As AI adoption increases across industries, companies are trying to transform the underlying data. Because we know that without reliable data, even the most advanced artificial intelligence is doomed to failure.
Many companies invest heavily in model development, but often overlook a fundamental and crucial problem: AI blindness. This term refers to organizations that do not evaluate whether their data is truly suitable for AI use, to people who blindly trust AI results, and to AI systems that ignore gaps and biases in data.
If left unnoticed, these gaps can lead to inaccurate results, poor decisions, and ultimately failed AI initiatives. Traditional data tools have not kept pace with innovation and many are ill-equipped to meet the specific needs of machine learning.
This creates trust gaps. In fact, our research shows that today only 42% of managers say they fully trust the insights generated by AI.
To solve this problem, companies need to ensure that they prepare their database to provide reliable AI information and recommendations. In a world where AI can help improve everything from customer experience to supply chain disruption, the cost of blindly relying on bad data is simply too high to ignore.
Why we should worry about AI blindness
AI initiatives often fail for a variety of reasons, including poor data quality, inefficient models, and a lack of measurable return on investment. Feeding incorrect data into AI systems produces incorrect results and reinforces biases. So if you can’t trust your data, you can’t trust your AI.
AI is becoming increasingly important to businesses and our research shows that 87% of business leaders now believe that implementing AI is critical to their business. As technology becomes an important decision-making tool, data errors can have serious consequences, from poor customer service to delivery delays or failed orders.
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Many companies assume that their data is “good enough” for AI to meet these needs, not realizing that there are hidden gaps: incomplete, inconsistent or outdated data.
To overcome AI blindness and identify gaps and biases, companies must create a database that is complete, consistent and delivered in as real-time as possible. Without this, companies jeopardize their decisions.
Traditional data tools are not enough for AI
To be truly useful, AI needs real-time, actionable, contextual data, and traditional tools are simply not designed to measure it. Legacy tools were designed for reporting, not machine learning.
As a result, they often lack AI-specific metrics to detect biased sources, outdated information, weak data line, or low training set diversity. Many of these issues don’t show up in dashboards, but they can still lead to biased or unreliable AI results.
To ensure AI insights are reliable and actionable, companies need a new layer of trusted intelligence in their data pipelines. Clearly defined parameters for diversity, speed and accuracy are essential. Only by laying this foundation and relying on the right data can AI scale effectively.
Companies should take steps to assess the suitability of their data for AI use. This gives them insight into AI-based metrics such as availability, completeness, timeliness and traceability, giving them deeper insight into the reliability of their data.
This comprehensive understanding ultimately allows them to be more competitive in the industry. Because data confidence analysis is a continuous check and not a one-time check, it enables dynamic, evolving evaluations as data changes.
What are the benefits of AI-based data?
Artificial intelligence has transformative potential if the data it is based on is used correctly. Companies need to be patient when implementing AI and not skip the step of making sure they have the most complete, reliable and up-to-date information.
When trust in data is built into every AI project from the start, companies can be at the forefront of AI implementation and unlock its full value.
Ultimately, using AI for decision-making starts with having the right underlying data. When companies can ensure the reliability of their data, they can create better models, make faster decisions and gain the lasting trust of their customers.
