Stop Blaming the Data. Start Fixing Your Objectives

AI learns from us. And we’re biased.

Because AI is trained on largely human-generated content, it picks up on our biases and bakes them in. That’s why most conversations about AI bias focus on bad data. Garbage in, garbage out. Simple enough. But even with clean data, bias still creeps in.

A more subtle and often overlooked issue is objective bias. It’s less visible than a dataset problem and is one of the biggest challenges for customer-facing AI use.

In this article, I’ll dig into what objective bias looks like as part of the customer experience (CX), why it matters and what brands can actually do about it.

Defining Objective Bias

Objective bias isn’t about flawed data. It’s about flawed intent. AI does exactly what it’s told to do, and if it’s told to maximize revenue, it will—even if that means damaging the relationship with the customer.

Take Delta Air Lines. They recently announced AI-powered pricing designed to determine the maximum the consumer is willing to pay. It’s a perfect example of objective bias. The system isn’t trained to help you find a good deal. It’s trained to boost conversion and lower operational costs.

Let’s say you’re booking a trip to Paris. You want the best fare, but the system wants the best margin. The AI might offer an $800 flight when a $400 one is available. Not because the AI is wrong, but because it’s doing its job.

Not exactly the kind of personalization consumers are begging for …

Why it’s inevitable

Objective bias is a reflection of your brand’s values, culture and priorities. It’s woven into the fabric of your AI. The real question is, which way does it “lean”? Does it favor customer goals or revenue goals?

Different teams, regions and cultures have different mindsets and will train the AI model differently. If sales take the reins, it will lean toward conversion. If the CX group is in charge, it might be better aligned with service and savings.

Same architecture, different outcomes.

The solution isn’t to strip bias out completely—it’s to point it in the right direction. Bias your AI for long-term loyalty, not short-term wins.

The Consequences of Misaligned AI

The biggest risk brands face when it comes to objective bias is loss of trust.

Customers are already fed up with generic, irrelevant brand interactions. When AI makes those experiences worse, it frustrates and alienates your buyer.

If large language models (LLMs) are trained on biased, assumption-based data, they’ll churn out impersonal responses. As a result, customers will feel like the brand couldn’t care less about them. They may buy from you today but are less likely to stick with your brand in the long term.

Experience now drives loyalty. Many customers are even willing to pay more for it. So, when an AI tries to upsell a high-dollar product that doesn’t fit the need, they notice. They opt out. They don’t come back.

The agentic AI problem 

That risk balloons when we look at agentic AI.

Agentic AI is built to act on its own. It can complete multistep workflows without human involvement. But if the AI’s logic is flawed or the training is misaligned, the damage grows.

Experts agree that agentic AI has a long way to go. In fact, a recent report shows that while nearly all CFOs know about agentic AI, only 15% are seriously considering it. Corresponding data indicates that the ability to accurately monitor and prevent bias was a key barrier to adoption.

Most agentic systems still struggle with ambiguity, persistent memory and accountability. That’s a dangerous combination when there is no clear way to diagnose or correct errors or biases as they occur.

Brands shouldn’t sit on the sidelines, but they do need to proceed strategically.

How Brands Can Minimize Objective Bias

Let’s be clear: You can’t eliminate bias. You ARE the bias.

Your brand shapes how AI behaves—for better or worse. These biases already exist in your current customer interactions. They’re in the friction in your cancellation flow, the transparency of your terms and conditions or dark patterns on your website.

The difference with AI bias is scale. AI can amplify those decisions faster and with far less oversight, which will erode long-term goals like brand loyalty and lifetime value.

That’s why you need to get ahead of it:

1. Ask the right questions

Before you begin your AI journey, stop and ask: “Do we actually have what we need to do this right? Can we pull this off without putting the consumer experience and our brand at risk?”

Too many brands jump into AI because they don’t want to fall behind. But trying to keep up with the Joneses is a bad strategy.

Do you have the right customer data, integrations and governance to support a customer-facing AI use case without increasing bias? Do you fully understand your customers’ goals?

