How to Unlock the Full Potential of AI Service Management with Data Replication

AI Service Management (AISM) is the process of integrating artificial intelligence (AI) technologies into service management processes to improve efficiency, automate routine tasks, and enhance decision-making in IT and business operations. 

Use cases including AI-driven automation, intelligent chatbots, and predictive analytics allow organizations to improve service efficiency and enhance employee and user experiences. 

• Related post: How AI for ITSM Will Revolutionize the Workplace (and How It Won’t)

However, despite its promise, fully realizing the potential of AISM presents challenges. 

A recent study, The State of AI in ITSM – 2024 and Beyond, highlights that while 71% of organizations are actively exploring AI in ITSM, 8% aren’t even considering AISM, and only 4% have achieved complete integration. 

The hurdles, often rooted in data-related complexities such as data quality, mapping and processing, underscore the importance of robust data replication solutions that can streamline the distribution of data and enable AI scenarios. 

ServiceNow and AI Service Management

ServiceNow is doubling down on AI, working towards realizing its vision of becoming an AI-powered “platform of platforms”. 

To achieve this vision, ServiceNow wants to provide a wide range of AI-enabled features that support a broad range of use cases on the platform, to keep users (and their data) within the platform.

While this suits ServiceNow, it is limiting for the many organizations with ambitions for AI reaching beyond the ServiceNow platform. We expand on these limitations later.

ServiceNow’s AI Capabilities

2024’s Xandu release added a number of AI and GenAI capabilities to the platform, and ServiceNow’s existing AI capabilities now include:

Now Assist: An embedded suite of AI capabilities within the Now Platform designed to enhance productivity and efficiency by offering tailored GenAI experiences directly integrated into ServiceNow products. Now Assist supports administrators, developers, agents, and employees with targeted use cases.

Now Assist Skill Kit: Frameworks and tools that allow developers to build, test, configure, and publish new GenAI skills and prompts for platform-wide deployment. The capabilities help improve employee and customer experiences while boosting agent and developer productivity.

Agentic AI: Create and deploy tailored fleets of AI agents to support multiple use cases. Agents can be customized using the Now Assist Skill Kit to meet specific needs, creating highly personalized and efficient support systems.

Data visualization generation: Users can leverage advanced AI/ML models to transform massive volumes of complex data into intuitive, visually rich representations, facilitating better insights and decision-making.

Chat and email reply generation: Automatically generate context-aware response recommendations for chat and email interactions. This improves customer satisfaction by speeding up resolution times and ensuring accurate, personalized communication.

Integrations with Slack/MS Co-Pilot: An integration allowing users to interact with the platform via conversation tools. Users can perform various actions such as requesting catalog items, searching knowledge bases, or engaging with virtual and live agents directly within these platforms.

Summarizations for knowledge articles, changes, incidents, cases, and problems: Automatically generate concise summaries for knowledge articles, changes, incidents, and cases. Employees can use these summaries to quickly understand the impact of changes and assist users in grasping the measures taken to resolve issues.

LLM-based prompts: These prompts leverage large language models to deliver GenAI-enabled conversational prompts, such as reminders to complete tasks, enhancing collaboration and productivity.

There’s More to AI Service Management Than What ServiceNow Can Offer

For some organizations, ServiceNow’s vision of delivering an AI-enabled “platform of platforms” provides some advantages. It allows them to streamline the implementation and use of enterprise solutions and utilize AI capabilities without building or training their own AI models.

However, many organizations have and actively utilize other enterprise solutions. Often, these solutions are designed specifically for functions that ServiceNow also provides. And often, such purpose-built solutions are more fully-featured and robust than the ServiceNow alternative. 

For such organizations, the platform becomes a data silo that prevents them from feeding ServiceNow data within specialist solutions that are more full-featured, capable of serving specific use cases, and able to incorporate areas of the enterprise that ServiceNow does not cover. 

Integrating, Replicating and Extracting ServiceNow Data for AI

To break down the data silo and realise the full potential of AI and ServiceNow, organizations need a means of extracting ServiceNow data and replicating or integrating it with third-party systems. 

This allows organizations to tap into AI use cases that exist beyond the reach of the ServiceNow platform including:

Utilizing in-built AI capabilities in third-party solutions

ServiceNow is by no means alone in its investment into AI. Many enterprise solutions are now rolling out AI capabilities tailored to the operations they support. 

Generally, solution providers will have more scope to refine AI capabilities for a specific operation if their solution is purpose-built for that operation. 

For example, Power BI and/or Tableau users will have a broader set of AI capabilities for reporting, analytics and data visualization available, than users of ServiceNow.

With an efficient means of integrating, replicating and extracting ServiceNow data, organizations are free to feed ServiceNow data into a wide range of purpose-built, AI-enabled third-party solutions.

