Conversational AI has been experiencing a renewed focus in recent years. In the past few years, we’ve seen language models achieve state-of-the-art results, demonstrate impressive results with language understanding benchmarks like General Language Understanding (GLUE) and SuperGLUE, and lend themselves to practical applications. Even so, conversational AI is far from being solved. However, we’re moving to an AI- first world, where people expect technology to be naturally conversational, thoughtfully contextual, and intelligent – and so most companies will have to consider adopting an AI assistant sooner or later.

In this article, I’ll first discuss the five levels of AI assistants using a standard model for conversational AI maturity. Second, I’ll summarize my own recent experience building a level 3 AI assistant. Finally, I’ll outline various custom tools I built to continuously iterate upon, improve, and monitor the AI assistant in production.

The Five Levels of AI Assistants

Most AI assistants today can handle simple questions, and they often reply with prebuilt responses based on rule-based conversation processing. For instance, if a user says X, respond with Y; if a user says Z, call a REST API, and so forth. However, for AI assistants to provide value to business functions like customer service, supply chain management, and healthcare workflow processes, we need to move beyond the limitations of rule-based assistants and to a more standard maturity model for conversational AI. In this article, we’ll talk about how to model and deploy a contextual assistant and discuss real life examples of contextual assistants in production.

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