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Today’s CIOs, CEOs have started Artificial Intelligence(AI) pilot experiments in their organizations. Service desk handles different types of routine and non-routine tasks on a day to day basis. Gartner reports that IT organizations spend 66 percent of their resources on day-to-day operations, in “keeping the lights on” activities. End users who are millennials and Gen Z demand better experience. Artificial Intelligence, AI technologies leverage the power of data to draw predictions and automate processes to meet customers’ expectations.
Driverless cars, virtual assistants and robots are one among us today which are powered by AI technologies such as Natural Language Processing (NLP), Machine Learning (ML) or voice assistants. These technologies are no longer a hype but a reality today in many businesses. AI is a long-term investment which takes time and resource to plan, execute and realize benefits. 2018 would be the year when businesses create new roles and investment in AI and analytics. According to Gartner, “By 2020, the average person will have more conversations with bots than with their spouse.”
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While AI & ML have already set their footprints in e-commerce, automobile, and other industries, IT has just started embracing these trends. IT Service Management (ITSM) has a huge potential to benefit from AI as service desk agents perform a variety of transactional tasks. But, it is important to understand the basics of AI, the impact of AI adoption, and do some groundwork before implementing it. AI helps IT to meet the growing expectations of users in terms of faster service and the latest technology. Digitalization drives AI to leverage data and accelerates business performance.
Lack of Education
Inconsistent data management
Improper Change Management
Education and awareness are crucial while kicking off new technology investments. Since AI is still not fully mature yet, ambiguity exists among IT stakeholders especially service desk agents. Lack of proper education leads to failure of adoption. Proper communication and education to IT admins and agents about AI advantages would improve the outlook of AI. Initially, everyone in the organization might be reluctant about this new technology due to fear of losing jobs but it is important to overcome this resistance through proper education. Educate the differences between these different technologies such as AI and ML and their potential benefits.
Tip - Start demoing AI & how it can reduce routine tasks of agents.
Tip - Classify, store and manage data for every process
Bring Your Own Device (BYOD) and cloud applications increase the possibility of shadow IT. Employees and siloed departments tend to use independent apps in order to get things done faster. This might improve their productivity but using unauthorized apps in workplace increases security risk. IT has no records of these data and therefore, this is an inhibitor for technologies such as AI or ML while analyzing historical data. Eliminating Shadow IT enables holistic IT approach and effective functioning of technology implementations.
Tip - Let IT be the single point of contact for any technology implementation
Handling cultural change, ethical dilemma and resistance from agents are crucial while implementing AI for ITSM. Planning a proper change management is key. Agents and end users need to be trained on AI enabled solutions and their potential benefits. It is significant to highlight that AI technologies will not replace humans whereas they will complement human agents improving overall efficiency. It’s the responsibility of management to communicate the purpose of AI and remove this aversion from agents. Trust building and proper change management help in better adoption.
Tip - Pilot AI experiment with few agent groups
Enterprise Service Management
Employees prefer one stop portal to access any service and AI & ML deliver better results in the centralized set up. AI has to be an enterprise-wide initiative with a huge volume of data available across all functions. Enterprise wide service management has huge volume of data collection and analysis. This drives big data implementation to derive meaningful inferences. This leads to better customer success management, data-driven decisions and analytics.
A repository of FAQs and solutions is crucial in building an effective knowledge management. Knowledge base acts as a source for big data analysis and AI. Business intelligence is a result of an effective knowledge management. Identifying new solution articles to add to knowledge base and suggesting the relevant article to end users are some of the common use cases.
“Through 2020, 99% of AI initiatives in ITSM will fail, due to the lack of an established KM foundation.” from new Predicts 2018: IT Operations on Gartner
Digital initiatives drive AI innovation in most organizations. Digital transformation leverages technology innovation to drive business growth and efficiency. This exercise starts with reviewing current legacy applications and optimizing them for improved efficiency. DX strategy has to be an enterprise-wide initiative and not restricted to IT alone. Applying AI technologies such as chatbots, virtual agents to improve customer interaction are common examples.
40% of digital transformation initiatives will be supported by Machine Learning and Artificial Intelligence by 2019.” - IDC
Agility is important to any business to move faster. Business applications, processes and models have to be flexible in terms of customization and set up. AI technology implementations demand data mining and data availability. Service management uses agile framework to speed up development process and time to market.
