Is ServiceNow AI Truly Ready For The Market?

ServiceNow has long been a leading player in ITSM. But as Artificial Intelligence (AI) continues to reshape the ITSM industry, is ServiceNow AI living up to expectations?

While the platform has made major strides with AI capabilities, how effectively it’s meeting user needs and addressing market challenges still varies from one organization to the next.

In this blog post, we’ll explore ServiceNow’s AI capabilities, assess whether the technology is market-ready, and look into why custom AI solutions and AI models trained in house are better able to meet organization’s needs.

What Is ServiceNow AI?

ServiceNow AI refers to the suite of AI-enabled capabilities provided via the ServiceNow platform. The ITSM solution provider is deeply integrating AI into its suite of products to automate workflows, improve efficiency, and drive innovation. AI capabilities within ServiceNow can range from basic automation to more complex machine learning models that help businesses make smarter decisions. Here’s a closer look at some of the key AI features in ServiceNow:

What Are ServiceNow AI Capabilities?

ServiceNow leverages a variety of AI features across its platform to support both IT and business operations. Here are some of the key ServiceNow AI capabilities:

ServiceNow AI Agents: AI agents help automate a wide range of tasks ranging from basic (agents that operate based on predefined rules and triggers) to more complex (fully self-sufficient systems capable of carrying out tasks without any human intervention).
AI Agent Orchestrator: An AI control tower that provides a centralized solution for analyzing, managing and governing AI agents and workflows that include AI-driven decisions. This allows users to design and deploy more intelligent, scalable processes.
ServiceNow AI Search: Natural language understanding (NLU) and machine learning assisted search that aims to identify user intent and deliver always relevant results based on the context of the query and users’ past behavior.
Generative AI / Gen AI: The integration of generative AI models into ServiceNow allows the platform to generate original content, opening up new possibilities for automating tasks and driving innovation. Example uses include text summarization, prompt enhancements, and features such as text-to-code conversion.
ServiceNow Now Assist: A collection of features that leverage generative AI and domain-specific models, along with ServiceNow’s workflow automation capabilities, aimed at improving productivity and efficiency, delivering better self-service, recommending actions, providing answers and more.
ServiceNow Virtual Agent Chatbot: The ServiceNow AI chatbot aims to provide intelligent conversational experiences that support instant resolution to common requests.
AI Agent Studio: A ServiceNow application for building, managing and testing AI agents and agentic AI workflows.

While ServiceNow’s AI capabilities are impressive on paper—spanning everything from virtual agents to generative automation—not all organizations are yet seeing the promised impact in practice. The question remains: are these AI features truly ready for the market? And are enterprises truly ready to adopt them?

Are Enterprises Ready for ServiceNow AI?

As artificial intelligence rapidly evolves, organizations are under increasing pressure to adapt and leverage AI-driven solutions for enhanced productivity and smarter workflows. ServiceNow AI offers powerful capabilities, but the question remains: Are enterprises truly ready to implement and benefit from it? In this section, we explore the market’s preparedness for ServiceNow AI by examining the tools available for training and the varying levels of AI maturity across organizations.

ServiceNow AI Certification and Training

When it comes to ServiceNow AI’s market readiness, the currently available AI features and capabilities are only part of the equation. We must also consider how ready the market is for ServiceNow AI. To support this, ServiceNow provides training resources such as “Introduction to Generative AI”, aimed at promoting effective and ethical use of AI among its users.

ServiceNow AI Maturity Index

ServiceNow itself released the “Enterprise AI Maturity Index 2025” – a comprehensive framework for understanding and measuring AI maturity across organizations.

The AI Maturity Index measures the progression of AI adoption within organizations, and it reveals that businesses are at different stages in their AI journey. Some organizations are already fully equipped to integrate advanced AI solutions like agentic AI—AI that can autonomously make decisions or execute tasks without human intervention—while others are still in the nascent stages of AI exploration.

