AI & Agent
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.
Proactive Change management
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
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.
Classification of incident vs service request
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.
Auto resolution of tickets
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.
Service Item Auto-approval
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.
AI & management
Strategic decision making
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.
Predict SLA/contract violations
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.
AI Readiness Assessment