Build vs. Buy: Why 80% of Internal AI Projects Fail

The Dream of the Own AI Team

It sounds tempting: your own team of data scientists, full control over the code, and tailor-made solutions. But the reality in August 2025 looks different. 80% of these projects never make it past the proof-of-concept stage. The reasons are varied, but mostly it is due to the underestimated complexity of the infrastructure.

The Hidden Costs of DIY

It's not the model that is expensive – it's the surroundings. Data pipelines, vector databases, RAG systems, monitoring, compliance. An internal team spends 90% of the time on 'plumbing' and only 10% on actual value creation. Meanwhile, competitors who rely on platforms are pulling ahead technologically.

Platforms as Accelerators

The market has consolidated. Platforms like Kivanto.ai offer enterprise-grade infrastructure 'out of the box' today. This means: immediate start, guaranteed security, and continuous updates. The question is no longer whether you can build AI yourself, but whether you can afford to be so slow.