AI blindness is costing your business: how to build trust in the data powering AI
AI success hinges on trustworthy, bias-free data foundations
· TechRadarOpinion By James Fisher published 17 December 2025
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As AI adoption accelerates across industries, organizations are racing to transform the data that powers it. This is because we know that without trustworthy data, even the most advanced AI systems are destined to fail.
Many organizations are investing heavily in model development, but they often overlook a critical underlying issue – AI blindness. This term refers to organizations failing to assess whether their data is truly fit for AI use, humans blindly trusting AI outputs, and AI systems themselves being unaware of gaps and biases in the data.
James Fisher
Chief Strategy Officer at Qlik.
If these flaws go unnoticed, they can lead to inaccurate outputs, poor decisions and, ultimately, failed AI initiatives. Traditional data tools have not kept pace with the speed of innovation, and many are ill-equipped to meet the unique demands of machine learning.
As a result, trust gaps are appearing. In fact, our own research finds that only 42% of executives say they fully trust insights that are generated by AI today.
To overcome this, organizations must ensure they are putting in the work to prepare their data foundation to deliver trustworthy AI insights and recommendations. In a world where AI can help to power everything from customer experience to supply chain disruption, the cost of blind trust in flawed data is simply too high to ignore.
Why we should be worried about AI blindness
AI initiatives often fail for several reasons including poor quality data, ineffective models and lack of measurable ROI. Feeding bad data into AI systems leads to inaccurate outputs and reinforces biases. Therefore, if you can’t trust your data, you can’t trust your AI.
AI continues to grow as a priority for businesses, and our research reveals that 87% of business leaders now view AI execution as mission-critical. As the technology becomes a key tool for decision making, data flaws can lead to significant consequences - from customers receiving poor support to delays in shipping or orders not being met.
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