ServiceNow Revamps Intelligent Chatbot with Generative AI

ServiceNow recently unveiled Now Assist for Virtual Agent, a solution that fortifies the platform’s chatbot with textual applications of Generative AI. The combination is designed to enable conversational, self-service user experiences to easily initiate — and complete — what otherwise may be complicated workflows.

Now Assist for Virtual Agent is the latest ServiceNow offering to exploit the enterprise worth of Generative AI. The company previously announced ServiceNow Generative AI Controller, which enables the platform to access Generative AI models via OpenAI API and Microsoft Azure OpenAI Service. ServiceNow architected Now Assist for Search on top of the controller; the former provides search results from internal enterprise sources.

The Virtual Agent solution sits atop the search and controller components, both of which are available for rapid question answering, intelligent search, and content summarization. Now Assist for Virtual Agent also integrates with a designer component (Virtual Agent Designer) for creating workflows.

According to Jeremy Barnes, ServiceNow VP for Platform Product AI, uniting these capabilities through Now Assist for Virtual Agent “gives a control that our customers know and love, but allows it to be much more rapid to create these conversational experiences that users want so they get resolutions faster.”

From Analysis to Action

Coupling leading-edge natural language technologies with ServiceNow’s chatbot expedites timely action from information retrieval, textual summarizations, and low code workflow design. Users can ask questions, issue prompts, and search content before acting on results. Jon Sigler, ServiceNow VP for the Now Platform, described a use case in which an employee requires Now Assist for Virtual Agent to search through internal documentation to find options for benefits packages.

In this example, the bot would access the relevant knowledge base to find all the pertinent documents, send it to Azure OpenAI service or OpenAI API to summarize this information, then present results to the user. “In conjunction with the Generative AI piece, we can add actions users can then take,” Sigler commented. “In this use case, they may be able to change their benefits based on the conversation they’re having with the bot, as opposed to going in and doing it themselves or getting an agent involved.”

Conversational Workflow Design

Another premier benefit of buttressing Virtual Agent (VA) with vanguard language understanding models is users can build workflows for the bot via natural language interfaces. Augmenting the VA designer with the heightened Natural Language Understanding of contemporary Large Language Models (LLMs) creates a situation in which “anyone can build one of these VA workflows, and you can now do that conversationally with a great experience,” Barnes remarked.

The actions that can be taken after employing LLMs to search through and summarize results from relevant documentation can be input in workflows as suggestions. The entire process is substantially accelerated, from designing workflows, triggering them via natural language, and implementing next actions. Barnes explained these “actions can be taken because they’re embedded in a workflow platform that has built them in. So that connection, instead of you going through a traditional, slot-filling, cumbersome flow, enables you to get to the resolution much faster while still invoking stuff that’s there.”

Internal Applicability

One of the caveats about using bots like ChatGPT for information retrieval is it searches for answers through public sources on the internet, which isn’t always reliable. Now Assist for Virtual Agent supports implementations in which results are gleaned from trusted, internal sources. Granted, API calls are still made to summarize those results as needed.

Nonetheless, since the answers are derived from enterprise content repositories or databases, they’re much more credible than they’d be otherwise. “It’s not that we expose all of the internet to what we’re making available,” Sigler said. “The real power here is taking things from within the instance, the customer-specific data, the knowledge articles.”

Underlying Utility

Decoupling internal sources of searches via natural language models from environments in which models synthesize results is crucial to the overall enterprise adoption and underlying utility of Now Assist for Virtual Agent. “That is the real key to success here,” Sigler reflected. “It’s your ability to take that domain-specific instance, specific data, and send that off to the backend and get a summarization of that.”

When these capabilities are joined with the ability to take action from an intelligent bot with an easily-defined, conversationally developed workflow, the effectiveness and efficiency of this approach become apparent.