Most products work great out of the box. Shiny, new, no wear and tear.
But where their quality really shows up is in the days that follow, when customers are using the product daily, introducing it to new scenarios, and testing the limits of what’s possible.
The gap between what something looks like out of the box and how it holds up in real world conditions translates to the world of AI. Parloa calls it the Great AI Divide.
With the amount of AI vendors on the market today, the options for production-ready AI seem to be endless. To effectively cross the divide into value-driving customer experience, however, business leaders need to confirm three things from the start: how success will be defined, how to test for edge cases, and who owns the agents after they go live. Ultimately, business leaders need to approach adoption with the customer in mind.
Success means something different to the business leader, vendor, and customer
AI initiatives stall when no one agrees on what “good” looks like. Without a clear metric to track towards, there’s no way to effectively program an AI agent. As most AI projects tend to involve multiple stakeholders, different metrics may matter more to one person than another. In customer experience, success comes from the customer. What were they trying to achieve by calling, and were they able to achieve it? The metric that best reflects this becomes the North Star.
Additionally, with certain AI vendors, success may define pricing. In outcome-based pricing models, AI vendors will bill based on what they deem as a “success.” If the business leader, finance team, and vendor all have different definitions, the pilot will raise financial flags as soon as it hits production.
Test for the edge cases, not just the hopeful scenarios.
Pilots fail quickest when they’re only tested on what the business hopes to happen. In these scenarios, agentic CX operates no better than a standard IVR tree. It’s designed in the interest of the business, not the customer. As soon as the customer asks the question in a roundabout way or from a highway with the window down, the agent fails almost immediately.
Strong AI vendors will work with business leaders to run AI agents through thousands of simulated conversations before launch, including all of the messy realities that are bound to happen. In simulation, failures are caught and fixed before they hit the customer, saving money and time, and eliminating frustration down the line.
CX should own the AI agents.
Engineering, IT, and legal can (and should) be involved in setting the first AI agent live. After that, subject matter experts should feel empowered to own and operate the AI agents within their departments. In CX, operators know the customers best because historically, they’re the ones having conversations directly with the customers every day. When they see an AI agent not performing as it should, they make a quick fix, rather than submitting an IT ticket. When CX takes control of their agents, they can scale and deploy, in the interest of the customer.
Technology matters. Customers matter more.
There’s no shortage of AI technology available for CX today. The decision comes down to buying AI that looks flashy and sounds good in a pilot but needs to be thrown out 30 days later, or investing in a platform durable enough for each unique customer and scenario. Because at the end of the day, anything contributing to customer experience should always come back to the customer.
Interested in learning more about what crossing the AI Divide looks like? A CIO's guide to crossing the AI divide walks through the technical requirements, vendor evaluation questions, and success metrics business leaders need to align on before scaling AI past a single use case.
