5 Ways Machine Learning Will Revolutionize End User Engagement with ITJune 3, 2016 No Comments
Featured blog by Seng Sun, CEO of SunView Software
With the emergence of the digital enterprise, organizations are looking for smarter ways to transform their ITSM operations. This is in an effort to support a proactive, people-centered approach that their end-users have come to expect from consumer technologies while also empowering and maximizing employee effectiveness.
These new realities of the digital enterprise mean that people, processes, and technologies in the organization must be aligned around agile approaches that can better manage, deliver, and support solutions for a new generation of knowledge workers that are both tech savvy and demand a consumer-like user experience.
In the past, these methodologies often proved difficult to implement, however, thanks to new innovations in AI technologies, organizations can now package the complexity of ITSM intelligence. The barrier has never been lower for organizations to harness the power of machine learning and big data to better realize the dream of delivering truly proactive, end-user centric service. Intelligent augmented systems and smart-enabled technologies are the keys to unlocking the full potential of next generation service management.
Below are 5 ways that machine learning will usher in this new era of ITSM:
1. Power Self-Service with Intelligent Search: Gone are the days of users submitting service requests into the black hole of the service desk. By tapping into an organization’s historical datasets and search queries, intelligent search powered by machine learning can instantly deliver better and more contextual results as soon as a user begins typing. Search results will also become more targeted over time as the technology iteratively learns from user queries – prioritizing the results that matter most to a specific individual within the organization. Intelligent search can also help to index knowledge articles, DIY videos, FAQs, documents, scripts, etc., into a knowledge graph that makes it easier for end-users to find relevant information for answering questions or resolving issues. With intelligent search capabilities, organizations can empower end-users by providing smarter self-service that helps them find the results they’re looking for faster and in real-time.
2. Keep Users Informed with Smart Notifications: Today’s IT workers demand intelligent, “always-on” technologies that can help them to make smarter decisions and transform ITSM operations from reactive to proactive. With machine learning technology, ITSM systems can recognize potential problems or failures through advanced pattern-identifying algorithms that constantly learn from an organization’s historical data. From there, organizations can proactively deliver notifications to warn both end-users and service technicians of current, and planned service interruptions as well as keep track of daily service usage patterns to build contextual intelligence and support more personalized notifications.
3. Reduce Resolution Times with Curated Knowledge: Team work is essential for any positive outcome, and the service desk is no different. Today, service desk collaboration is augmented via machine learning technology to promote end user engagement and reduce resolution times. This is done by technology that dynamically adjusts the communication streams between IT and the end user as interactions progress. For instance, help desk technicians can immediately access and share incident history, recommended solutions based on historical data and knowledge base articles, and any other relevant information while working tickets.
4. Better Engage Users with Intelligent Request Handling: With the future inclusion of machine learning to assist in the process of request handling, automated bots can be leveraged to automatically respond to service request with suggested solutions based on the context of the problem. Additionally, bots or virtual personal assistants may automatically submit service request on the user’s behalf using natural language processing algorithms to interface with the end user and abnormal behavior detection to predict problems before they occur.
5. Become Proactive with Abnormal Behavior Detection: New machine learning technology opens the door for proactive delivery of services based on abnormal behaviors in the digital workplace, whether that’s human or machine. One example is measuring sentiment of the end-user throughout the collaboration with the service desk to detect overall satisfaction levels, leading to the automatic escalation or reassignment of a support ticket if deemed necessary. The same can be said for machines, with support tickets being automatically submitted if a machine’s performance values or usage patterns fall outside normal behavior.
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