Summary: Our recent series of articles on AI strategies shows the options available for the strategic direction of your AI-first company. Here are some thoughts on moving from strategy to implementation, including some useful tools to help in planning.
Hope you’ve been following our latest series of articles describing and comparing the four major strategies for AI-first companies. Now that you’re better equipped to pick a strategy, we offer a few thoughts on moving from strategy to implementation.
To start with, we need to clarify what we mean by AI-first companies. The confusion as usual comes from what exactly you mean by AI.
My favorite quote on this topic:
If you’re talking to a customer it’s AI.
If you’re talking to a VC it’s ML.
If you’re talking to a data scientist it’s statistics.
That about summarizes the lack of clarity around the topic. Basically it depends on who you’re talking to. Personally, when I’m talking to a professional audience of data scientists I go for the more restrictive (and accurate) definition that AI is a subset of ML, the set of techniques based on deep learning and reinforcement learning.
But to make this particular article a little more accessible to the entrepreneurs who may be considering AI-first startups, I’m going to make an exception and use the broader definition (credit Louis Dorard for this particular formulation).
Read full article by Bill Vorhies, here.
