How to Get Started with ServiceNow AI Agents and AI Control Tower

AI is not a typical software project. It is an experimental journey that only works when you pick the right business problem, have the right data, and build trust in the output. When you bring that mindset to ServiceNow AI Agents and ServiceNow AI Control Tower, you get something powerful: automation that people actually use, and governance that keeps the whole program accountable.

Think about it like using Google Maps. When you enter a destination, you don’t want 20 options and a wall of data. Instead, you want 1-3 clear routes, based on traffic, rules, and millions of prior journeys. You trust it because it learns from real world inputs and gets better over time. ServiceNow can work the same way for service and operations when AI is set up to be prescriptive, not noisy.‍

What ServiceNow AI Agents Do Well

ServiceNow AI Agents deliver the most value when you start with tractable use cases. A tractable use case has real teeth. You can measure it, the data exists, and the outcome matters because they are actionable. In field service, that might be an agent that triages incoming sensor alerts, checks warranty and parts availability, opens a work order, and schedules the right technician. In customer service, it could summarize cases, suggest next actions, and push the right workflow without forcing a user to click through five tools.

The key is trust. Field engineers and service agents will follow an AI recommendation when they have seen it work. We build that trust by backtesting and by starting small. For example, take five years of vibration data from a critical device. Train a model. Ask a simple question. Could the system have warned us weeks earlier? How often would that have been correct? When the answer is yes, the team leans in. At the end of the day transparency matters. Showing why an AI Agent made a recommendation (not just the recommendation itself) helps build confidence and trust.

What ServiceNow AI Control Tower Does

ServiceNow AI Control Tower gives you the operating model you need to scale responsibly. It connects AI strategy to business services, tracks performance and risk, and enforces governance across any AI investments, including ServiceNow native features, third party models, and agentic workflows.

In practice, Control Tower provides:

• Clarity: one view of which AI projects maps to which services, and the value they are delivering
• Accountability: ownership, approvals, and controls built in, so pilots don’t drift without governance
• Transparency: visibility into risks, adoption, and performance, helping leaders avoid wasted spend on compliance surprises

Most AI programs fail not because the models don’t work, but because adoption and governance are missing. Control Tower helps you avoid that trap turning scattered pilots into a sustainable AI portfolio.

Start with Outcomes, Not Platforms

A classic mistake is treating AI like a standard app build. Configure forms, integrate systems, test, deploy, call it done. AI work is different. Expect iteration. Expect to change your framing when the data tells you to. Expect some ideas to stall because the signal to noise ratio is not there yet.

I recommend starting with one small, high value problem. Prove that the data supports the outcome. Put an AI Agent in the loop with clear guardrails. Measure the impact. Scale the pattern to the next device, the next product line, or the next region.

The upside of AI in service is huge. Lower downtime, higher customer satisfaction and faster resolutions. The downside is wasted spend or risk if you scale the wrong projects. That’s why governance is critical.

Companies that build these AI muscles now will be better positioned when more advanced, multimodal models arrive, and they are arriving quickly! ServiceNow gives you a framework to adopt responsibility today and scale tomorrow.

Field Service and IoT, The Practical Path

Preventive maintenance is a natural fit for AI in field service management. Many schedules today fire on cycles or counters. That is useful, but it is still reactive. When you layer IoT signals with AI, you can spot subtle patterns early and create work orders before an equipment failure. Small wins matter here. One avoided outage can save tens of thousands of dollars an hour in downtime, sometimes more. The other win is planning time. If you can see a likely failure weeks ahead, you can order parts, line up the window, and avoid chaos.

Two practical notes from my time in the field:

1. Make sure your sensors provide the right resolution. Sometimes the first step is upgrading hardware or telemetry.
2. Watch compute and model costs. The best architecture balances accuracy and cost across your actual volumes.

Why ServiceNow Helps You Scale

ServiceNow’s architecture and unified data model are built for enterprise workflows. That matters when you bring AI into the mix. You need the platform to ingest events, correlate them with services and assets, and trigger the right workflow every time. You also need clear ownership. With AI Control Tower, you get one view of AI investments, value, adoption, and risk, tied to the services your business runs on.

The Real Work? Change Management

If you don’t bring the organization along, AI will look like a neat proof of concept and then stall. Success comes from change management as much as technology.

That means:

• Building trust: show why an AI Agent makes a recommendation, not just what is suggested.  Front line staff adopt AI faster when they see the logic behind the recommendations.
• Addressing fears: be upfront that AI is there to augment, not replace, human experience and expertise. The best outcomes happen when AI and people work together. AI handling repetitive analysis and pattern recognition, and humans applying judgement, empathy, and creativity. They should be viewed as complementary, not as adversaries.
• Creating Champions:  Identify change agents in each region or business unit who want to lead and can share success stories and drive improvements.
• Treating adoption as a product:  Launch it, market it internally, set up feedback loops, and continue iterating.

Budget time for training and support. Even the best model won’t stick if users don’t feel confident and heard.   

A Crawl, Walk, Run Roadmap

Crawl

• Pick one tractable use case with clear value. 
• Confirm the data pipeline. 
• Backtest to build trust. 
• Launch an AI Agent with humans in the loop. 
• Measure time saved, SLA impact, or avoided downtime.

‍
‍Walk

• Use AI Control Tower to standardize intake and governance. 
• Add a second use case or scale the first to a new region. 
• Introduce automated handoffs to Field Service Management or Customer Service Management. 
• Track adoption, accuracy, and value in one dashboard.

Run

• Expand the agentic pattern set. 
• Link AI outcomes to contract terms, spares forecasting, and customer promises. 
• Use Control Tower to prioritize investment, show ROI by service, and manage risk across the portfolio.

Here to Support You

At Bolt Data, we pair ServiceNow AI Agents with real operational context. We wire up the data, validate the use case, and stand up the Control Tower so your leadership can steer. We come from service and field service, so our scorecard is simple: less downtime, faster resolution, better customer experience, and programs that scale.

Setup time with our team to learn how we can help you evaluate your current AI initiatives or help you get started with one tractable use case with clear value.