The AI Maturity Index highlights survey results speaking to the rate of AI adoption, including the adoption of agentic AI in particular:

28% of respondents are very familiar with agentic AI.
55% are somewhat familiar, showing interest but with limited experience.
43% are considering adoption of agentic AI in the next year.
33% are actively piloting or using agentic AI, with at least one fully functioning use case.

These figures illustrate a landscape where the market’s readiness for ServiceNow AI varies considerably. Some businesses are fully prepared to embrace AI and deploy it at scale, while others are more cautious, testing the waters or unsure whether the benefits justify the investment at this stage. The AI Maturity Index thus highlights that readiness is an arbitrary and two-sided issue—while some are highly ready, others need more time to build confidence in AI’s capabilities.

Is ServiceNow AI Ready for the Market?

When evaluating whether ServiceNow AI is ready for widespread enterprise adoption, it’s not just about an organization’s AI maturity or preparedness—it’s also about the quality of ServiceNow’s AI outputs. Even the most mature organizations can struggle if the AI doesn’t perform reliably.

The challenges surrounding AI accuracy and output quality are not trivial, and these concerns will impact how companies perceive ServiceNow AI readiness.

Decline in Trust in AI Output

Despite growing enthusiasm for AI, a recent study by Avanade reveals a significant decline in trust surrounding AI-generated outputs. The findings suggest that while more businesses are adopting AI solutions, many are becoming increasingly cautious about relying on them due to concerns over accuracy and consistency. According to Bhavya Kapoor, Avanade’s Asia-Pacific President, this decline in trust signals market maturity—businesses are now more aware of the limitations inherent in early-stage AI models.

As businesses enter the pilot or proof-of-concept stages with AI technologies, they often encounter unreliable outputs that require fine-tuning to achieve the desired results. This raises a crucial question: How can companies ensure their AI models are properly trained and optimized for their unique needs?

The Challenge with Pre-built AI

One of the main challenges businesses face is limited control over the training and fine-tuning of pre-built AI models. With third-party solutions like ServiceNow AI, companies typically have little influence over how AI models are trained or customized. While convenient, these out-of-the-box AI features often fail to meet the nuanced requirements of organizations, particularly when specialized tasks are involved.

Self-trained AI: A Path to Better Results

For enterprises with the resources and maturity to do so, taking full control over AI model training can offer significant benefits. While most organizations won’t develop large language models (LLMs) that rival ChatGPT or other leading platforms, they can customize AI models to meet their specific needs. For instance, summarizing case notes, suggesting relevant articles, automating incident classification, and predictive monitoring for things like SLA breaches all benefit from AI that’s fine-tuned on data unique to the business.

Why is this important? Because tasks like these require highly specific datasets that reflect the unique nature of the organization. Self-trained AI allows businesses to create solutions that are more precise and relevant than generic, third-party models. This level of customization enables organizations to align AI capabilities with business goals, driving tangible improvements in operational efficiency.

Solving Trust and Control Issues by Taking AI In-House

As skepticism around AI’s outputs continues to rise, many organizations are looking to custom AI solutions to regain control over the quality of their models and data. By bringing AI in-house, businesses can tailor models to meet their unique needs, while minimizing the risks associated with relying on generic, off-the-shelf solutions. However, taking AI in-house isn’t just about gaining control—it’s about how that control is wielded to achieve the desired outcomes.

How Are AI Pacesetters Using AI and ServiceNow?

The ServiceNow AI Maturity Index highlighted “Pacesetters” – organizations that are outperforming the rest of the study’s participants in terms of AI maturity – making up 18.2% of respondents.

Interestingly 56% of Pacesetters have made significant progress connecting data and operational silos, compared to 41% of others. This indicates that part of developing AI maturity is the ability to access the data that enables AI.

This allows Pacesetters to adopt custom AI models and integrate best-of-breed AI solutions to solve more complex problems, rather than relying solely on ServiceNow’s out-of-the-box features. For instance, AI may be used to detect security vulnerabilities, predict service outages, or enhance customer experiences in ways that ServiceNow’s native features can’t support yet.

