Going from Pilot to Deployment with AI – 4 Factors to Consider

Many books could be written on the subject of AI strategy, and we've seen that "strategy" means something different from one enterprise to the next. This article is a brief overview of the common steps in creating an AI strategy - in roughly the order that the steps are usually executed.

In the 1990's, Ben Horrowitz described a product manager as follows:
"A good product manager is the CEO of a product."
That definition isn't always a perfect fit, but it can be a good way of summarizing the responsibilities of a product manager; they are wholly in charge of bringing an in-house product from inception to generating an ROI. 

Making AI work has a lot to do with "getting things right" even before a project starts, including:

In the classic business book Good to Great, author Jim Collins talks about the different approaches for technology adoption between high-performing and average companies. Collins' research indicated that high performers tend to adopt technology as an accelerant to an existing, working strategy - while underperformers tended to adopt technology in an attempt to jumpstart a change in direction or strategy that they haven't yet undertaken.

Over the last four years, interviewing hundreds of AI researchers and AI enterprise leaders, we've consistently heard the same frustrations about AI adoption said time and time again.
"Culture is hard to change."
"Leadership doesn't know what they're trying to accomplish."
"Nobody knows what to do with these data scientists we've hired."
etc...
In our one-to-one work with enterprise clients, we've taken the most prevalent, recurring challenges to AI deployment and put them together into a framework of "prerequisites" to AI deployment.