Certainly, the Inflation Reduction Act will continue to play out as it relates to pricing and reimbursements, as well as potential impacts to exclusivity. But putting aside regulatory impacts for a moment, the most consequential change I see is the democratization of AI. AI in and of itself has been put into practice by companies for years, but now there’s this ever-expanding canvas upon which to apply it. As a result, over the next few years, we will see an acceleration of AI-driven analytics and product development across the entire clinical and commercial ecosystem.
Pharma, like many industries, has been awash with data, but it’s been siloed. This data came from an array of sources –– public, private, third-party, one-off, syndicated, and/or derived from a mix of all. Data abundance isn’t the problem. But a lack of concrete answers with use-case relevant specificity comes up again and again in my discussions with clients. In other words, don’t just give me data, tell me how I should be thinking about it.
Just as when search engines first organized the internet, giving us access to the entirety of the world’s content, we can now take that content and decipher it faster and more accurately than ever before. That can mean augmenting proprietary data assets built over decades with unstructured content generated in real time to extrapolate a deeper context. It means taking a very complex display of information and, in natural language, explaining to a leader who doesn’t necessarily swim in that data every day. In practical terms, it means supporting a higher likelihood of success on the first attempt, such as in protocol designs or commercial performance forecasts.
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