The Benefits of AI in ITSM (and the Challenges)

Many organizations are quickly recognising the potential of AI in ITSM. 

ITSM solutions like ServiceNow are a rich source of data and much of that data can be used to optimize processes related to ITSM.

Table of contents:

• AI in ITSM: Top Use Cases 
• Why is AI Adoption in ITSM Gaining Traction? – The Top 3 Benefits of AI in ITSM
• Challenges of AI in ITSM
• Integration Solutions for AI in ITSM

The ITSM providers themselves understand this, and are increasingly offering AI capabilities through their platforms.

However, due to the broad focus of organizations like ServiceNow, the uses for such examples of AI are limited. As such, organizations seeking to take full advantage of AI in ITSM are using purpose-built, third-party AI solutions.

In this post, we highlight the use cases for and benefits of AI in ITSM, and the challenges associated with implementing and using AI in ITSM effectively.

AI in ITSM: Top Use Cases 

There are many potential use cases for AI in ITSM. Below, lists some examples of how AI is already impacting the ITSM space:

Service Desk

AI can augment various service desk tasks, such as autonomously handling ticket routing, resolving common issues, and responding to basic queries (via chatbots).

Automating these mundane tasks reduces the in-house IT team’s workload, allowing them to focus on addressing more complex problems that demand critical thinking and human input.

Incident Management

AI solutions can classify incidents based on predefined criteria, automatically route tickets, and resolve certain incidents with minimal human intervention.

By analyzing large volumes of historical data, AI tools can identify patterns and accurately predict future incidents. This improves Mean Time to Recover (MTTR) and minimizes service disruption.

Problem Management

AI solutions specialize in analyzing large datasets to detect probable patterns. This can be applied in ITSM to analyze IT incident data to identify underlying issues, and recommend optimal solutions.

By analyzing and identifying trends in historical data, AI can also do things like predict spikes in new cases, helping leadership plan resource allocation accordingly and proactively mitigate issues.

Change Management

AI algorithms analyze data to understand the potential business impact of specific implementations/changes and recommend value-driven solutions.

By automating change management processes (such as testing and deployment), AI can support change management at a greater scale, reducing error risks, accelerating implementation speed, and ensuring smoother transitions.

Asset Management

Through advanced data analysis, AI algorithms can streamline asset management and identify key opportunities to optimize asset usage, reducing dependence on internal teams.

IT personnel can use the insights to improve resource allocation across teams/projects and implement strategic plans to maximize productivity and cost-efficiency. 

Knowledge Management

Organizations can use generative AI solutions to create reliable, high-quality documentation based on existing data and knowledge repositories.

This could encompass FAQs, procedural documentation, knowledge articles, case resolution summaries and other resources that promote self-service, allowing end-users to resolve technical issues independently.

For instance, AI algorithms can analyze a user’s query and track the knowledge database to deliver the most relevant knowledge articles to match that query, improving UX.

Enhanced Security and Compliance

By automating security and compliance workflows within enterprise service management systems, AI tools can supplement security and compliance efforts.

They can effectively monitor and enforce security regulations autonomously, providing alerts for critical issues.

By continuously analyzing potential security threats, AI solutions enable IT teams to operate proactively, build intelligent security systems and implement advanced security protocols to protect sensitive business data. 

Why is AI Adoption in ITSM Gaining Traction? – The Top 3 Benefits of AI in ITSM

Utilizing AI in ITSM can benefit organizations in a number of ways including:

• Supporting limited in-house resources
• Enhancing employee and end-user experience
• Enabling analytics-driven performance improvement

Supporting limited in-house resources

AI solutions significantly reduce the burden on ISTM staff by implementing automated workflows for routine tasks and ticket-handling processes.

Self-service options (like knowledge-based workflows) and chatbots accelerate issue resolution for end-users, boosting customer satisfaction and increasing engagement. 

By improving and supporting multiple functions across ITSM, AI contributes to better time-to-resolution and ticket volumes, increased productivity and more.

Enhancing employee and end-user experience

By quickly processing and analyzing large datasets, AI tools allow support personnel to deliver highly accurate responses to service requests, tailor-made to fit the user’s specific needs and historical patterns.

