilCHughes) May 27, 2025
The Bottom Line
Bulent Cinarkaya closed the conversation by reflecting on the enthusiasm surrounding agentic AI. At the Knowledge25 event, his team’s roadmap session occurred on the final afternoon. He expected a half-empty room. Instead, it was standing room only.
Customers were so engaged that they began answering each other’s questions. They weren’t just exploring AI as a concept. They were using it, adapting it, and contributing ideas to improve it.
For a technology often seen as abstract, that kind of hands-on momentum shows something real is happening. Technicians no longer have to settle for outdated tools or disconnected systems. And as agentic AI continues to evolve, it’s becoming clear that these tools can do more than save time. They can change how work feels.
