Are We Building AI on Sand?
What if the biggest risk to your GenAI strategy isn’t the model you choose, but the data you feed it?
Organizations are racing to adopt generative AI, investing heavily in tools, pilots, and transformation initiatives. Yet, beneath this surge lies a stark reality: a recent MIT study found that nearly 95% of GenAI pilots fail to deliver measurable business impact.
That’s not a technology failure. It’s a foundation problem.
GenAI doesn’t fail because it’s incapable, it fails because it’s built on fragmented, low-quality, or inaccessible data. In many cases, organizations are effectively deploying cutting-edge intelligence on top of outdated, siloed, and inconsistent data ecosystems.
The result? Impressive demos, but disappointing outcomes.
