Business success is highly dependent on how efficiently an enterprise handles its service requests. Users generate support request tickets when they encounter any issues with the product or services rendered by the enterprise. To meet customer/ user expectations promptly and deliver an enhanced service experience, enterprises need to prioritize resolving these issues quickly and efficiently to sustain themselves in the market.
Challenges Faced by Enterprises Using Pre-AI Ticketing Process
The traditional (pre-AI) ticketing systems may not be compatible or efficient in handling support requests in the current fast-paced business scenario. Let’s look at some of the disadvantages associated with the pre-AI ticketing process:
Considering the growing complexity of business operations and the involvement of large amounts of data, providing timely support can become very challenging. Resolving user queries, using spreadsheets and multiple systems can delay the process, increase the chance of errors, and hinder productivity.
2. Difficulty in classifying and assigning tickets to the right resource:
When a user generates a ticket, the agent classifies the request based on severity and technical complexity and assigns the problem to a pertinent trouble-shooter for further resolution. It can be a challenging task for agents to predict the priority and allot these queries to relevant experts within the stipulated timeframe. Moreover, when multiple requests are generated at the same time, agents may find it very difficult to handle all the tickets or allocate them to relevant trouble-shooters efficiently, and this may disrupt the overall workflow process.
3. Repetitive ticket resolution:
There will be repetitive user queries and it can be really difficult to offer permanent resolutions for such repetitive requests using traditional ticketing systems. Such common support requests can hinder the productivity of experts when the agent, as well as the users, have to undergo the whole process every time to get a resolution.
4. No actionable insights:
A large volume of data is generated daily through these support desk requests, and outdated processes may not be able to analyze the quality of such queries, provide any actionable insights and enable informed decision-making. Acquiring relevant insights such as the cost per ticket and other metrics helps identify the pain points and improve operations.
How AI Helps Organizations Improve the Ticketing Process?
The problem with the current (pre-AI) ticketing systems is that they are rules-driven. Hence, despite having a lot of data within applications, a huge volume of this data goes unexplored and is not analyzed effectively to identify flaws in the system and use it towards building better applications.
A pattern recognition algorithm using AI can help in understanding the past patterns of tickets that are created, thereby allowing the analysts to get more insight into what aspects of applications are failing, and what solutions were provided.
8 Ways AI Can Enhance the Ticketing Process
The demand for AI-based ticketing systems is growing exponentially. Companies can now analyze historical data for customer insights more efficiently, improve applications, reduce human intervention, enhance user experience and optimize overall productivity.
1. Ticket Prioritization
When a user raises a ticket, AI integrated systems investigate the ticketing history, identify similar requests, then apply relevant variables to analyze the severity of the request and prioritize it accordingly. It increases agents’ productivity by eliminating the tedious process of categorizing support requests, allowing them to focus on high-value operations.
2. Smooth Ticket Assignment
With the large volume of queries and tickets generated every day, it becomes a difficult task to assign the tickets to relevant support teams to resolve the issue. AI analyzes historic ticketing data and identifies the relevant experts to whom the tickets can be assigned. Assigning it to the appropriate trouble-shooters reduces the response time and thus improves the user experience.
3. NLP-enabled Ticketing
Natural language processing (NLP) capabilities as part of AI can be implemented to identify the language (words) used in the user tickets, understand the context and allocate it to relevant professionals. This is beneficial for MNCs who are bound to handle queries from various territories where users speak different languages.
4. Ticket Cost Analysis
AI leverages data analytics solutions and helps visualize relevant ticketing metrics through advanced dashboards. These insights help identify the average cost per ticket, the average cost per incident, the category, and where the business needs to improve or initiate new practices in resolving user issues.
5. Resolution Recommendations
Advanced AI solutions assist users with reference material that recommends appropriate resolutions for their queries and reduce the workload of trouble-shooters. Besides, AI leverages machine learning and helps in providing instant and automated solutions to repetitive support requests based on historical patterns through chatbots and virtual agents.
6. Volume Forecasting
An AI-enabled automated ticketing system allows predicting the volume of tickets that could be generated in the next month, quarter, or next year and categorizes it based on complexities and relevant expert groups for resolution. Such forecasts help facilitate future business operations, optimize employee productivity, enable upskilling, enhance operational efficiency, and ensure hassle-free workflows.
7. Cognitive Automation:
With possible ticketing tasks automated and replaced with modern technologies, the users can get services as per their convenience and requirements. This way, users need not wait for a long time to get resolutions for their issues, especially for repetitive and common queries. Unlike humans, technologies that empower AI and Robotic Process Automation (RPA) can be fine-tuned to function throughout the day for enhanced productivity.
Accomplishing tasks through the traditional (without AI) ticketing system can take a long time and may not be economical. AI-enabled automated ticketing system helps cater to service requests and resolve them within a short time, making it an affordable option.
In today’s technology-driven business world, integrating advanced technologies like AI with current business processes is becoming a necessity. Being prepared to resolve a large number of service requests promptly and meeting customer expectations is the need of the hour in today’s highly competitive business world.
AI is becoming an indispensable part of most businesses today, minimizing errors caused by human intervention, reducing incidents, and ensuring a quick turnaround time on resolution. Furthermore, the AI-enabled ticketing process enhances user experience, increases employee productivity, and ensures quick, reliable, and consistent resolutions.
Besides, AI-driven ticket analytics enable seamless integration with ticket management tools for further operational enhancements. KANINI leverages AI-enabled technologies to facilitate support request processing and focuses on enhancing enterprise workflows. Contact us to understand more about how KANINI leverages AI to enhance your ticketing process.
Anand Subramaniam leads Data Analytics & AI practice at Kanini and is passionate about the data science domain and has championed data analytics practices across startups to Enterprises in various verticals. He is a thought leader, start-up mentor, and data architect. He brings forth over 2 decades of techno-functional leadership in envisaging, planning, and building high-performance state-of-the-art technology teams.
Anand Subramaniam leads Data Practice at Kanini and is passionate about the data science domain and has championed data analytics practices across startups to Enterprises in various verticals. He is a thought leader, start-up mentor, and data architect. He brings forth over 2 decades of techno-functional leadership in envisaging, planning, and building high-performance state-of-the-art technology teams.
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