Why do AI projects keep failing in finance?

Why do AI projects keep failing in finance?

Hint: It’s not about the technology.

What the Numbers Say

Gartner (2024): 85% of AI projects in financial services fail due to poor data quality, fragmented workflows, or lack of defined business logic.

The Real Challenge

The financial services industry are full of ambition when it comes to AI — and rightfully so. From fraud detection to predictive analytics, AI can revolutionize everything. But ambition alone doesn’t get results.

Most AI projects stall not because of bad algorithms, but because they’re built on shaky foundations. Legacy systems, undocumented processes, siloed data — it’s chaos under the hood. When you introduce AI into that environment, it doesn’t create clarity. It magnifies confusion.

Many firms leap straight into machine learning models or LLM pilots without fixing the basics: how decisions are made, how data flows, and how outcomes are tracked. They want an intelligent assistant, but haven’t built the workflows or decision trees that assistant needs to follow.

At Nxel, we believe that scalable AI outcomes depend on structure. Before you launch the AI rocket, you need a strong launchpad — and that launchpad is workflow automation powered by codified business logic.

Our Perspective

“AI is the engine. Automation is the operating system. You wouldn’t install a race car engine in a rusted frame.”

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We work with financial institutions to build structured, scalable systems that unlock real value from automation and AI. If you're facing process challenges or considering transformation, let's talk.