data monitoring

With the potential to decrease ticket volumes and enhance service quality, AI-driven automated solutions offer a promising avenue for managing IT workloads more effectively.

Leveraging artificial intelligence (AI) and automation in IT service management (ITSM) can alleviate the growing burden faced by IT professionals by helping resolve routine tasks and boost productivity.

AI and automation technologies can help ITSM teams by performing activities they would rather not do, including handling L1 tickets and manually categorizing and assigning other tickets.

These technologies can also assist them with everyday ITSM activities by providing predictive intelligence, insights and summaries by scanning large volumes of historic ticket information.

“These applications, for example AI-powered virtual support agents that can handle L1 support, ultimately will help reduce ITSM teams’ workloads significantly,” said Ashwin Ram Ragupathi, ITSM evangelist for ManageEngine.

Acting as the first line of contact, these virtual agents can collect necessary information from the end user, understand the context and provide access to knowledge articles or suggest a recommended course of action so end users can self-resolve issues.

“One other important task that can be automated using AI-driven solutions is knowledge generation, where solution articles can be automatically created based on the solutions applied to similar, historical incidents, taking into account their success rates,” Ragupathi said.

AI and Automation Have Multiple Applications

Scott Wheeler, cloud practice lead at Asperitas, said automation also has applications in compliance and reporting.

“Routine audits for compliance with internal policies and external regulations can be automated to ensure continuous adherence without manual checks,” he explained.

Regular operational reports such as performance analysis, incident logs and compliance status can be automated, providing insights without manual effort.

AI can also be used to track and manage IT assets across their lifecycle, from procurement to disposal, including updates on status changes, location tracking and maintenance schedules.

To integrate automation and AI into existing ITSM tools, Wheeler advised organizations to make a thorough assessment of current ITSM processes and tools.

“Identify areas where processes are manual, time-consuming, or prone to errors,” he said. “These areas are prime candidates for automation.”

He recommended clearly defining what the aims are regarding AI and automation, such as reducing incident response times, improving customer satisfaction or optimizing resource allocation.

The Power of Predictive Intelligence

With predictive intelligence capabilities, ITSM platforms can now categorize, prioritize, route and assign incoming tickets based on past performance much more efficiently than earlier rule-based automation.

The latest enhancements in AI technologies and their applications can even help provide recommended resolutions automatically based on context and historic patterns.

Ivanti CPO, Srinivas Mukkamala, explained predictive analytics can forecast potential system failures, security threats, performance bottlenecks and more.

“This allows for IT teams to proactively address these issues,” he said.

Furthermore, predictive analytics examine historical data to identify patterns, helping in optimizing resource allocation and improving system performance.

“As a result of this trend, operational efficiency is enhanced and downtime is minimized, directly contributing to IT operations productivity and cost reduction,” he said.

Prince Kohli, CTO of Automation Anywhere, explained AI-powered automation has been made possible by advances in the AI fields of text and image recognition and the creation and usage of high-quality machine learning and foundational models that power generative AI.

When coupled together, these advances allow a digital worker to “read” and “understand” their environment, which in turn allows them to act on it.

They can then perform complex activities such as dynamically optimizing workloads, allocating and de-allocating resources more efficiently, and proactively identifying and resolving issues before they escalate.

“They are essential in any domain where there is a large amount of data or a need for a real-time response,” Kohli said.

Furthermore, it allows IT professionals to use natural language to query or create rules and to obtain information, thus reducing the need to communicate in specific configuration mechanisms or programming languages.

“This, in turn, reduces errors in operations,” he said. “The automation of repetitive and common tasks save valuable time and free up humans, enhancing workload management and ultimately increasing job satisfaction and reducing burnout for IT teams.

Kohli noted Automation Anywhere uses AI-powered automations to look for malicious patterns in data traffic and act upon them in real time.

“As one can imagine, both the scale of data and the need to act quickly can only be served by an automated solution,” he said. “The best metrics measure impact and other outcomes for goals that are strategic to the team.”

To gauge performance, Kohli recommended starting with well-defined traditional metrics such as service level agreements (SLAs) on uptime, response time and net promoter score (NPS), that help measure the quality of operations.

“We also recommend looking into translating these into an effective increase in IT team capacity, as measured by reduction in person-hours spent on a process or task,” he said.

Mukkamala noted another key performance indicator (KPI) for evaluating the effectiveness of AI-driven automation is the reduction in incident response times.

“A significant decrease in response times is a clear indicator of the efficiency of AI in managing IT workloads and automating problem resolution,” he said.

Strategic Transformations Take Time

Kohli noted change takes time and AI is new to most people, so it’s important to allow time for a strategic transformation to take place.

Similarly, organizations should create and protect the budget for this transformation, so it does not get rolled back into day-to-day operations.

“Finally, they should decide on a timeline to measure the return on this budget,” he said. “The return is measured not only in savings and dollars, but also in employee job satisfaction as measured by NPS.”

Ragupathi added while the KPIs widely depend on the AI use cases implemented, some key metrics that can help organizations understand the effectiveness of AI-driven automation include the percentage of requests handled by virtual support agents and the number of issues resolved with AI-recommended resolutions.

“The latest advancements in LLMs, like the ability to understand and interpret images and videos, will soon find their way into ITSM practices like incident management,” he explained.

This will provide more context and information to technicians about any issues reported since the screenshots and screen recordings submitted by end users can be analyzed.

Mukkamala said by utilizing a common data repository of shared assets and services, AI can access the necessary data to effectively automate routine tasks and decision-making processes.

“This integration not only speeds up the resolution of IT issues but also enhances the overall responsiveness and agility of IT services,” he said.

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