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General Electric (GE) was founded in 1889 by J.P. Morgan and Anthony J. Drexel who came together to finance Thomas Edison’s research and merge their companies together. Originally, GE was an industrial and consumer products company but today, more than 130 years later, GE has transformed itself into a multinational, digital industrial corporation ranked as the 33rd largest company in the United States by gross sales in 2020, according to Fortune 500.

Today, Coca-Cola is the world’s largest beverage company, selling over 500 soft drinks in more than 200 countries. In 2020, Coca-Cola had over 80 thousand employees worldwide.

As artificial intelligence makes its way into more industries and workflows, more and more non-technical team members will be charged with leading AI projects. The next wave of AI catalysts will be familiar with AI at a conceptual level (read: executive AI fluency), but will mostly be expert in bridging AI's capabilities to important business workflows and objectives.

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.