This means employees are better supported in completing their work and providing high levels of service. 

In turn, customers also benefit from employees that are better able to serve them. Additionally, examples of AI in ITSM such as intelligent knowledge management helps customers resolve their issues quickly, and independently by suggesting possible resolutions to issues without requiring human input. 

Analytics-driven performance improvement

AI-driven analytics can provide actionable insights into critical ITSM issues to improve operational efficiency.

AI algorithms can identify difficult-to-handle tickets, recurring issues, and knowledge gaps. Relevant stakeholders can use these insights to gain a holistic view of ITSM health and take suitable measures to improve IT service delivery and operational resilience.

Challenges of AI in ITSM

Despite the several benefits, AI adoption in ITSM comes with a set of challenges, both technical and practical. 

Technical Challenges

Data quality and data volume

AI systems need large volumes of credible, high-quality data to function optimally and deliver accurate results.

While many organization’s ITSM solutions produce and collect the volumes of data required to effectively influence AI solutions, they don’t always have an efficient means of accurately replicating the required data into third-party AI solutions.

As such, choosing the right integration solution to support high data volumes and the throughput required to transfer large datasets, without affecting the ITSM solution’s performance is vital. This rules out many API-based integration solutions.

Data security

When dealing with large data volumes, organizations must implement strict security measures and incident management procedures to safeguard business-critical information against cyberattacks and misuse.

Data security is of particular concern when transferring data from an ITSM solution into a third-party AI solution.

Organizations need the right tools and security features/protocols to ensure the risk of data exposure is mitigated when data is in-transit or at-rest in the new solution.  

Practical Challenges

Lack of expertise

AI adoption within ITSM is often hindered by the scarcity of AI/ML expertise. Lack of proper training, industry knowledge, and practical implementation skills make it difficult for IT staff to leverage AI technologies for optimal results.

Ethical concerns

Establishing clear guidelines and ethical frameworks is crucial to mitigating potential issues that could hamper operations and market reputation. IT teams must carefully assess ethical considerations related to AI, including bias, privacy, and transparency.

User acceptance

AI algorithms are akin to “black boxes”—they deliver results without clear explanations. This lack of transparency often directly correlates to the resistance to AI adoption across IT projects/use cases involving sensitive data. 

Continuous monitoring & improvement

Since AI models thrive on continuous monitoring and refinement over time, organizations must actively invest in ongoing training to empower employees with the right knowledge and skills necessary for successful AI implementation.

Integration Solutions for AI in ITSM

Data replication and integration solutions can help organizations feed ITSM data into AI solutions so the data can be used to support AI in ITSM use cases.

The right integration solution will help organizations overcome the technical challenges of implementing AI in ITSM initiatives.

Features to look out for include:

• High-throughput data transfers to deal with the high volumes of data required
• Minimal impact on the performance of the ITSM solution
• Data transformation capabilities to ensure data arrives in a usable format
• Secure data transfers and suitable levels of encryption

One such solution supporting all of the above, is Perspectium DataSync. 

Perspectium is a data replication solution and service provider, created specifically for the leading ITSM platform, ServiceNow. 

With Perspectium DataSync, organizations have an integration-as-a-service solution that can replicate over 20 million records per day to various external targets, allowing ITSM data to be used in AI, among other initiatives. 

And even when handling throughput as high as 20 million records per day, Perspectium does not degrade ServiceNow’s performance.

This means data transfer speeds out of the platform and query speeds within the platform remain high, and employees using ServiceNow and/or ServiceNow’s data can work without integration-related disruption. 

This puts DataSync in contrast with many API-based integration solutions, where external API calls cause and suffer from performance issues when requesting and retrieving large datasets.

Perspectium’s API-free technology avoids this performance degradation by operating internally, within ServiceNow and using efficient Push-technology to initiate data transfers. 

DataSync also provides security capabilities such as encryption and obfuscation to keep data secure both at-rest and in-transit. 

Want to learn more about replicating ITSM data in AI solutions? Click here.