PharmTech AI Pulse Check: Fixing AI Slop at the Source

AI-generated "slop" has become a fixture of daily inboxes, but is it creeping into pharmaceutical manufacturing and quality documentation? That's the question at the center of Part 1 of the debut episode of the new PharmTech AI Pulse Check series, in which host Christopher Cole is joined by Richard Jaenisch, Senior Director of Education, Outreach, and Digital Experience at OpenBiopharma Research and Training Institute; Florin Muraru, an independent regulatory affairs executive with more than 20 years of experience; and Gourav Pandey, quality and CMC risk and compliance specialist at Takeda, unpack where AI-generated content is showing up in SOPs, deviation reports, and literature summaries, as well as who's responsible for catching it before it reaches a regulator.

Jaenisch argues that deviations have become generative AI's most common entry point in manufacturing, largely because form filling is low effort and tempting to automate. But he cautions that speed can come at the expense of accuracy: "Well, you just made a slop factory." The real risk, he says, isn't the technology itself but whether operators, especially newer, less-seasoned staff, are trained to recognize which content is fit for an SOP and which isn't.

Muraru reframes the problem as one of authorship rather than tooling. In his view, AI slop isn't about the model but the person behind it, and the danger emerges when documentation is generated faster than it can be meaningfully reviewed, shifting the real bottleneck from review capacity to the presence of an engaged human in the loop. He notes that polished, fluent AI output can actually be harder to audit than an obviously rough draft, since errors are easier to spot in something that already looks imperfect. He also points to the FDA's April warning letter involving Purolea as an early real-world example of the stakes.

Jaenisch pushes the discussion further upstream, distinguishing between what he calls first-degree slop—content a human directly reads, edits, and signs off on—and second-degree slop, where a person trusts an AI tool's advice without independently verifying it, and that flawed judgment quietly propagates through decisions and conversations. Pandey argues the fix isn't more scrutiny at the review stage but better-structured source documents that clearly separate deterministic rules from areas requiring human judgment: "You don't fix the slop by reading harder or putting on more reviewers." Doing so, he says, lets AI genuinely help rather than generate polished documentation that still requires heavier reviewer and approver attention.

Across the conversation, the panel converges on a shared concern: GxP-regulated environments can't rely on AI-generated content "looking right" as a proxy for being right, and that training, sourcing discipline, and a habit of asking "where did you verify that from?" will matter more as generative tools become embedded in manufacturing and quality workflows. Watch the full episode for the panel's complete discussion of where AI slop is most likely to surface next and what pharma companies can do now to get ahead of it.