By integrating these tailored AI solutions, these enterprises are able to extract more value from ServiceNow, enhancing both the platform and related ITSM processes with specialized features and automations that align with their specific needs. This kind of customization allows organizations to truly leverage AI to drive innovation and business efficiency.

Example Use Cases for AI in ITSM Beyond ServiceNow’s Limits

Organizations with an efficient data pipeline between ServiceNow and third-party AI-enabled solutions, or between ServiceNow and custom AI models, can leverage more advanced, impactful use cases. While ServiceNow provides a foundation of AI features, integrating best-in-class third-party AI tools or custom models can take your ITSM workflows to the next level. Below are several advanced AI use cases that go beyond what ServiceNow offers natively:

Predictive Incident Management

With an efficient data pipeline, organizations can train their own AI models on historical incident and resolution data. These models can be used to predict and prevent IT incidents before they occur, or to suggest proactive actions based on past incidents and patterns.

AI-Driven Knowledge Base Optimization

AI-powered recommendation engines can continuously refine knowledge base suggestions based on evolving data, such as past incidents or user behavior. This allows for more accurate and context-sensitive resolution suggestions, enhancing the knowledge base’s effectiveness.

Root Cause Analysis with Machine Learning

Machine learning algorithms can analyze large datasets from past incidents to uncover hidden patterns and root causes of recurring issues. This enables faster and more accurate root cause analysis, helping organizations address the source of problems rather than just their symptoms.

Automated Incident Resolution Resource Allocation

AI can allocate the most qualified technician to resolve incidents based on factors like skills, availability, and ticket complexity. By leveraging historical data, AI ensures that incidents are handled by the best-suited team members, improving resolution times and efficiency.

Sentiment Analysis for Feedback and Satisfaction

AI can analyze customer and employee feedback from multiple sources (surveys, chats, social media) to gauge sentiment and identify areas for improvement. This deeper insight into satisfaction levels can inform service improvements and drive better decision-making.

Intelligent Ticket Routing Based on Historical Data

AI models can intelligently route new tickets to the most appropriate team or technician based on historical data. This dynamic routing ensures that incidents are addressed by the right resource every time, improving efficiency and resolution times.

Proactive Trend Analysis for Service Improvement

By analyzing historical ticket and incident data, AI can identify trends that may indicate potential issues or opportunities for service improvement. This proactive analysis allows organizations to act before problems arise, rather than waiting for incidents to occur.

Advanced Incident Priority Scoring

AI can dynamically adjust the priority of incidents by analyzing a wide range of factors, including urgency, impact, and available resources. This ensures that the most critical issues are addressed first, helping organizations meet their service delivery goals more effectively.

Predictive Demand Forecasting

AI can predict future ticket volumes based on historical trends, seasonality, and other factors. By forecasting demand, organizations can better allocate resources, reduce downtime, and optimize their response to fluctuating service desk activity.

How to Overcome Data Pipeline Bottlenecks for AI Success

Taking control of AI means having the ability to tailor solutions, but this control alone doesn’t guarantee success. What businesses do with that control is what truly matters. Data quality is one of the biggest hurdles that enterprises face in the AI journey.

For AI models to generate reliable, actionable insights, they require access to high-quality, real-time data. Unfortunately, manual data entry often results in inconsistencies or incomplete datasets, which can negatively impact AI performance.

Traditional methods of data movement, such as ETL (Extract, Transform, Load) and API integrations, often struggle to keep up with the massive data volumes generated by modern enterprises. As a central hub for many business operations, ServiceNow produces vast amounts of valuable data that can be used to train AI/ML models. However, many organizations find it difficult to extract ServiceNow data at the necessary scale to feed into their AI models effectively.

Relying on legacy integration and replication solutions can lead to performance issues, with data pipelines becoming overloaded. This results in bottlenecks that slow down the movement of data and create delays, particularly in critical areas such as AI model training and other downstream processes.

To mitigate these issues, many enterprises resort to extracting large datasets during off-peak hours, but this only worsens the problem, introducing further delays, timeouts, and even incomplete data delivery that impacts decision-making and reporting accuracy.

