Customer-Centric AI: Why Your AI’s Objectives Matter More Than Your Data
Here's something you probably don’t need to be told again:
“Your chatbot is only as good as your data.”
That’s conventional wisdom at this point, as brands have been pouring resources into cleaning up the datasets that their customer-facing AI draws from. They’ve launched chatbots, predictive pricing, automated fraud detection and so on, all while focusing on data quality. Better data means better responses, better AI interactions, and ultimately better customer experience (CX). Right?
But some brands are learning that it isn’t just the data that determines the CX quality their AI delivers. It’s also the objectives they set for it.
Imagine a chatbot designed to provide product support. Ideally, it resolves customer issues quickly, so it would make sense to tool the chatbot with speed in mind. But if its objective is simply to close tickets as fast as possible, it might rush customers through scripted answers, miss the real problem and leave people frustrated.
If, on the other hand, the objective is to genuinely solve customer issues and build trust, the chatbot will take the time to listen, clarify and follow up. Same data, different outcomes—because the objectives drive the experience.
Brands want to say their AI is “fair” and “customer-centric.” But every AI is biased, and that bias comes straight from the goals we give it. The question is who’s steering it and what they’re aiming for. Are you training your AI to squeeze out short-term profit, or to build customer trust that lasts? Are you chasing quarterly spikes, or loyalty that pays off year after year?
The objectives are part of the strategic layer of the customer-facing AI. And if you’re responsible for your brand’s CX, this is where you come in.
If you want to avoid the pitfalls of objective bias—and actually earn customer trust in AI—it’s time to audit your objectives, not just your algorithms.
Objective Bias in AI: What It Means for Your Brand
Objective bias is the tendency of AI systems to reflect the goals set by the people who design, train and deploy them. If your objective is to maximize profit, your AI will find ways to do that—even if it means charging customers the highest price they’re willing to pay, or leaving them frustrated and less likely to return. If your objective is to build loyalty, your AI will look for ways to keep customers coming back.
When I talk to CX and customer care leaders, I hear a lot about “fixing the data.” And yes, clean data matters. But if your AI’s goal is to upsell every customer, it’ll find ways to do that, even if the data is perfect. The bias is baked into the objective.
Objective bias is also a customer trust issue. We saw this with Delta Airline’s AI dynamic pricing experiment. Based on , consumers (and U.S. legislators) were worried that the airline would use AI to charge customers different fares based on their personal data—that is, charge the maximum of what the AI thinks the individual customer is willing to pay for flights. Delta issued a and are saying they were considering AI for “dynamic pricing” (which airlines have used for decades to set fares based on demand) as opposed to “surveillance pricing.”