If the answer is no, or even “kind of,” you’re not ready.

2. Balance objectives

To effectively balance customer and business objectives, think about the customer’s needs as the goal, while your business objectives are the boundaries. Your AI should operate inside those confines, but aim for a customer-first outcome. You can also look at it as a balance between short-term and long-term thinking.

Short-term metrics, like revenue per interaction, are important. But they often conflict with long-term value. Even the “Godfather of AI” warned against AI driven by short-term profit, because that mindset doesn’t scale.

Your AI might hit its revenue target today, but are you willing to trade customer loyalty for a quick buck?

Consider the Delta example again. The strategy is technically smart and business-aligned. But consumers weren’t thrilled about the idea of shelling out more for airfare, and the brand took a hit.

Think in five-year timelines. You need to grow lifetime value slowly and sustainably.

3. Understand your customers’ evolving needs

Not just in general, but in each use case. What are they trying to accomplish?

If you don’t understand that, your AI will just be guessing. That’s why your customer profiles need to be current, complete and specific, both at a high level and on an individual basis.

Broad segments and outdated assumptions won’t cut it. You need data that represents the real person on the other side of the interaction. That will lead to a deeper understanding of the customer and form the foundation of your LLM training.

Retrieval-augmented generation (RAG) models also help here, pulling from curated, relevant data to give the consumer a better experience for the specific task they’re trying to perform.

But it’s not a one-and-done exercise. Customer goals shift and expectations change. Brands need to update their AI systems regularly to reflect the latest developments. That means revisiting training data and facilitating ongoing learning, not just fine-tuning outputs.

4. Scrutinize AI vendors closely

Not all vendors are created equally, and big promises don’t always mean big results. Choose partners with real-world expertise and a proven track record, not just flashy demos. Vendors with decades of domain-specific data can use it to better train models compared to a newer brand relying on generalized datasets.

Your customer may notice the difference in depth of data when they need specialized support.

And remember, if AI fails in the wild, your brand will suffer. Just ask the people impacted by the 2024 CrowdStrike outage. The average consumer didn’t blame the provider. They blamed the brands that deployed the tech.

Look for vendors who’ve done this before, in your industry, with your use cases. Domain knowledge beats ambition every time.

5. Build in governance

If you don’t define logic clearly and consistently, your AI will start making decisions based on patterns, not policies. Those patterns might not represent your brand, your values or your legal obligations.

Centralized orchestration and rule-setting are critical for making sure AI does what it’s supposed to—every time, across every customer interaction. Without this kind of governance, one model might handle a billing question one way, while another gives a totally different answer.

Stick to industry best practices and lean on risk management frameworks to safeguard the brand. Good governance won’t slow you down. It’ll save you from cleanup later.

6. Scope agentic AI with caution

The media makes it sound like agent-based systems are the future of everything. In reality, most brands aren’t ready, and that’s okay.

Since there’s not a lot of proof yet, start small. Partner with a technology provider who’s done it before and can guide you along the way. Prioritize low-risk workflows with clearly defined steps where the level of agency can be trusted—ideally, owned by a single team. These use cases typically have clear logic, accountability and oversight. Then you can learn and scale from there.

If multiple teams are involved or the process lacks structure, don’t expect machine decision-making to work for your customers.

To truly be successful, agentic AI requires access to a complete and current customer profile. Without real-time context, even the best models will produce disconnected, biased experiences.

Bias Is a Mirror, Not a Malfunction

AI doesn’t invent bias. It reflects what it’s told through data, training and business priorities. That’s why alignment matters. If your systems aren’t designed around the customer, AI will only widen the disconnect.

Objective bias can’t be fully removed, but it can be managed.

Make long-term loyalty your primary goal. the rest will follow. When every model decision is filtered through retention, lifetime value and trust, the rest of the priorities (governance, customer understanding, balanced objectives) naturally fall into place.

Shortcuts for today almost always cost you tomorrow, but proceed with loyalty at the heart of your strategy, and AI goes from a liability to an advantage.