Develop a self-service culture by marketing your service desk accessible from anywhere. End users demand immediate response and resolution through self-service. Therefore, chatbots and virtual agents improve response rate and deliver a consistent user experience. Tier I queries can be deflected using these technologies which saves time and resource for agents.
Create an AI Learning & Development center to develop awareness, educate stakeholders about AI&ML technologies and to enforce cross-departmental collaboration among IT, business and data analysts.
Build a modern analytics culture for proper data collection, storage and analysis. Data warehouse forms the basis for AI innovation. Facilitate relevant support systems including technology capabilities, resource planning and governance. Convert data into insights for better decision making.
Implement an unified digital enterprise with integrated solution that acts as a single source of truth and enables AI-powered analytics and automation.
AI can be implemented in ITIL modules such as incident management, service request management, change management etc. to automate routine activities. Before implementing these use cases, it is recommended to understand the inhibitors and drivers of AI as mentioned above. AI technologies interact mainly with three actors such as
Agents are often loaded with routine tasks such as ticket assignment, firefighting etc. AI enabled technologies boost productivity and let agents resolve complex Tier II, Tier III issues by automating these routine activities.
AI powered knowledge management provides a solution from the repository if available or searches the cloud to suggest a relevant solution. Besides this, it creates new articles if not available already and provides smart suggestions for IT agents while providing resolution. Deep learning technology is used in knowledge management for solution recommendations to agents and end users.
User satisfaction and experience have become one of the key metrics in measuring service desk success. Predicting end users’ sentiment at the time of raising tickets depending on the usage of words and previous CSAT survey results help agents to respond appropriately and improve CSAT. ITSM trends involving AI and ML play a major role in this to be proactive.
ITSM solutions integrate with other business applications such as monitoring tools, facilities management etc. Service desk powered by AI & ML create tickets automatically on its own if a particular infrastructure goes down or something deteriorates. It also informs the relevant users who might be affected and creates a problem ticket for root cause analysis.
Change Management minimizes risk and impact. Machine Learning, ML gauges the potential risk and prompts Change Manager to execute the back out plan. ML also helps during change evaluation and planning to schedule the change request appropriately.
Asset lifecycle and performance can be effectively monitored by AI powered technologies. If an asset’s performance deteriorates, ML identifies this based on previous trends and notifies Asset Manager to replace the respective asset. It places an automatic service request to replace the particular asset.
AI & End user
Chatbots and virtual agents ensure real-time, consistent and personalized interactions with end users increasing customer satisfaction levels. Chatbots enable consistency in terms of language, response time and availability. However, it does not replace human agents as they are involved in solving complex Tier II and Tier III queries.
End users often get confused with the difference between an incident and service request. AI technology identifies the ticket type based on its past learnings and classifies them for the service desk agents. This eliminates the routine task of ticket classification performed by the agents.
AI powered technologies respond to end users’ queries with real time solutions without any human intervention. They search the knowledge base for solutions. If not available, they suggest solutions from the cloud and create new articles that can be stored in the repository.
When end users place any service request, Machine learning checks for the service item availability and approves automatically without any human intervention. Approval is handled based on the priority, past history and impact of the requested item.
Predictive analytics analyzes past results and forecasts future projections including revenue, customer satisfaction and resource planning. This helps management to make informed decisions through budget forecast and expense management. It also provides insights on agent and service desk performance.
Based on the previous trends, any future SLA violation can be identified and notified to the right agent. This is done depending on the ticket volume, seasonal work load, infrastructure failure and resource issue. Contractual agreement is maintained as well as customer issues are resolved on time.
Service desk agents spend most of their time in ticket classification and assignment. AI technologies take care of identifying the right group and right agent. It also suggests the management on staff hiring based on the workload and future resource planning.
Effective knowledge management deflects L1 and L2 tickets. This allows human agents to focus more on complex projects, thus saving time and resource.
Interaction with service desk becomes consistent for end users with the help of chatbots and virtual agents which deliver seamless service experience.
These technologies enable agents with the right resources, sometimes handling tickets by themselves which result in the reduction of average resolution time.
Data can be converted into insights and this can be consumed by the management to take meaningful business decisions.
AI & ML helps businesses to be proactive in identifying potential incidents and deflecting trivial issues improving customer satisfaction
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