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5 Common Mistakes That Risk Your Pharma AI Efforts

Published Date: Friday, May 31, 2024
Last Updated on: Friday, May 31, 2024
AI in healthcare

This article appears in Firece Pharma online.

By Kelly Waller

While it’s true that artificial intelligence can occasionally falter, the last thing we want to do is compound these errors with misinformed data strategies. The risks are significant, particularly in the context of AI engagement.

This can be a tall order as the global pharmaceutical industry races to adopt AI technologies for patient engagement. Generative AI alone could unlock $110 billion in economic value annually for pharmaceutical and medical product companies, according to a 2024 McKinsey & Co. report.

Indeed, AI tech can produce blockbuster results. Novo Nordisk, the maker of the Ozempic, uses AI to process large volumes of data for clinical trials. These insights could improve drug adherence by pinpointing patient motivations and compliance challenges.

However, the urgency to incorporate AI into an overall data strategy can cause pharmaceutical companies to miss potential hazards, resulting in patients receiving the wrong messages. In our experience, five strategic factors can make a company vulnerable to these mistakes.

The 5 Top Data Mistakes Pharma Companies Make In AI

Personalizing for drug patients is uniquely complex because it must adhere to industry-specific regulatory standards. A data architect can guide your organization through these standards while preventing missteps from derailing AI strategy.

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