The real bottleneck holding AI back is rarely the model or the use case. It is data engineering infrastructure that was never built to scale.
Every business that runs on data faces the same challenge. The data that powers competitive advantage cannot be bought off a shelf. It must be harvested - with precision, at scale, and at speed. In 2026, most organisations are using AI to do that harvesting. Most of them are getting it wrong.
And yet, only 25% of organisations have moved 40% or more of their AI pilots into production.[2] The bottleneck isn't the model. It's not the algorithm, the vendor, or even the use case. In the vast majority of cases, it's the data engineering infrastructure underneath - fragmented pipelines, unstructured silos, absent governance, and architectures that were built for reporting, not for real-time intelligence.
