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 Delta’s president’s remarks to investors, 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 denial that they’re engaging in “discriminatory or predatory pricing practices” 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.”

Instacart has caught similar flak for experimenting with “individualized” pricing for groceries using AI tools, according to a Consumer Reports investigation. When customers hear you’re using their data to set their price (which is different from recommending a bundle or promo offer), they might not assume you’re doing it to offer them the lowest price, but that you’re trying to maximize your profits. That’s a distrust of your AI’s objectives, not the data.

This isn’t just an airline or grocery problem. In healthcare, algorithms trained to predict patient risk have sometimes prioritized cost over care, leading to sicker patients being overlooked for extra support because the AI was tuned to minimize expenses, not maximize health outcomes. This sort of tuning can also carry discriminatory side effects, risking the organization’s brand perception, not to mention legal action.

In telecom and other subscription-based industries, AI systems can be tuned to minimize churn or maximize upsell. The difference comes down to what the business values most. The point is, if you’re only looking at this month’s numbers, you’ll miss the bigger picture, which is why every brand should be asked: 

Is Your AI Trading Loyalty for a Quick Buck?

In retail, AI-driven recommendation engines can push more high-margin products to customers over the less profitable products, regardless of their fit. That might boost profit in the short term, but if customers feel manipulated, they’ll start shopping elsewhere. The same goes for banking, where AI can optimize loan offers for profitability. When a customer shops around, they’ll likely figure this out, and there goes your prospect.

Think back to the scenario of an AI model being tuned to figure out (and offer) the maximum price a customer will pay. The brand might see lift on those sales, but the real blind spot is not knowing the customer loyalty repercussions of that business-centric bias years down the road.

Who Owns the Objectives?

In most organizations, AI objectives are shaped by a mix of teams: CX, marketing, loyalty, legal, customer care and others. But the ownership of long-term goals isn’t always clear.

This makes cross-functional collaboration so important in AI strategy. Brands need to bring together everyone who impacts CX and focus on what drives loyalty and sustainable growth. If objectives keep changing quarter to quarter, AI will deliver inconsistent results. The best outcomes come from setting clear, long-term objectives and revisiting them regularly.

7 Steps for Auditing Your AI Objectives

Here’s a practical framework for leaders to audit and align AI objectives.

1. Define Your True Business Goals

Clarify whether you’re optimizing the AI model for profit, loyalty, retention or a mix. Make these goals explicit. Then share them across teams and lock in their alignment.

2. Map Objectives to Customer Outcomes

Ensure every AI objective connects to a positive customer experience, not just internal metrics. Ask yourself: “How will this objective affect the customer’s journey?”

3. Evaluate Vendor Alignment

Ask vendors how their AI platforms support your objectives and mitigate bias. Request evidence and case studies. Don’t settle for vague promises.

4. Monitor Long-Term Metrics

Track retention, loyalty and customer trust over time. Adjust objectives as needed. Use cohort analysis and customer feedback to spot trends. Your teams with the longest-term metrics must have a critical seat at the table here.

5. Build Governance and Transparency

Establish clear processes for reviewing and updating AI objectives. Document decisions and rationale. Review objectives for any disparate impact they could have on specific groups of people.

6. Test and Learn

Run experiments to see how changes in objectives affect outcomes. Use A/B testing and pilot programs to gather data before rolling out changes.

7. Stay Informed

Keep up with analyst research, industry trends and regulatory changes. Objective bias is a moving target, so stay ahead by learning from others.

Align Your AI for Loyalty

The objectives you set for your AI shape every customer interaction, every outcome, and ultimately, your brand’s reputation. If you’re implementing customer-facing AI, you’ve got a bigger job than just picking the right tech: You can help ensure the AI model is tuned for long-term success.

If you’re ready to put objectives at the heart of your AI approach, CSG can help. We work with clients to set clear goals, align teams and track results so your AI delivers on what matters most.