Support cases at many businesses follow the Pareto principle. For example, DoorDash and Uber 87% of support requests relate to 16 issues. Deploying chatbots to solve high concentration issues makes it economical to build and maintain conversation trees by hand. What about the remaining 20% and what about businesses that have a wider distribution of issue types?
Advancements in large language models might make the last 20% tractable if not economical. The general reasoning skills and ability to respond to unique situations with mostly right answers could be a boon for chatbot based support. What’s more, businesses can use their existing data to fine tune and supplement resolution paths using support materials they have already produced for human agents.