3 ways Artificial Intelligence Will Help IT MSPs Do Better in 2021

CIOs are now using artificial intelligence (AI) and machine learning (ML) technologies to make IT service management processes more efficient.

A typical use case for artificial intelligence in ITSM involves natural language processing (NLP). User requests for IT services are automated using NLP. IT practitioners get a deeper understanding of their processes by applying machine learning (ML) to ITSM data. The natural language processing technology that powers virtual agents is very often integrated with channels that the employees are familiar with. Many organizations integrate virtual agents with chat services like Slack, where employees can directly communicate with the IT service desks.

ITSM systems generate large volumes of data, so applying machine learning to these systems makes sense. The data collected by these systems is large not only in volume but also in detail. All of this data helps us understand existing IT assets and processes, along with information about their ownership.

These insights help IT understand the real priorities of ITSM issues, work proactively instead of reactively, accelerate time to resolution and enhance employee productivity. In the current age of remote work, enhancing employee experience to ensure business continuity is at the top of every CIO’s mind, and artificial intelligence will prove to be just the right technology to use to face this new challenge.

Let’s look at the three ways artificial intelligence will help IT MSPs to do better in 2021.

Chatbots integrated with an ITSM environment can easily be used to categorize the problem in employee requests. For example, if an organization has integrated Freshservice’s Virtual Agent with MS Teams, it creates a channel for employees to raise a service request or resolve their issues. The chat interface is a familiar UI for the employees, and the chatbot will identify whether the employee has a service request or an incident to raise using machine learning.

Another important and time-consuming task normally performed by an agent or complex workflows is routing a ticket to the correct support groups. Chatbots will triage the incoming requests or incidents to the right support group, making the process a lot efficient.

The historical ticket data and an extensive ITSM knowledge base will help agents resolve various requests faster. However, this requires the admins/agents to create an extensive knowledge base covering a wide range of requests and incidents. The ability to directly convert a resolution email to a knowledge base article will help build a rich knowledge base repository. When a similar problem arises, AI and machine learning can be used to dig through this repository and present the closest match to resolve the issue faster.

A well-managed repository will also help with incident resolution throughout the solution. AI can provide advice that is as simple as a related or similar incident along with its history, or a solution article with words that match the current incident/request, thereby shortening the time taken to think through the issue from scratch.

Like employee onboarding, many requests to IT demand human staff hours to perform a series of complex tasks to fulfill the requests. Machine learning models watch and learn how humans carry on and execute them to automate them in the future.  By recognizing patterns in the request types and execution methods, machine learning-based models make intelligent suggestions for even the most complex IT processes.

Hemalakshmi is a Product Expert with Freshworks. Her responsibility includes educating and helping industry peers and customers on best practices, tips and tricks, quick guides, and solutions around IT Service Management and its various use cases. In her 6+ years of experience in the core SaaS business applications serving as a product expert, Hema has worked with multiple businesses in helping them with their business needs and setting up their service desk solution – Freshservice. Follow her on LinkedIn.

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