Making AI real
“There is still an issue of translating this technology into real, tangible economic benefit,” argues Forrester senior analyst Dario Maisto. I’ve definitely seen this in my work running developer relations at MongoDB. I don’t spend time with the executives telling Wall Street how AI will transform their businesses, as has been commonplace on corporate earnings calls. Instead, I work with the developers tasked with turning dreams into reality.
As I wrote in June 2024, most companies seemed to be succeeding with smaller-scale retrieval-augmented generation (RAG) investments. This makes sense given the relative immaturity of the industry. To do AI well, you not only need to get your data in shape, you also need experienced employees. And even if LinkedIn is telling you that your job candidate was a low-level data analyst last year but now has flowered into an experienced data scientist, the reality is different. Most people are far better at positioning themselves as AI experts than actually demonstrating the requisite background in artificial intelligence and machine learning.
As such, it’s perfectly appropriate for a company to start building up AI muscle with RAG applications or other table-stakes workloads. That’s where you’ll also begin to develop your employees. You have to start somewhere, and, with a Deloitte study finding enterprises new to AI get just 0.2% returns on their AI investments, it’s best to start now, even though the real payoff may come much later.