Critical notes for both founders and investors before getting into the AI game

For those that don’t know, my work focuses on the business/financial and operational aspects of startups. I help founders build sustainable businesses based on their innovations and I help investors assess the potential business/financial applications of innovation.

My latest project has taken me down the rabbit hole of the world of AI, and I wanted to share with you a summary of all my findings.

Specifically, I wanted to share with you four key considerations for making AI sustainable as an actual business case—both for founders who are developing products and for investors who are funding them.

Let’s get started

I’m going to be blunt here. I fear AI’s hype. I’ve been exposed to too many situations of “AI for X,” where founders are using the latest buzzword haphazardly in their decks, and VCs are throwing money at bad ideas based on them.

What follows are four “musts” any startup needs to have to be tenable when dealing with AI. These are items that must be confirmed beyond the tech, the team, and the extensive math.

Founders, if these things aren’t solved or figured out, go back to the drawing board.

Investors, if these things aren’t solved or figured out, reconsider.

(I use the term AI here as a catch-all for machine learning, deep learning, neural networks, cognitive computing, etc.)

1. The use case must be explicitly defined

Two young men approached me some time ago for help with their startup, building AI for the HR/staffing industry. Our conversation goes as follows:

Me: So what aspect are you looking to improve?

Them: What do you mean?

2. Specified, proprietary data

3. The end user/last-mile solution must be built

4. Secured buy-in from end users