AI is a powerful technology, but you can’t merely set-it and forget-it. Here are some things to consider when evolving your AI system along with your business.
“Adapt or Die” doesn’t only apply to natural ecosystems; it’s also an imperative within the business world. Companies must evolve in order to avoid falling behind in an increasingly competitive global marketplace. The good news is that Artificial Intelligence (AI) enables a new dimension of versatility; the bad news is that this flexibility comes with challenges of its own.
Case in point: The challenge of maintaining an AI-powered conversational user interface. For example, IPsoft’s market-leading digital colleague Amelia allows businesses to iterate user engagements instantly and at scale, be it to drive new efficiencies, improve the customer experience or comply with ever-evolving regulatory environments. While Amelia comes with Machine Learning (ML) and Intelligent Automation (IA) functionality that allows her to operate with some autonomy, she still requires ongoing guidance and training. Companies need to keep her updated to reflect the latest offerings and procedures as those elements change over time, and to keep pace with user expectations that are also rapidly evolving).
Here are a few things to keep in mind to ensure your AI systems are keeping up with your business needs.
Quality Control Through Analytics
Data-driven analytics provide enterprises with valuable insights that can be used by companies to make future decisions based on the best information available. Quantitative analytics are particularly useful when iterating user engagements with AI, since nearly every action can be quantified in some form (e.g., session completion rate, time to resolution, level of human intermediation, etc.).
This data-driven environment not only provides an unprecedented level of transparency, but it can be communicated in almost real time. For example, AI systems can be set to proactively notify the appropriate team about spikes in user behaviors which may indicate a problem, e.g., a sudden increase of customers abandoning sessions with a virtual agent when inquiring about certain issues. Once AI teams are made aware of a problem, they can review the relevant call/chat logs from these sessions to diagnose the problem and implement a fix.
Quality Control Through Qualitative Inputs
Quantitative analysis can provide granular, real-time insights into an AI system’s behavior; however, since these solutions involve engagements with humans, old-fashioned qualitative analytics cannot be discounted. For example, quantitative analysis is useful for letting your team know if time-to-resolution has improved month-over-month, but it won’t let you know if users feel that they achieved a resolution in the appropriate amount of time.
One of the best ways to achieve a qualitative analysis of user experience is just by asking customers directly. If you don’t have the budget or resources to conduct in-person focus group, feedback can be gathered remotely through virtual surveys. These surveys can include both structured questions (“On a scale of 1 to 10, how likely would you be to recommend this service?”) or unstructured ones (open-ended text comments). You can help drive survey participation from customers through various incentives or solicit them directly through a paid testing service.
Organizations can also tap into “social intelligence,” in which you monitor public discussions of your product or service on social media and other Web discussion groups. These unsolicited user opinions may be the less reliable (you don’t necessarily know if the person actually used the product), but if an opinion or issue comes up multiple times, this information can be used to iterate and improve your AI engagements.
Regardless of whether you undertake fixes based on qualitative or quantitative feedback, analytics allow you to readily determine any issue and, just as importantly, the effectiveness of any fix used to correct it. In short, AI allows companies to evolve and improve their businesses on both the backend and the front, so long as companies make sure their AI systems are evolving along with them.