Introduction

Technology revolution has rapidly changed the way businesses and users work. Businesses deliver great user experience powered by technologies such as AI, ML etc. Artificial Intelligence is already part of our lives that is delivering a great experience. Consumers expect the same level of experience in the workplace. IT leaders are having a lot of technology conversations around AI as customers expect a service similar to Amazon’s Alexa, Google Home etc. Instant response, personalization are fundamental expectations from a service desk. Therefore, the service desk will take a new dimension powered by AI and automation to deliver exceptional service.

According to a PricewaterhouseCoopers (PwC) report, the global economy can see a potential contribution of $15.7 trillion from AI by the year 2030.

There has been a mad rush for companies to go behind these technologies without understanding the fundamentals. If you are clueless or just starting to think about AI, don’t worry. You’re not alone. Though this technology is at a very early stage, this would be the vcompetitive differentiator in the future.

Leading analyst house Gartner predicts that 30% of CIOs will be investing in AI as one of their top five priorities by 2020.

This requires a lot of groundwork in order to understand, prepare and implement AI technology. This eBook gives you some practical tips on how to get ready for this AI revolution and groundwork that you could start today to handle this.

There are three types of Artificial Intelligence: 

  • NLP - Natural language refers to the way humans communicate with each other. It is the interaction of intelligent systems using a natural language. Example - English.

  • ML - Machine learning is a field of science that learns from data patterns and make recommendations or decisions.

  • Virtual agents for self service - Digital assistants provide solutions drawn out of data models and previous history. This also drives self-service and consistent user experience.

Preparing for an AI revolution

Data

Data is the foundation for AI operations. Data mining and management form the core of AI application. Consistent data flow is mandatory for the success of AI. AI learns from the past data and improves continuously. Service desk integration with external marketplace apps demands consistent data sync. Service desk has continuous data flow to other third party systems and vice-versa. Sharing real-time data is important for a meaningful sync. With the growing importance of big data, businesses are taking conscious efforts in collecting, storing and managing data so that it is easier for AI/ML programs to understand and learn. REST APIs are used by the service desk to send and receive data with external systems.

Therefore, structured or unstructured data is the lifeblood for both supervised and unsupervised learning. AI programs analyze huge data sets and find out statistical patterns. Data management involves data organization, cleaning, testing, and optimization. Finding the right data infrastructure is key for AI success.

It’s time for you to rethink your data consolidation strategy and align them based on the above requirements such as real-time data sharing. Before investing in any AI strategy, it is important to answer the following questions.

Establish a solid Business Intelligence (BI) program to drive the decision-making process. AI can leverage BI programs to further optimize and improve the process.

Strong Knowledge 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:

Gartner predicts that through 2020 99% of artificial intelligence (AI) initiatives in IT service management (ITSM) will fail due to the lack of an established knowledge management foundation.

Establish clear objectives for your Knowledge Management program such as:

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.

Self-Service culture

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.

A survey conducted by Enterprise Management Associates (EMA) found improvement of end-user experience via mobility, self-service and bots to be the leading priorities.

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.

Training & Education

Automation aims to deflect trivial tickets which are generally handled by Tier I support agents. Therefore, there is a common fear of job loss and insecurity among support agents. Leadership is responsible to educate and train agents that AI does not replace human agents. Training is crucial for your employees to acquire the relevant skillsets and embrace the workplace changes without any downtime. New skills development helps businesses to stay competitive in short term and long term. People skills are equally important as the technological investment for AI. Learning & Development is important for all employees to overcome the fear of job insecurity and find a suitable role in the AI automated business. AI adoption rate differs from business to business but investing in your employees is the first step to get ready for AI. It’s the time to rethink our approach to education and employee development in order to adapt AI better within the organization. Cultural training of employees is also significant to drive the right behavior and self-service culture.

Implement a pilot experiment of AI in order to demonstrate the value it brings to the employees. Highlight how AI will be beneficial in their day to day work and the positive impact it brings in. Artificial intelligence and automation will bring a huge change to your business. It brings opportunity for your business to grow and build a stronger and reliable organization. Change Management is important as AI and automation are expected to introduce a lot of changes. This will definitely have a huge positive impact on the organization’s productivity. AI can be a great enabler that empowers people, improves the quality of life and opens up opportunities. It’s not a fight between man and machine, but man assisted by machine.

Lean & Agile frameworks

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.

Summary

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.

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