Before deploying Conversational AI, determine your ideal use cases, your intended audience and how often you plan to make adjustments.
Successful Conversational AI deployments require significant planning. Building, training and scaling solutions requires close supervision, some trial and error and considerate preparation. Before you begin your first Conversational AI deployment, determine why you’re implementing the solution, who should be involved in the process and your ideal first use case.
In this post, we’ll walk through four Conversational AI best practices and what you need to know before your initial project goes live. Although these suggestions provide a rough understanding of what to consider prior to deployment, keep in mind that every Conversational AI deployment and use case will be different. A recent IDC report detailed three real-world Conversational AI success stories from Bankia, Telefónica and Ken Nugent, P.C. Attorneys at Law (click the link to read the report in its entirety).
Pick the Ideal Use Case
Prior to implementing an AI use case, find a repetitive task that your company must manage hundreds or thousands of times each day. Answering customer FAQs, replacing lost or stolen credit cards— these common tasks cost companies billions of dollars each year to manage. With Conversational AI, companies can handle these repetitive tasks with ease, while saving money that can be reinvested into the business.
Conversely, attempting to tack AI onto a unique or complex problem with little to no historical data will likely lead to frustration. For AI to be successful, it must draw on data and experiences in order to make the correct decisions and choose the ideal resolution paths. In order to hit the ground running with your AI deployment, select an AI platform or solution that is already trained to handle the most common use cases within your industry.
Build for Your Specific Audience
Before designing your Conversational AI user experience, determine your intended audience. An AI system for teenage gamers should be dramatically different than one designed for insurance agents. Gamers are more inclined to want colorful, outside-the-box interfaces that feature unique fonts and animation. Business users want to be able to find information quickly and leave the environment without any diversions.
By determining your system’s intended audience, you can program it to deliver the precise experience required for each interaction. Amelia clients have deployed Conversational AI systems that display a sense of humor, use small talk and show extreme empathy. They’ve also built systems that are focused on providing results with machine speed.
Form a Center of Excellence
As we’ve detailed in previous posts, we recommend building a small, reliable team of super users to test and revise your Conversational AI project. This should be a group of passionate and imaginative workers that will help create and build the initial AI use case. Before designing the experience, language, or roles, ask your super users how AI can make their work easier and more interesting.
If all goes well, your super users will realize that they will be tasked with less repetitive work thanks to an AI system, and that they also will be armed with powerful information at all times. The group can build enthusiasm among their colleagues who will in turn also want to use the system. This will engender positive employee opinions about Conversational AI, and foster a sense of employee ownership. Employees will not feel as if AI was forced upon them; they will feel like they helped create technology-based business process improvements.
Test and Innovate in Short Cycles
When implementing a new Conversational AI use case, it’s important that organizations test viability in short cycles, rather than investing a large amount of resources into cross-departmental deployments. Companies should focus on the initial use case and master it before scaling a similar experience elsewhere within their organizations.
By assessing AI deployments in 30-60 day cycles, organizations can determine the viability of a particular use case before committing to a larger deployment. If realignment is necessary, the initial investment will have been small enough so as not to cannibalize potential ROI. Conversely, if the initial trial is wildly successful, the organization will be able to ramp up quickly without allowing probable ROI to go unrealized.
To hear more about experiences with deploying Conversational AI, read the IDC report.