How AI transforms IT Service Management
In the quest of smarter, faster and reliable services, IT leaders are in search of new methods and processes to improve their service delivery. In the past year or so Artificial Intelligence started delivering new breakthrough features which will transform IT Service Management (ITSM) in resulting efficiencies for organizations. Many organizations are moving towards automation and utilizing Artificial Intelligence (AI).
We have seen many changes in the way IT delivers its services. I feel there is a new wave of disruption driven by AI. Enabling a rapid self-resolving service desk that analyzes input from conversations and matches it to corresponding solutions. As organizations automate more services through AI, they also produce significant data. The biggest productivity advances of the future will come from solutions based on AI and machine learning (ML) to harness the data and deliver smarter and more efficient services.
Automation can utilize our talented human teams to do the tasks that are more value add, like innovation, strategic tasks or those tasks which people or uniquely qualified to do. Artificial Intelligence (AI) is a buzz word in the industry, let's look at what it means, how it works and how it's going to transform IT Service Management.
I believe there are many ways AI will transform IT service management which drastically improves how an organization provides service.
Here are a few examples:
- AI will power the self-serve platform. The innate machine learning ability allows it to constantly learn and track user behavior leading to better suggestions. This enables organizations to take their self-service offerings to the next level, in turn reducing the number of tickets which results in cost and time reduction.
- It will empower employees to experience effective self-service for simple issues. For example: setting up a voicemail, finding out the status of an existing ticket or resetting their password. However, if the issue escalates or becomes more complex and they can’t quickly find answers on their own, they can still submit a ticket to get support.
- By analyzing and comparing text from the ticket to the resource pool, AI systems can rapidly and accurately prioritize requests and provide deliverable solution options to end-users while they can focus their time on more complex specific tasks.
There are two basic types of AI Assistants widely being used throughout the industry: Front end assistants and AI-assisted agents.
- A Front-end AI assistant is a conversational AI assistant that interacts directly with a customer without any human intervention. They're also known as chatbots or virtual assistants. These are widely used on websites and messaging platforms on the internet where users ask certain questions. Front-end assistants provide a pre-configured answer based on pre-configured backend flow.
- An AI assisted agent is a human agent supported by an AI assistant. They're also known as a human-in-the-loop system. It’s similar to the frontend AI assistant but when the AI assistant can’t handle certain types of queries or a user requests more help than the assistant can provide, then the assistant connects the user to a human
There are many companies who are adopting a "front-end AI assistant" to handle first level customer inquiries such as FAQs, providing quick information, answering a particular type of repeated queries, etc.
However, in my view due to the limitations of not being able to serve complex requests, a "front-end AI assistant" is not enough. Although they are adept at filtering through initial and basic requests, they become stagnated as they lack the ability to further develop the interaction some users are looking for. Throughout the interaction, if the user needs to have the option to talk to a human agent, the AI assistant needs to be able to provide that option. Either solution will reduce support center average issue handle time.
A good AI assistant solution separates itself by being able to provide more insight through issue identification, provide fixes and also execute pre-populated workflows such as assigning a ticket to an appropriate team, send a request for approval, etc.
AI assistants can help organizations in multiple ways at a point of entry:
They can be connected to a messaging platform (e.g. Slack, Microsoft Teams, Skype, Google Assistant or Alexa) where users contact your support (traditionally via phones, emails, self-serve, etc.). AI assistants can act like your traditional support in triaging and logging a ticket.
For example ‘I need to reset my network login password’ the AI assistant can take the keywords ‘network’ ‘password’ ‘login’ and automatically handle tasks like approvals, open a self-serve browser to reset the password from identity management tool or log a ticket to backend team along with the conversation. This enhances the current self-service model and also reduces the average issue handling time.
When utilized properly, AI can also automate front and backend processes, as well as provide IT teams (customers in some cases) with recommendations to certain types of system issues. They also promise predictive analysis such as anticipating issues before service disruptions.
As AI is maturing at a fast-pace and beginning to transform the industry, AI assistants will be on track to replace traditional call center support and BPO type environments. They will also assist organizations to meet their increasing ITSM demands thus allowing human resources to focus on major incidents or other ITIL processes.
As AI capability is being rolled out, both employees and IT staff will be able to appreciate smarter and faster IT support. I don’t think it will be as dramatic as driverless cars, but when it comes to ITSM, AI will improve the way we work in the future.