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.
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.
Many organizations assume their data is ‘good enough’ for AI to be able to meet these requirements, without realizing the hidden gaps – data that is incomplete, inconsistent or outdated.
To overcome AI blindness and identify gaps and biases, businesses must build a data foundation that is instead complete, consistent and can be provided as close to real-time as possible. Without this, organizations are taking a gamble on the decisions they make.
Traditional data tools aren’t enough for AI
To be truly valuable, AI requires context-aware, real-time and fit-for-purpose data, and traditional tools simply aren’t designed to measure that. Legacy tools were built for reporting, not for machine learning.
As a result, they often lack AI-specific indicators to flag biased sources, outdated information, weak data lineage or poor diversity in training sets. Many of these issues don’t show up in dashboards but can still lead to biased or unreliable AI outputs.
To ensure that AI insights are reliable and actionable, organizations need a new layer of trust intelligence across their data pipelines. Clearly defined parameters for diversity, timeliness and accuracy are essential. Only when these foundations are in place, and AI is built on the right data, can it scale effectively.
Organisations must take steps to assess their data’s readiness for AI use. By doing so, they will gain visibility into AI-aligned metrics such as readiness, completeness, timeliness and traceability, providing deeper insight into their data’s trustworthiness.
This well-rounded understanding ultimately enables them to be more competitive in the industry. Since data trust analysis is continuous rather than a one-time audit, it allows for dynamic, evolving assessments as data changes.
What are the benefits of AI-powered data
AI holds transformative potential – if the data powering it is done right. Businesses must be patient when implementing AI, and not skip the step of ensuring the most complete, trustworthy and timely data is feeding it.
If data trust is built into every AI project from the outset, businesses can stay ahead of the curve when implementing AI and unlocking its full value.
Ultimately, using AI to inform decision making starts with having the right foundational data. If businesses can ensure their data is trustworthy, they’ll build better models, make faster decisions and earn lasting confidence from customers.
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