The hidden costs of bad data: Why AI needs data integrity

AI is only as good as the data it learns from. Without a strong data integrity foundation, AI models risk reinforcing biases, misinterpreting trends, and making flawed predictions — ultimately leading businesses down costly and damaging paths.

AI and the problem of self-perpetuating biases

AI models don’t just consume data once; they continuously learn from it. If an error or bias exists in the data pipeline, AI models will reinforce it repeatedly.

Imagine a company using AI to recommend which product a salesperson should pitch to a potential customer. If the AI is trained only on past sales data, it might conclude that the oldest product — simply because it has been sold the most — is always the best option. This seems harmless at first, but in practice, it means the company struggles to launch or sell new products, hindering innovation.

Without intervention, data biases like these become deeply embedded in AI decision-making. And as AI adoption accelerates, organizations are increasingly skipping critical steps in verifying the quality of the data being fed into their models.

The three universal data challenges

1. Data is always increasing

With the explosion of digital transformation, businesses are generating and storing more data than ever. Yet most of this data isn’t properly validated before being used in AI models.

2. Data is always moving

Data flows through multiple systems — Salesforce, SAP, data warehouses, third-party vendors — before reaching AI training pipelines. Each transformation introduces risks of corruption, loss, or misinterpretation.

3. Data is always changing

Updates to applications, changes in APIs, schema modifications, and network infrastructure upgrades all introduce new variables that impact data quality.

The complexity of modern data ecosystems means that traditional “stare and compare” testing methods are no longer sufficient. Businesses need automated, end-to-end data integrity solutions to ensure their AI models are built on a solid foundation.

Data integrity: The AI readiness factor

So what’s the solution? The key is to shift from a reactive approach — only addressing data issues when they cause business disruptions — to a proactive, automated strategy that validates data at every stage of its journey.

Tricentis Data Integrity provides a structured approach to catching errors before they impact AI models.

Here’s how:

• Continuous, automated data integrity testing ensures that data pipelines remain accurate and reliable, even as systems evolve.
• End-to-end visibility across data transformations ensures that errors aren’t introduced during ETL processes.

The cost of bad data in AI

Bad data doesn’t just slow down AI innovation — it leads to real financial losses. Analyst firms estimate that poor data quality costs businesses trillions of dollars annually. Whether it’s AI models providing incorrect recommendations, regulatory fines due to inaccurate reporting, or wasted resources on debugging faulty training data, the risks are enormous.

Organizations can no longer afford to treat data quality as an afterthought. AI and ML initiatives will only succeed if they are built on trustworthy, validated data. The risk of poor data quality permeating your AI/ML initiatives or others is too high to not incorporate a data testing methodology.

Future-proofing AI with data integrity

At Tricentis, we’ve seen firsthand the challenges businesses face when AI models go wrong. The companies that succeed in deploying AI effectively aren’t just those with the most data — they’re the ones with the best data.

Before training your next AI model, ask yourself: Do I know for sure that my data is clean, complete, and free from bias? If the answer is no, it’s time to prioritize data integrity.

To learn more on how to prepare your organization for the AI/ML journey, register for this upcoming webinar.