Is Legacy Infrastructure Quietly Killing Your AI Investment?
Enterprise AI investment has never been higher. Global organisations collectively deployed $684 billion on AI initiatives in 2025 alone.[1] Boards have been briefed. Strategies have been approved. Vendors have been selected and pilots have been launched. By 2026, 78% of organisations describe AI as central to their future competitive strategy.[2] The direction of travel is not in doubt.
And yet, the results tell a different story. More than 80% of all AI projects fail to deliver their intended business value.[3] 95% of generative AI pilots fail to scale beyond proof of concept.[4] Gartner warns that through 2026, organisations will abandon 60% of AI projects specifically because their data isn't AI-ready.[5] The problem isn't the model. It isn't the algorithm. It isn't even the use case. The problem - in the vast majority of cases - is the architecture underneath. Monolithic applications, fragmented data stores, batch-oriented pipelines, and opaque legacy codebases are quietly killing AI initiatives that were well-conceived and well-funded. They just weren't built on foundations capable of supporting them.