To overcome these challenges and ensure AI success, enterprises must adopt high-throughput architectures for data integration and replication. These advanced solutions are designed to handle the ever-increasing data volumes, providing seamless, real-time data flow into AI models without disrupting daily operations. By adopting these next-gen data architectures, businesses can create the ideal conditions for AI to thrive—ensuring more accurate, efficient, and actionable results.

Building A High-Throughput ServiceNow-AI Pipeline with Perspectium

As organizations increasingly turn to AI to improve efficiency, automate workflows, and gain actionable insights, it’s clear that a robust AI pipeline is essential for success. ServiceNow’s AI capabilities offer incredible potential, but to truly unlock their power, businesses need a seamless way to move high-quality, real-time data from ServiceNow into AI models and third-party AI-enabled solutions. This is where Perspectium steps in.

Perspectium provides a comprehensive solution for building high-throughput pipelines that enable efficient data movement between ServiceNow and external solutions/AI models. By leveraging Perspectium’s advanced data replication and integration solution, enterprises can ensure that the data fed into AI models is not only accurate and up-to-date but also delivered with the speed and scale to train them efficiently.

Perspectium outperforms traditional API and ETL solutions due to its innovative approach to data replication and integration, specifically designed to handle the high data throughput demands of modern enterprises.

High-Throughput, Low Impact

One of the key technological advantages is Perspectium’s ability to bypass ServiceNow’s REST API, which is often a bottleneck in traditional data movement solutions. Instead of initiating data transfers from external systems via REST API calls, Perspectium uses more efficient, natively available push technology within ServiceNow itself. This reduces the load on ServiceNow by eliminating the impact associated with external requests, ensuring faster and more reliable data movement.

Another major differentiator is Perspectium’s use of a publish-and-subscribe architecture powered by a message bus (MBS). With this model, data is pushed directly out of ServiceNow into the message bus, and subscribing targets (whether internal systems or external solutions) can retrieve the data from there without putting any additional strain on the ServiceNow instance. This decouples the process of data ingestion from data replication, which is a critical innovation.

One of the key benefits of using a message bus is that it enhances data quality by ensuring data reliability and preventing data loss. In traditional API and ETL methods, each target system needs to make its own queries to ServiceNow to retrieve data. In high-volume environments, this can lead to redundant queries and performance bottlenecks.

With Perspectium’s message bus, data is buffered and stored temporarily within the bus before being retrieved by subscribers, ensuring that all data is reliably delivered even if one or more subscribers experience outages.

Rather than the transfer failing outright and risking data loss, the data simply waits, encrypted at rest in the MBS’ queue until the target system is back online. In high-volume data environments, this mechanism is crucial for maintaining data integrity and preventing errors that could disrupt downstream AI/ML models, reporting, or analytics.

Created by ServiceNow’s Founding Developer, For ServiceNow Power Users

Additionally, Perspectium was built by the founding developer of ServiceNow, who leveraged his deep understanding of ServiceNow’s architecture to create a solution that perfectly aligns with the platform’s core capabilities.

This intimate knowledge of ServiceNow allows Perspectium to optimize data replication in ways that traditional methods cannot. In fact, ServiceNow itself relies on Perspectium to manage the high-volume data pipeline between multiple ServiceNow instances and cloud repositories.

ServiceNow adopted Perspectium after realizing that traditional API and ETL methods were insufficient to meet their throughput demands and were causing unacceptable performance degradation on the platform. By using Perspectium, organizations can replicate data at the scale and speed necessary for AI models and large-scale integrations without sacrificing system performance or data accuracy.

With this architecture, Perspectium enables businesses to keep their AI models fed with real-time, high-quality data—without the usual slowdowns or workarounds associated with traditional data integration methods. This not only enhances AI accuracy but also ensures that organizations can scale their operations while maintaining seamless, uninterrupted access to critical data.

If you’re ready to take your ServiceNow AI capabilities to the next level, Perspectium is the perfect partner to help you build an AI pipeline that’s fast, reliable, and tailored to your unique business needs.