How AI Enables ITSM, ESM, and the Delivered Employee Experience

It isn’t enough to know that artificial intelligence (AI) can help your organization’s IT service management (ITSM) operations and outcomes. It’s a little like wanting to adopt something as “a good thing to do” rather than ensuring that the something is “the right thing to do.” To help, this blog outlines how AI can already be used to improve ITSM and enterprise service management (ESM) operations and outcomes, starting with the benefits.

The high-level benefits of AI adoption in ITSM and ESM

When considering the benefits of AI, it’s important to understand that it helps with both insights and actions, meaning that the generic benefits of traditional automation also apply to what might be considered “intelligent automation.” In many ways, AI amplifies the benefits of traditional automation across the triumvirate of “better, faster, cheaper, ” including better data-informed insights and superior experiences across ITSM and ESM use case scenarios.

Example ITSM and ESM benefits include:

• Operational efficiency gains – where the technology reduces IT personnel workloads, freeing people up to work on higher value-add activities. The increased speed also positively impacts employee experiences and lost productivity.
• Improved employee experiences – faster operations and AI-enabled capabilities such as chatbots and virtual assistants improve employee experiences across the different service providers.
• Reduced operational disruption – where speedier issue resolution, preventive problem management and maintenance, self-healing (for IT), and reduced human error all contribute. This benefit, again, positively impacts employee experiences and their level of lost productivity.
• Cost reduction – where automation, more efficient resource utilization, proactive problem management, and improvement initiatives all contribute to cost savings.
• Data-informed decision-making – this includes strategic decision-making, capacity planning, demand forecasting, and improvement identification. Better decisions can positively impact all of the above benefits, including the employee experience.
• The ease of scalability – AI-enabled capabilities help organizations to grow, cope with skill shortages, and better adapt to internally and externally driven change.
• Improved perceptions of the service provider’s operations and outcomes – based on the cumulative effect of the aforementioned benefits.

The common ITSM and ESM AI use cases

AI is already being used in ITSM tools to improve service provider operations and outcomes, speeding up activities and removing the reliance on manual effort (and its limitations). Examples of ITSM and ESM AI use cases include:

• Process automation – which speeds up operations, improves experiences, and frees up personnel to focus on other tasks and issues. For instance, incident ticket categorization and routing, accurately classifying (or categorizing) incidents and routing them. This IT example also applies to other corporate service providers such as Human Resources (HR), Facilities, or any other business function that handles employee or third-party requests for help, information, service, or change. This AI-enabled process automation can also include automated provisioning or issue resolution.
• Virtual assistants and chatbots – which help both service provider staff and the people they serve. For example, providing knowledge in context, answering common questions, guiding people through troubleshooting steps, or automating routine tasks. Again speeding up operations, improving experiences, and freeing up service provider personnel.
• Knowledge management improvements – which include the analysis of past incidents and solutions to create new knowledge articles, new article suggestions to fill knowledge gaps, and pushing knowledge to people when needed. This AI enablement improves the service provider’s knowledge management capabilities, operations, and associated employee experiences.
• Experience personalization – for example, AI can personalize the content and layout of self-service portals based on employee preferences and behavior data, improving the employee experience.
• Advanced analytics capabilities – that improve service delivery and the associated outcomes and experiences, such as:
• Predictive analytics – where serious IT issues are predicted, using historical data and patterns, and addressed before adversely impacting business operations.
• Root cause analysis – where data sources such as performance metrics, configuration data, and log files are used to identify the root causes of problems.
• Demand forecasting – where historical patterns and trends are used to predict IT demand.
• Continual improvement – where analyzing patterns, trends, and correlations in ITSM data can highlight issues and opportunities.
• Risk analysis – for example, with change enablement, AI can analyze historical change data to predict the risks associated with a proposed change (and the expected impact of a change).
• Asset optimization – analyzing asset usage patterns to recommend revised IT asset allocation and cost reductions.
• Sentiment analysis – to gauge customer satisfaction and improve services in the right places.

The impact of generative AI technologies on ITSM and ESM

The above list covers many of the AI-enabled capabilities already seen in ITSM tools. But there is a new kid on the AI block with the sudden rise in interest in ChatGPT and other generative AI tools. The opportunities of generative AI for ITSM and ESM will be covered in future SysAid blog posts, including the pros and cons of its use.

If you have any questions regarding the currently available AI-enabled ITSM and ESM capabilities or the ChatGPT “gold rush” the IT industry is experiencing, please let me know in the comments section below.