Preparing for an AI Revolution in IT Service Management

What’s the connection between AI and KM? Knowledge availability lays the foundation for an AI program. AI, ML and NLP algorithms expect a consistent data structure. Data and Knowledge Management go hand in hand. Knowledge Management stores and manages data for easy accessibility. These algorithms rely heavily on these stored historical data to analyze patterns and evolve. Therefore, they demand a strong Knowledge Management culture that can be accessed and interpreted easily. This has resulted in the businesses rethinking the way knowledge is developed, collected and shared. The common challenges for Knowledge Management are as follows:

• Knowledge exists in silos
• Limited or no knowledge transition
• Ambiguity in knowledge ownership
• Cultural barriers
• Poor training or mentoring programs
• No process in place to store and track knowledge
• Low engagement due to a bad user interface
• No technology backed up Knowledge Management
• Operations constraints limiting people from sharing knowledge

Establish clear objectives for your Knowledge Management program such as:

• Optimize resources by sharing already available knowledge
• Distribute knowledge to the right users at the right time and make it accessible
• Develop a knowledge-driven culture that is a cultural transformation beyond process implementation
• Make Knowledge Management an integral part of the day to day activities
• Create a KM council to manage end to end process for AI & Knowledge Management

In the service desk, knowledge silos exist and they are scattered. The greatest challenge for the IT leaders is to consolidate and distribute this knowledge evenly across the organization. Knowledge Management acts as a frame of reference for NLP use cases and virtual assistants.

Businesses constantly work towards improving self-service adoption. Self-service is important for the success of any AI initiative and to deliver a great end-user experience. AI applications include intelligent chatbots, smart solution suggester which leverage the self-service platform. Therefore, businesses want users to access the portal in order to deflect trivial tickets and automate using AI or ML technologies. Self-service is a culture that has to be driven from top to bottom.

Artificial Intelligence techniques like Natural Language Processing (NLP) and text analytics improve self-service efficiency in solving issues better. Self-service is built on a strong knowledge base to analyze and retrieve information. Therefore, self-service and Knowledge Management go hand in hand. Make it intuitive for the users to access this easily. AI-powered self-service systems anticipate users demands and are proactive.

Marketing the self-service is important to highlight the benefits of self-service culture. It is the responsibility of the management to promote, incentivize and reward users for self-service adoption.

AI experiments and innovations require a lot of changes and new pilot projects. Traditional ITSM methodologies are too rigid to adopt these AI projects. Therefore, businesses need to embrace modern practices such as DevOps, Agile and Lean in order to overcome these bottlenecks. These modern frameworks enable collaboration and flexibility to support new technologies. This is an iterative approach which deploys smaller, frequent, and low-risk changes into the environment. These frameworks complement each other and can work well with each other. Integration of all these frameworks results in agility and more fluidity which are essential for new AI experiments. Rapid prototyping, frequent releases, and sprints are essential for AI experiments that are supported by these frameworks. Agility and flexibility let companies operate efficiently in a dynamic environment by adopting rapid changes. This builds trust among people and improves AI adoption. The key is to have fixed goals that align with purpose and vision. Agile AI approach breaks down the project into smaller components for faster deployment. This gives them flexibility in rapidly evolving environments. Agile AI leverages data science and it expands to other business functions. If you are using ITIL in your organization, ensure you adopt practices such as agile to make it suitable for AI projects.

While AI is still in the early stages of practical application for most fields, it cannot be denied that the concept has finally stepped beyond sci-fi fantasy and into everyday, grounded reality. Few industries can afford to ignore this trend and least of all those like ITSM which are centered aroundprocessing information and finding solutions as efficiently as possible. Those ITSM providers that have begun laying the groundwork through stronger knowledge management, self-service, and agile practices will be well placed to lead the industry in the next few years.