Taking AI Products To Market: 4 Considerations For AI GTM

I have been taking data and AI products to market for almost 11 years. A lot is hiding under the surface that most data scientists and businesses only learn about through trial and error. The model is just one part of a larger puzzle that must be solved.

The problem is that data teams and the business focus on the technology problems surrounding data engineering and model development. Those are the two lowest-value workflows that data teams manage.

A trained model is not a product. OpenAI didn’t deliver an unprecedented model to the public. They delivered it with an unprecedented focus on peoples’ needs, accessibility, and ease of use.

AI go-to-market strategies must focus on the big picture. They must answer the question, why does the business turn models into products the way it does? AI GTM strategy informs decision-making about how models should be productized, commercialized, and monetized.

Monetization

The AI Last Mile problem is really a first-mile problem. Planning starts with the business, not the technology. Just because it is technically feasible doesn’t mean the company can make money with it. The business must build an opportunity discovery process to connect technical initiatives with core business strategy.

This was missing at McDonald’s and is one reason the data team there has been marginalized, even bad-mouthed by their CEO. The business doesn’t know how to connect the data team’s work to its strategic goals. Opportunities come in two flavors, so the business needs two processes.