AI in Biopharma Manufacturing: Are We There Yet?

by Jack Prior

Are we there yet? Jack Prior is a bioprocess engineer who has spent his career in manufacturing science and biopharma manufacturing, using process data and process models to monitor, understand and control process variation — for yield improvement, tech transfer and troubleshooting. He began in the late 1980s at MIT looking at how to apply artificial intelligence to bioprocessing, perhaps a little too soon; forty years on, in 2026, the time for AI seems finally to have arrived. The promise has never been greater, and neither have the pitfalls and the challenges. The question this podcast asks is: are we there yet — in our data readiness, in our technology, and in our regulatory frameworks? Season 1 is an experiment. There are at least forty regulatory guidances and white papers to understand, so Jack uses AI to work through them in a narrative format, one question per episode, sized for a walk. The voices are AI characters, not Jack; the views are not his or his employer's, and none of it is regulatory advice. Each episode is fact-checked, claim by claim, against the source documents, with the results in the show notes. Researched, scripted and voiced by AI systems under Jack Prior's direction. Sam and Sarah are AI characters; nothing in the episode is Jack speaking, and none of it is a statement of his views or his employer's. Generative AI can be confidently wrong — check the sources. Corrections: jack@jackprior.ai.

Website

Latest episodes

Showing 10 · updated from the feed

Convergence: Where Is This Going — and Are We There Yet?

A site head wants one slide by Friday: four AI systems, three columns, do now, wait for, do anyway. Sam and Sarah close the season by reading the two documents that say w

Sep 6
44 min

Change: What Happens When the Model — or the Process — Changes?

A vision vendor pushes a new model version at two in the morning and a language-model provider deprecates the version a site validated. Sam and Sarah split 'change' into

Sep 6
40 min

Humans: Who Is Accountable When the Model Is Wrong?

Every document that lets AI into GMP lets it in on one condition: a human in the loop. Sam and Sarah read what that phrase actually means across six documents, anchored o

Sep 6
36 min

Data: Is My Data Fit for Use?

Every framework this season assumes the data under the model is trustworthy. Sam and Sarah read what the rulebook actually requires: ALCOA and its pluses from FDA's 2018

Sep 6
35 min

Evidence: What Do I Have to Show?

Once a model is allowed through the gate, what do you owe on paper? Sam and Sarah read FDA's 2025 draft for its credibility assessment plan and report, sized to model ris

Sep 6
39 min

Model Type: Can It Learn After Deployment — and Can It Be an LLM?

For twenty years the model you picked was an engineering choice. Draft EU GMP Annex 22 makes it a compliance decision: static and deterministic machine learning only in c

Sep 6
37 min

Grading: How Much Does This Model Matter?

Seven documents grade models seven ways, and they grade different things. Sam and Sarah start with the 2011 ICH Points to Consider, which graded models low, medium and hi

Sep 5
30 min

Context of Use: What Decision Is the Model Making?

The one idea in nearly every document: context of use. Sam and Sarah read FDA's January 2025 draft closely, from question of interest to role and scope to model risk as i

Sep 5
28 min

Five Moves: How the Expectations Are Evolving

Before reading any single document, you need the map. Sam and Sarah sort the AI-in-manufacturing rulebook into its four layers, replace "binding or not" with maturity and

Sep 5
36 min

Are We There Yet? (Trailer)

Jack Prior introduces the show: a bioprocess engineer who started out in the late eighties at MIT looking at how to apply AI to bioprocesses, on why 2026 is the year that

Sep 5
2 min