Let me be upfront: I was the technical co-founder of an AI startup and it failed.

PharmaForesight was an AI startup in the pharmaceutical business intelligence industry. Here was our elevator pitch:

“The rate of return for pharmaceutical companies on their R&D is currently below their cost of capital — therefore it is becoming less profitable for pharmaceutical companies to invest in innovative drugs. To decide what clinical trials to conduct, the likelihood of approval is a crucial metric which is currently being calculated in a very subjective and biased way. Our AI algorithm can estimate this figure much more accurately, saving time, money and ultimately benefits patients.”

PharmaForesight failed despite following much of the best practices associated with startups.

We had a strong team and iterated fast using a lean startup strategy. We conducted just shy of 100 interviews with a variety of different stakeholders to identify the early adopters and validate demand for our product. After only four months, we partnered with the global portfolio management office of a large pharmaceutical company that paid us to build our model and we retained all of the IP.

But ultimately, things didn’t work out—due to some bad luck but also poor judgments. AI startups require subtly different strategies and approaches to Software-as-a-Service (SaaS) startups — these aren’t widely appreciated. My aim in writing this article is to tell you about our mistakes so you don’t make the same ones.

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