Artificial intelligence is receiving huge investments, but many organizations are failing to capitalize on its potential.

The problem of data quality

The main problem does not lie in the AI technology itself, but in the difficulty of identifying and managing truly relevant data. AI amplifies what it is fed, including the confusion arising from incorrect or irrelevant data. This leads to teams overwhelmed by an excess of information and results below expectations.

Scaling the confusion

Instead of improving decision-making processes, AI can accelerate the spread of errors if fed with poor quality data. Companies must therefore focus on cleaning, validating and accurately selecting data before implementing it in AI models. A data-centric approach is essential to obtain real value from artificial intelligence.

For those evaluating on-premise deployments, there are trade-offs to consider. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these aspects.