Using ServiceNow data to train AI models

As well as utilizing ServiceNow data within AI-enabled, third-party solutions organizations can also replicate ServiceNow data externally to train their own AI models.

ServiceNow data can be an excellent source for training AI models, given its rich repository of structured and unstructured information related to IT and business processes. 

• Related post: Using ServiceNow Backups to Train AI

By replicating ServiceNow data externally, organizations can use it to train AI models for:

• Predicting incident resolution time: Predict the expected time to resolve an incident based on historical trends, enabling better SLA planning.
• Automating ticket categorization and routing: Automatically classify tickets and route them to the correct teams, reducing manual effort.
• Identify the root cause of recurring incidents: Highlight recurring patterns and anomalies to pinpoint root causes.
• Assess the impact of a proposed change: Predict the likelihood of success for a proposed change and identify potential risks.
• Predict SLA violations before they happen: Identify tickets at risk of SLA breaches and prioritize actions to avoid penalties.
• Predictive Maintenance: Predict when equipment is likely to fail, enabling proactive maintenance.
• Capacity Planning: Predict resource needs for service desk teams and optimize staffing levels by anticipating future ticket volumes.
• Customer Support Escalation Prediction: Proactively flag tickets likely to escalate and alert managers.
• Service Outage Prediction: Predict potential service outages based on historical data and mitigate outages by identifying early warning signs.

This is by no means an exhaustive list of use cases where ServiceNow data can be used to train AI models. Furthermore, additional use cases will become apparent as organizations’ AI initiatives mature.

However, the list clearly indicates the value of replicating ServiceNow data externally, and allowing for such use cases to be pursued. 

How to Expand AI in ITSM Beyond ServiceNow

Organizations that can efficiently extract, replicate and integrate ServiceNow data are better placed to benefit from AI in ITSM. 

But the question is how? 

Point-to-Point vs. Publish/Subscribe?

In terms of integrating and replicating ServiceNow data for use in AI, organizations have two primary options in point-to-point, and publish/subscribe integrations.

In the point-to-point model, ServiceNow integrates directly with target solutions. Data transfers are initiated by API calls from the target that are processed by ServiceNow.

While this initially provides a degree of simplicity, there are significant limitations in terms of scalability:

• Throughput: As the volume of data and/or number of systems increases, the more competition there is for ServiceNow’s operational bandwidth causing performance bottlenecks. 
• Maintenance Overhead: Integrations become harder to maintain and debug as more systems are added.
• Flexibility: Not suitable for environments where new systems need to be added frequently.

In contrast, the publish and subscribe model introduces a message broker between the source (ServiceNow) and the target(s). This means ServiceNow data is replicated (published) within the message broker, from which target systems retrieve data. 

This circumvents scalability issues by limiting the operational strain on ServiceNow, and offloading the burden of distributing data to the message broker. 

With the publish and subscribe model, organizations experience benefits in:

• Throughput: Transfer large volumes of data quickly, without degrading performance.
• Scalability: Distribute large data volumes among various systems simultaneously without impacting ServiceNow performance.
• Software consolidation: Limit integration sprawl by removing the need to maintain several integrations simultaneously.
• Development and maintenance savings: With fewer systems to maintain, organizations can make better use of development resources.

Unlock the Full Potential of AI Service Management with Data Replication from Perspectium

To truly unlock the full potential of AI Service Management, organizations must look beyond the ServiceNow platform, and the limitations of traditional, point-to-point data integration methods. 

An effective AISM strategy requires the ability to leverage data from multiple sources—both within and outside of the ServiceNow platform. This is where robust data replication solutions like Perspectium become essential.

Perspectium offers a powerful and scalable publish-and-subscribe solution that simplifies the replication and integration of ServiceNow data with other enterprise systems, ensuring that valuable data can flow seamlessly across platforms. 

By leveraging Perspectium’s data replication capabilities, organizations can easily integrate ServiceNow data with third-party AI tools, utilize advanced AI and ML models for insights, and train custom AI models based on rich historical data.

Perspectium differs from traditional point-to-point integrations. These integrations degrade performance, limit data replication speeds, and become increasingly difficult to manage as the number of systems grows. 

Instead, the Perspectium approach optimizes data throughput and performance while reducing the complexity and overhead of maintaining multiple integrations. 

In summary, Perspectium is the most appropriate ServiceNow data replication solution for enabling AI Service Management.

It puts ServiceNow’s end-users in control of the platform’s data and addresses the critical need for efficient, scalable, and low-maintenance data integration. This allows organizations to expand their AI capabilities and create more intelligent, automated, and effective service management processes.

If you are in search of a native data replication solution that gives you greater control over your ServiceNow data, get in contact us today!