Latest AI Articles:

When most professionals think about “AI consulting” they tend to think about technical machine learning services, like: Building our data infrastructure, crafting and testing new algorithms, interesting AI systems into existing IT infrastructure.

In the vast land of opportunities that AI creates, how do we select the projects that will generate ROI? Do we gain inspiration from reading AI use-cases relevant to our industries? Do we search through our own lists of existing priorities and hope the applications for AI will become clear?

AI adoption involves more than educating stakeholder groups (SMEs, IT, leadership) on the technical nuances of AI. It involves navigating human motives and incentives.

While overt AI "flops" are less common than they were three years ago, the pattern of failure for AI projects is still much the same.

You can invest in AI maturity and future capability - or you can have "quick wins" with surface-level AI applications that have relatively short-term, narrow ROI.

Picking first AI projects is challenging - and leadership is right to be wary of making the wrong investment. The challenge lies in both (a) identifying the right projects, and (b) ranking and determining the right ones.

This article is the third in a series part in a series about AI product development.

In the first installment in this series, we covered how to develop AI product ideas with both near-term adopt-ability and long-term potential.

So you've decided you want to take an AI product or service to market.

Before you sell anything - you'll have to decide what kind of product or service to develop.

Whether you're a startup or an enterprise, developing AI products is challenging.

Not only do you have to wrestle with the challenges of finding a use-case that where AI can actually deliver value into an enterprise workflow, but you also have UI concerns, and - often - much higher demands to monitor algorithmic drift and other technical issues.

In this article, I'll explore some of our lessons learned in getting the value of AI products or services to stick with enterprise buyers.