Artificial Intelligence at Service Now

ServiceNow is an American enterprise software program firm headquartered in Santa Clara, California. The firm employs greater than 29,000 folks globally and reported fourth-quarter 2025 subscription income of $3.57 billion, with fiscal 2026 subscription income steerage of $15.53 billion to $15.57 billion.​

The firm has additionally invested closely in AI and automation to enhance workflow effectivity and enterprise productiveness. ServiceNow acquired Passage AI to strengthen conversational AI capabilities, expanded its partnership with NVIDIA to help autonomous AI brokers, and committed $1 billion by way of its enterprise arm to again enterprise software program and AI-related startups.

ServiceNow has additionally made a CA$110 million investment to help AI adoption in Canada’s public sector, together with infrastructure and an AI Center of Excellence.​

The firm is actively utilizing its personal AI platform (Now on Now) to attain vital ROI, demonstrating how C-suites can transfer past AI experimentation into tangible, scalable automation.

Here are two key AI use circumstances, leveraging Now Assist (GenAI), that enterprise leaders can be taught from:

• Reducing agent documentation time with embedded generative AI: Leveraging generative AI inside current ITSM (IT Service Management) and CSM (Customer Service Management) workflows to automate summaries, decision notes, and data article creation, serving to brokers save time and deal with higher-value help work.
• Predicting buyer escalations earlier than they occur: Using machine studying and real-time occasion monitoring to determine at-risk accounts early, automate proactive outreach, and scale back expensive buyer escalations.

Reducing Agent Documentation Time With Embedded Generative AI

​A San Jose State University analysis paper, “Empowering customer support with generative AI”, documents that customer support brokers spend 35-45% of their time on repetitive documentation, creating $2.6 billion in annual U.S. labor inefficiency throughout enterprises.​

A Harvard Business School study on specialization in repetitive work equally finds that service brokers lose vital time on documentation and summarization, diverting focus from high-value problem-solving.​

To remedy this, ServiceNow launched Now Assist for ITSM and CSM, embedding generative AI immediately into agent workspaces per the corporate’s official ITSM Now Assist documentation and CSM Now Assist information. Instead of standalone chatbots, it automates summarization of incident histories, drafting decision notes, and producing data articles inside current workflows.

Using machine studying and generative AI, the corporate automated the mundane elements of help circumstances, corresponding to summarizing lengthy incident histories, drafting decision notes, and producing data base articles.

ServiceNow reports: in Now Assist, LLMs ingest case context, generate editable summaries/notes in seconds,, and supply agent opinions in file time, so there isn’t any want for context-switching to exterior instruments.

​

​Screen of chat displaying dialog between the IT agent and Now Assist (Source: ServiceNow)​

In the identical whitepaper, the corporate shared that Now Assist generates notes inside seconds, permitting brokers to overview and refine them versus creating them from scratch. This diminished the time wanted for every decision word by roughly 80%.

​ServiceNow additionally shared that, on common, ITSM brokers save 4-6 minutes per use, and CSM brokers save 12-16 minutes per use. This proves that enterprise AI worth comes from embedding generative AI into current workflows, not standalone demos.

Predicting Customer Escalations Before They Happen

Historical monitoring relies closely on handbook checks of tickets and occasions, making it exhausting to identify deteriorating experiences earlier than clients escalate. Without a scalable method to predict which accounts are at threat, proactive outreach stays inconsistent and infrequently too late.​

ServiceNow turned a basic reactive help mannequin on its head by utilizing machine studying to foretell and stop buyer escalations.  According to a case examine published by ServiceNow, as an alternative of ready for patrons to complain or threaten to escalate, the staff now leans on ServiceNow’s personal Predictive Intelligence and Event Management capabilities to proactively determine at‑threat accounts and attain out earlier than points snowball. ​

The initiative is built on ServiceNow’s Predictive Intelligence framework, which hosts the underlying machine‑studying mannequin, and Event Management for actual‑time ingestion of efficiency‑associated occasions.

Within Predictive Intelligence, a supervised mannequin skilled on historic escalation patterns analyzes tickets, surveys, CSAT scores, and engagement alerts. Event Management provides actual‑time system alerts.

How the workflow operates:

• Builds and trains the mannequin: Historical escalations and correlated occasions are remodeled into structured options, that are then used to coach and validate an XGBoost classifier by way of a PoC/PoV section.
• Deploys real-time threat scoring: Once dwell, the mannequin constantly scores clients as new tickets and occasions arrive, assigning escalation-risk labels corresponding to low, medium, or excessive.
• Automates proactive intervention: When a buyer strikes into the high-risk class, ServiceNow workflows routinely generate precedence alerts, assign follow-up duties to help or account groups, and floor beneficial playbooks with subsequent steps.

Screenshot displaying the advantages of this answer (Source: ServiceNow)

​

Over time, the end result of every engagement, whether or not the escalation was averted or not, feeds again into the mannequin, constantly refining its predictions. ​

It additionally lays out the enterprise outcomes as follows:​

Timelier interventions have led to sooner response and backbone instances, greater buyer satisfaction scores, and smoother renewals and upsells, as at‑threat accounts are stabilized earlier than they turn into vocal.

Before the mannequin, solely about 11% of buyer engagements had been proactive; after implementation, roughly 68% of engagements grew to become proactive, enabling earlier and extra systematic outreach to at‑threat clients.

The system helped interact lots of of shoppers per yr, stopping a big share of escalations whereas protecting the false‑constructive charge round 3%, so engineering assets usually are not wasted on pointless circumstances.