Ground Truth: The Foundation of AI—and Everything Around It

Take a moment to think about how AI works. Beneath the breakthroughs and buzzwords lies something far more foundational than neural nets or transformer models: ground truth.

It’s the benchmark, the reality check, the difference between a model learning and a model guessing.

And guessing, especially in high-stakes settings like healthcare, finance, or transportation, isn’t good enough. But the same is true for everyday tools. If people are relying on AI to help them write, decide, plan, or create, then the bar has to be higher. Trustworthy systems don’t emerge from guesswork.

At its simplest, ground truth means human-verified data. It’s the set of labelled examples that tells the system, “This is a cat,” or “In this case, ‘jaguar’ refers to the car, not the animal.” It’s what allows machines to separate right from wrong, signal from noise.

But here’s the critical point: only humans can provide ground truth. Why? Because only humans have context. Only humans can weigh ambiguity, interpret nuance, and make judgment calls. AI can learn from patterns—it’s what it does best—but it doesn’t understand them. Without human input, machine learning becomes machine assumption.

And if we all agree that ground truth is critical inside the model, why aren’t we applying the same standard to everything outside it?

The Truth Gap Around the Model

We obsess over training accuracy, benchmark datasets, and model performance. But what about all the decisions that surround the model?

The pitch deck. The pricing strategy. The product design. The UX flow. The value proposition. The marketing claims. If these aren’t also anchored in ground truth—in actual human needs, behaviours, and expectations—we’re back to building in the dark.

And it’s not just the model that needs grounding. The whole business does.

10 Human Ground Truths You Can't Ignore

We asked over 600 people across the US, UK, Canada, Australia, and New Zealand of people about their experiences, expectations, and hesitations when it comes to AI. What we found was revealing—not just about how AI needs to work, but how it needs to feel. These are the human truths your product, brand, and strategy should be built on.

1. Users are tuned into artificiality

In our research, 69% of users said they can usually tell when something was generated by AI. Whether it’s tone, texture, or timing, people are attuned to cues of authenticity. If it sounds robotic or flat, they notice—and it undermines trust.

Implication: Make naturalness a design goal, not a byproduct.

2. People are willing to pay for AI—but only if it delivers real value.

64% of users said they would pay for an AI tool that truly meets their needs. The demand is there, but users are discerning. Value must be consistent, relevant, and obvious from day one.

Implication: Don’t sell a product. Solve a problem people care about repeatedly.

3. Bias is a barrier to trust.

More than half of users (53%) expressed concern about bias in how AI tools make decisions or generate outputs. This isn’t just an ethical issue—it’s a commercial one. If people believe your system is unfair, they’ll disengage.

Implication: Fairness isn’t invisible. Show your work.

4. Poor UX is still holding AI back.

51% of users believe the user experience of AI tools needs significant improvement. Many tools are powerful but unintuitive. If friction outweighs function, even the best model will be left unused.

Implication: Build for real people, not just early adopters.

5. Trust must be earned, not assumed.

46% of users said they don’t trust new AI companies yet. People are open, but cautious. Early impressions matter. From onboarding to branding, trust must be actively designed into the experience.

Implication: Trust isn’t a mood. It’s a system of signals.

6. There's more demand for AI than usage reflects.

46% of users said they feel like they should be using AI more than they currently do. This isn’t apathy—it’s friction. People want help. They just don’t always know how to start, or where the value lies.

Implication: Reduce ambiguity. Build for habit.

7. Differentiation is getting harder.

44% of users said there are too many AI tools, and they struggle to tell them apart. In a crowded market, small features aren’t enough. Your product needs a clear point of view and a memorable purpose.

Implication: If your value proposition isn’t instantly obvious, it’s invisible.

8. Most AI companies understand technology better than they understand people.

Only 38% of users felt that AI companies truly understand their needs. Technical innovation alone isn’t enough. Products need emotional intelligence too.

Implication: Empathy is not a soft skill. It’s a strategic one.

9. Retention is a major challenge.

A full 72% of users said they’ve used an AI tool once or twice, then never again. This is the leaky bucket problem. Even high initial interest won’t save a product that lacks staying power.

Implication: Early value is critical. So is repeatable usefulness.

10. AI is being used in both life and work—and each context needs its own design.

64% of users report regular personal use of AI tools, and 56% use them for work. These aren’t interchangeable contexts. Motivations, constraints, and expectations differ.

Implication: Don’t blur use cases. Tailor for them.

Ground Truth Is a Business Discipline

Here’s the kicker: even the most technically perfect model can fail commercially if the world around it—your pricing, your launch, your messaging—wasn’t tested against reality.

Ground truth isn’t just for machine learning teams. It’s for product managers, designers, founders, marketers, and investors. It’s a mindset: evidence over assumption. People over proxies. Validation over guesswork. Because ultimately, it’s people—not the model—who decide what works and what doesn’t.

And if your model is guessing, and your business is too, then it’s not just the AI that needs retraining.◼️

Connect with Protocol Theory

Protocol Theory is attending SuperAI in Singapore from June 18th - 19th. If you’re attending want to meet, schedule a call. We’d love to connect and talk about the future of AI, culture, and consumer behavior.