The pharmaceutical industry operates under considered one of the very best failure rates of any business sector. The success rate for drug candidates entering capital Phase 1 trials—the earliest variety of clinical testing, which may take 6 to 7 years—is anywhere between 9% and 12%, depending on the 12 months, with costs to bring a drug from discovery to market starting from $1.5 billion to $2.5 billion, in keeping with .

This skewed balance sheet drives the pharmaceutical industry’s seek for machine learning (ML) and AI solutions. The industry lags behind many other sectors in digitization and adopting AI, but the fee of failure—estimated at 60% of all R&D costs, in keeping with —is a vital driver for firms trying to use technology to get drugs to market, says Vipin Gopal, former chief data and analytics officer at pharmaceutical giant Eli Lilly, currently serving an identical role at one other Fortune 20 company.

“All of those drugs fail resulting from certain reasons—they don’t meet the factors that we expected them to fulfill along some points in that clinical trial cycle,” he says. “What if we could discover them earlier, without having to undergo multiple phases of clinical trials after which discover, ‘Hey, that doesn’t work.’”

The speed and accuracy of AI may give researchers the power to quickly discover what’s going to work and what’s going to not, Gopal says. “That’s where the massive AI computational models could help predict properties of molecules to a high level of accuracy—to find molecules that may not otherwise be considered, and to weed out those molecules that, we’ve seen, eventually don’t succeed,” he says.