Manufacturing Intelligence Episode 1 - Part 1: The First Step in AI Use in Pharma Manufacturing

The integration of AI in pharmaceutical manufacturing has become a critical focus, as evidenced by its prominence at events like PDA Week 2026. Despite this high interest, many industry professionals find themselves in a position in which they understand that they should be using AI but are unsure of the practical starting steps or how to implement it effectively.

According to Rick Jaenisch, the first essential move is a critical evaluation of one's workflow to determine if AI integration offers a genuine benefit to specific tasks. This involves analyzing every step of a process to see where the technology can be realistically put into play.

Currently, many professionals limit their AI usage to basic "Copilot" functions, such as email summarization. Jaenisch tells Christopher Cole that these are low-value trades; once such tools become standard, the baseline for efficiency simply shifts for everyone, providing no unique advantage. Moreover, AI is often "mid" at searching and lacks the ability to summarize verbatim consistently. Because users generally cannot control technical parameters like temperature or p-value, AI may produce complex, unwanted outputs—such as a 40-page essay when a user only needs a simple list of valves.

Jaenisch also cautions against "work slop," which refers to presenting AI-generated materials, such as presentations or reports, without human oversight or effort. He maintains a general rule: if you would not want to receive low-effort content, you should not produce it.

To bridge the gap between hype and reality, Jaenisch recommends specialized education tailored for life sciences. Learning what not to prompt is as vital as the prompting itself. Ultimately, AI must be treated as a focused tool used with specific intent to meet business goals.