As you plan your future investments in chatbot and conversational AI technology, there are some key areas to consider and evaluate. Here’s a checklist of what to look for when selecting a conversational AI chatbot for your business or organization.
Does it have multichannel support for the following?
- Web Chat
- Teams
- Slack
- SMS/Text
- Email
- Social Media
- Web Forms
- Ticketing
When it comes to AI-Based Natural Language Processing can it handle the following?
- Intent
- Labeling
- Response Management
Are these response actions available?
- Designed Dialog Paths
- Present Knowledge Base Articles and Services
- Personalize with Data from Backend Systems
- Launch Dynamic Forms
- Send Notifications via SMS/Text, Email, or Webhook
- Live Agent Failover
- Send URLs
- Launch Automations to Fulfill Requests
- API/Webhooks
When it comes to advanced training and learning, does it include the following?
- Preloaded Training Templates
- Iterative Models
- Supervised Learning—Human-in-the-Loop
- Intent Matching Confirmation
- Exception Processing
According to a recent survey, there are four key elements for a successful chat experience when it comes to using conversational AI for self-service. The top cited element was a strong knowledge base for content to feed the chat, which was named by 75 percent of respondents. That was closely followed by the ability to personalize the conversation with details about the customer, named by 63 percent. Third and fourth place were ranked equally, named by 62 percent, who equally weighted the ability to present questions to the employee that drives dynamic content with the ability to automate the fulfillment of requests from the chat interaction.
Conversational AI especially benefits service management teams when paired with enterprise integration and automation (using iPaaS). This combination can elevate chat from a glorified knowledge base search engine into an automated, action-centered channel to field requests.
No matter how good the knowledge base, personalized user information can only be uncovered through the inherent ability to connect to business systems via APIs and integrations. This capability is what makes it possible to automatically provide dynamic content and fulfill simple, repetitive requests for action.
One example of this in action would be an employee asking the chatbot how much paid time off (PTO) they have left for the year. A first-generation chatbot may not be able to answer that, instead offering a link to the employee knowledge base article about how much PTO each employee gets annually.
A conversational AI chatbot tied to a well-connected integration and automation layer could personalize the response leveraging Single Sign-On, and then access the employee’s data from another application to deliver an accurate, fast response. In this case, the response may say, “Currently, you have 12 days of PTO left this year.” It might even follow up with a question like, “Do you want to know how many of these days will roll over next year?” or, “Would you like to request time off?” If the end-user response is to request time off, the solution would present a form for the request to be entered and then pass that data back to the PTO tracking platform.
Incorporating a conversational AI-powered chatbot as part of your ITSM strategy can significantly enhance your user experience, streamline processes and unlock cost savings. By leveraging the power of conversational AI, you can stay ahead of the curve and better deliver top-notch IT support services.
Do you want to learn more about chat vs. conversational AI? Read our newly published market study: State of Chatbots and Conversational AI.
