Embracing AI When Your Industry Is in Flux
One of the great challenges we have seen businesses face in recent years is how they approach data and analytics (and now artificial intelligence) when their industries are undergoing major transformation. It’s hard enough to create a data-driven culture, compete on analytics, develop data-driven products and services, and so forth under normal business conditions, as we noted in our March column about the newest NewVantage Partners survey on big data and AI. But doing it while your business and industry are transforming — the old line of changing out a jet engine while the plane is flying through turbulence at 35,000 feet — is really tough.
It’s so difficult, in fact, that we always have our doubts when executives claim to have done it successfully. We are much more trusting when we’re told that the organization is simply making progress toward the goal.
“Making progress” is what executives told us at Parexel International, one of the world’s leading clinical research organizations, or CROs. Dr. Sy Pretorius, the company’s president, clinical development and chief medical officer, shared that Parexel is attempting to take an aggressive approach to artificial intelligence, but he also added, “I’m not sure if we have enough going on with AI.” That honesty made what he told us about adopting AI while dealing with enormous industry transitions even more convincing.
A Time of Dramatic Industry Change
The industry changes that Parexel and its competitors are experiencing are dizzying. CROs have historically focused on conducting clinical trials for pharmaceutical companies, but an array of new strategies and business models are moving the industry in a more data-driven direction. Let us count (some of) the ways:
New imperatives to process real-world evidence. Clinical trials used to all be conducted in the same way, using the classic randomized, double-blind, placebo-controlled “gold standard” methodology. But pharma firms and CROs are increasingly using new approaches to trials in which everyone gets the experimental treatment, and those patients are compared to a synthetic arm of patients with the disease who are receiving the standard of care for it. This means that the conductors of the trial need to obtain and analyze real-world evidence from sources like electronic medical records to analyze how the synthetic arm patients are doing. As Pretorius noted, “Once health care data can be used directly to build the clinical data set for safety and efficacy analysis, fewer patients will need to be enrolled and randomized, reducing the total duration of the study and the logistical burden on patients.” But the analytical and data management burden on the CRO is increased.