Only 1 in 5 AI projects make it to production.
You've seen the pitch deck.
AI will automate your operations, cut costs by 40%, give you superhuman insights, and transform your business overnight.
So you hire consultants, build a proof of concept, and the demo works perfectly.
Then you try to deploy it in production. Everything breaks.
80% of AI projects fail after proof of concept.
Smart leadership, decent budget, good intentions; but they still burned through 6 figures on AI and got nothing.
This wasn't some startup making rookie mistakes. This was a successful company with experienced engineers and proper funding. They followed all the "best practices," hired a big-name consulting firm, spent months on research and planning, and built impressive prototypes.
Sound familiar?
At Adyen, we find ourselves at a fascinating crossroads of technology, captivated by the immense potential of Large Language Models (LLMs). What sparked our exploration was a question that reflects curiosity and a strategic vision:
How can we use LLMs, traditionally known for generating text, for a more structured task like code generation and, more specifically, for augmenting the process of creating unit tests?