Anita Kantar
AI’s popularity explosion over the last several years has created a virtual army of chatbots and virtual assistants. Nearly every modern application now features some sort of chatbot. But if you look closely, you discover something interesting: chatbots and virtual assistants typically fall short of expectations. Sure, they can answer basic questions or refer users to a link. But they do not drive any meaningful operational outcomes.
We need a change of perspective.
If AI product development is to lead to a conversational interface that delivers true ROI, developers need to go beyond the novelty of answering basic questions. Creating a successful AI system requires digging well below the surface and embedding core technology deep into workflows. The deeper one digs, the greater the ROI. Digging deeply is the core AI product development philosophy at GojiLabs.
From Standalone Chatbot to Integrated System
According to GojiLabs, the thing that clearly differentiates a basic chatbot from a true AI assistant is its integration capabilities. A basic chatbot doesn’t do much on its own. It is programmed to lean on simple, pre-defined logic and keyword matching. It also operates in a silo. The traditional chatbot is detached from the systems it is supposed to support.
On the other hand, a truly productive AI assistant is an active participant in an organization’s workflow. It artificially understands context and nuance. It’s capable of taking action when necessary. This says something about AI product development: the goal is to build an ecosystem in which the AI conversational interface is a dynamic layer capable of connecting users to the organization’s software architecture and real-world data.
It Begins With the Right Foundation
Just as with any other type of software development, AI product development must start with the right foundation. Building a virtual assistant that drives ROI requires a meticulous process rooted in integration and user experience. To that end, there are four things GojiLabs says must be considered:
- Use case – Successful AI product development never starts with the technology. It starts with a reason for developing it in the first place. Use cases define everything in AI.
- Reality – AI is notorious for generic answers and virtual hallucinations. To avoid this, an AI assistant must be grounded in an organization’s knowledge base and internal documentation. Retrieval Augmented Generation (RAG) sees to it.
- Execution – An AI assistant should not just talk about a task. It should be able to complete the task whenever possible.
- Context and history – AI tools should always account for context and history. Maintaining a running conversation improves accuracy, functionality, and productivity.
By starting with use case and then building technology around it, it’s possible to create virtual AI assistants that put basic chatbots to shame. If that is not the goal of a company’s AI product development strategy, then why bother?
Accuracy and Long-Term Value
Another flaw in modern AI product development is the assumption that building a conversational interface is a onetime project. It’s not. If an organization does it right, it’s actually an ongoing enterprise. In addition, maintaining long-term value requires building in fallback mechanisms and human-handoff pathways capable of performing even when queries exceed the assistant’s normal scope.
Over the long term, developers should be paying close attention to accuracy and optimization. They should be monitoring interaction models and refining performance based on user behavior. They should be expanding integration to ensure the AI assistant evolves with the organization.
AI doesn’t have to be relegated to simple chatbots that only answer simple questions. But moving beyond the basic requires a new way of thinking about AI product development.
