Why most GenAI PoCs fail.
Why do most GenAI proof-of-concepts fail? Last week, I had the privilege of sharing our experiences with Swiss companies on #GenAI PoCs at the HSLU – Artificial Intelligence and Machine Learning AI Industry Event. GenAI projects often fail to progress beyond the PoC stage due to: 1- Insufficient answer quality. 2- Lack of added value to justify development and operational costs. 3- Limited resources or skills to operate and maintain the application. In a recent project, we spent only 8% of the time generating the first functional answer (including setting up the dev environment, indexing data, deploying the #RAG pipeline, etc.). However, 92% of the time was dedicated to refining and "perfecting" the generated output. One of the biggest challenges in Retrieval-Augmented Generation projects is defining what constitutes a “perfect” answer. Without a clear and agreed-upon objective, optimization efforts can become directionless and inefficient. To address these challenges, we’ve developed features like fact-checking mechanisms and a confidence level monitor to ensure accurate and reliable answers. Our goal is to build user trust and confidence in the system — while striving for full automation to eventually eliminate the need for human intervention. Are you also working on GenAI applications? Let’s connect and discuss! Donnacha Daly Deniz Gunaydin-Bulut Francesco Crivelli Marija Nikolic, PhD HSLU – Hochschule Luzern – Informatik Switzerland Global Enterprise Digital Switzerland