Why Your AI Project May Fail

This is true, if you don't have ready understanding/access into the local architecture, its much harder to get the data to train models in context.    And certainly also very hard to implement them into any sort of an deployed  model.  This is true whenever you hope to get anything used by a client, no necessarily just an AI project though.   To e clear though, you usually understand this fairly early on.   And people who are already there will usually tell you

The Dumb Reason Your AI Project Will Fail
by Terence Tse , Mark Esposito , Takaaki Mizuno and Danny Goh  in the HBR

Here is a common story of how companies trying to adopt AI fail. They work closely with a promising technology vendor. They invest the time, money, and effort necessary to achieve resounding success with their proof of concept and demonstrate how the use of artificial intelligence will improve their business. Then everything comes to a screeching halt — the company finds themselves stuck, at a dead end, with their outstanding proof of concept mothballed and their teams frustrated.

What explains the disappointing end? Well, it’s hard — in fact, very hard — to integrate AI models into a company’s overall technology architecture. Doing so requires properly embedding the new technology into the larger IT systems and infrastructure — a top-notch AI won’t do you any good if you can’t connect it to your existing systems. But while companies pour time and resources into thinking about the AI models themselves, they often do so while failing to consider how to make it actually work with the systems they have.

The missing component here is AI Operations — or “AIOps” for short. It is a practice involving building, integrating, testing, releasing, deploying, and managing the system to turn the results from AI models into desired insights of the end-users. At its most basic, AIOps boils down to having not just the right hardware and software but also the right team: developers and engineers with the skills and knowledge to integrate AI into existing company processes and systems. Evolved from a software engineering and practice that aims to integrate software development and software operations, it is the key to converting the work of AI engines into real business offerings and achieving AI at a large, reliable scale.  ... "