Harnessing ChatGPT for Efficient Load Balancing in Zendesk
This year, I've been experimenting extensively with ChatGPT and have found it incredibly useful for summarizing API documentation and generating boilerplate code for data retrieval and posting. ChatGPT has streamlined these processes for me, turning tasks that would typically take days into a few hours' work. It provides simple, easy-to-understand code templates, enabling me to construct more complex data ingestion pipelines that could yield significant savings for organizations in the future.
One project I'm developing is an automated, real-time queue management system. A limitation in Zendesk is the absence of prioritization based on associate skillsets. To currently overcome this challenge an associate must switch between multiple views or a team must continuously monitor and modify the ticket views visible by the associates. This process, known as load balancing, is a key responsibility of a real-time workforce analyst. However, as business hours extend and routing logic becomes more complex, the manpower needed for queue management increases linearly.
By inputting a similar query into ChatGPT, I can get a step-by-step solution to this challenge. ChatGPT's contextual awareness allows me to delve deeper into any step, offering detailed solutions like boilerplate code for utilizing APIs to extract necessary data.
The solution I'm exploring involves:
1. Connecting to the Zendesk API to access views.
2. Retrieving and counting tickets in each view by status.
3. Connecting to a Workforce Management (WFM) tool's API endpoint to identify logged-in users.
4. Compiling a list of logged-in users and cross-referencing their assigned groups.
5. Adjusting group and default view settings based on users' load balancing logic.
This script can be deployed on an AWS server, triggered as per business requirements. The main advantage of an automated load balancing algorithm is resource and time savings for the WFM department. Moreover, load balancing decisions can be updated frequently and swiftly, minimizing the chances of miscommunication and implementation errors.
Five years ago, my previous company hired a vendor for a similar task, costing over $100,000 annually just for queue monitoring, excluding automatic load balancing. Now, this cost has been effectively reduced to the $20 monthly fee for ChatGPT Pro. In conclusion, with the support of ChatGPT or other generative AI models, business users are no longer reliant on developers or vendors for customized solutions. The power lies in their ability to use generative AI as a tool for implementing solutions.