Crossing The Trust Threshold: When Quality Becomes Imperative in AI 

Over the past couple of months I’ve spoken to dozens of data teams who are actively building and deploying AI applications. While some of these applications can thrive without perfect accuracy, others demand high reliability as scale, visibility and business impact increase. This post explores the patterns that drive when and why trust becomes an imperative. 

There has long been a concept in reliability engineering that you should design for “as much reliability as you need and no more.” But for any production-ready application of value to the business, the answer to “how much” is never “nothing at all”. Conversely, if your application requires zero investment in reliability, I could make an argument that it’s worthless.

So I’ll define the “trust threshold” here as the point in your application’s lifetime where the cost of poor quality exceeds acceptable limits. This threshold is crossed due to four main factors: 

• Scale: As the system touches more users or business processes, small inaccuracies multiply into big problems.
• Visibility: Outputs for external customers typically demand the highest accuracy and reliability, followed by internal stakeholders, small groups of experts, then prototypes for personal use. 
• Regulatory/Compliance Risk: Certain use cases are inherently high-stakes due to legal or ethical obligations.
• Business Value: As a model’s output drives critical decisions or revenue, the cost of errors increases exponentially.

Typically, I am seeing teams cross this threshold after a successful proof-of-concept, where they’ve proven they can build something of value to the business, and are now scaling it to enough users where the absolute quality or dips in reliability of the application become noticeable.

And across the spectrum use cases, when exploring the question of how AI teams are ensuring trust and quality, the most prevalent response is human-in-the-loop evaluation. In some instances this is by design, a feature that allows for the right balance of automation and human judgement. 

But in other cases, especially when a certain scale or visibility is achieved, human-in-the-loop becomes necessary but insufficient to ensure the ongoing reliability and trustworthiness of the application. 

Patterns in Practice

As many data leaders are navigating what it means to have “AI-ready data”, the reality is that it depends on the needs of the applications you intend to build.  

We’ve seen public examples of customer chatbots gone awry, making it clear that any data supporting these applications must be secure, accurate and trustworthy as soon as you begin exposing the application to customers.  

I’m seeing leading data teams across a variety of industries take business process automation to the next level – with faster, more efficient processes aimed at image or document processing, as well as regulatory testing; work that now requires human oversight rather than human labor. In these cases the business value is already apparent, so the expectations on reliability are high – downtime can mean $ or non-compliance. 

The next class of use cases are often launched with more of a “test & learn” approach – product personalization, internal chatbots, decision support tools. In many cases, data leaders have told me these are net new capabilities, so the internal expectations are initially low. 

Media & technology companies I spoke to often raised the importance of brand safety in content personalization features, with some talking about features that did not meet user expectations, the most critical trust threshold for them. Ultimately, these creative personalization features are judged based on user response (e.g. thumbs up or down) and adoption. And once they are adopted, they’d better be reliable. 

Internal chatbots or decision support applications may initially skate by as “better than what we had before” but more often than not I’m hearing that failure is correlated with poor quality knowledge bases and metadata. Successful implementations are held to a high standard by their owners and consumers, with a need to ensure those knowledge bases are up-to-date and complete. 

At the lower end of the spectrum on trust expectations are the more opportunistic use cases – creative data practitioners who have generated entirely new datasets that are used for business insight, or identified a place where they could rapidly summarize customer call transcripts to deliver themes or segment their audience. These are typically offline exercises (for now) and their internal customers are invariably stoked for the unprompted support!

In time, I’m sure they’ll hit a threshold where someone asks them if the latest round of insights can be trusted. 

Recognizing the Trust Threshold

To identify whether your AI initiative is approaching the trust threshold, ask yourself, your team and your stakeholders the following:

Ask the Right Questions:

• What are the consequences of poor-quality outputs?
• Who is the audience, and how sensitive are they to errors?
• How does scale change the risk profile?
• Are there regulatory or ethical implications to consider?

Early Warning Signs:

• Increased error feedback from key stakeholders.
• Slow adoption despite technical accuracy.
• Hesitation to automate due to trust concerns.

How Data Teams Can Stay Ahead

Crossing the trust threshold isn’t a matter of it, but when. Leading teams are taking proactive steps to manage this transition effectively. 

• Proof-of-concepts & experiments: The fastest way to AI-ready data is to make it a necessity. Running small-scale experiments helps you identify and resolve quality gaps early, making production readiness a natural next step. 
• Prioritize human-in-the-loop systems: Involving humans at key decision points not only mitigates risk but also aligns with what users expect—a balance of automation and human judgment. Human review becomes both a quality gate and a source of continuous feedback to improve the system.
• Build feedback loops: Implement systems that capture and respond to user input over time. One of the simplest and most effective methods I’ve seen is a thumbs-up/down mechanism for end users to rate the output’s quality. This data provides valuable insights to fine-tune models and build trust progressively.
• Evaluate the required level of precision: Not every application requires the same degree of accuracy. Align model performance with business needs—whether it’s 99.9% accuracy for highly regulated use cases or “good enough” for internal brainstorming tools.
• Implement observability: You can’t improve what you can’t see. Track the data, system, code, and model to identify where failures occur and monitor the system’s ongoing reliability. Observability is foundational for maintaining trust as AI applications evolve.

Our promise: we will show you the product.