Senior Director
Education, Outreach, and Digital Experience
Open Biopharma Research and Training Institute

Richard Jaenisch

In this clip from episode 1 of Manufacturing Intelligence, Richard Jaenisch of Open Biopharma discusses uses of AI for deviations and investigations in pharma.

Manufacturing Intelligence Episode 1 - Part 2: Automating Deviations with AI

According to Rick Jaenisch, senior director of Education, Outreach, and Digital Experience at OpenBiopharma, every large life sciences company is currently adopting either proprietary AI tools or enterprise versions of models like ChatGPT, Gemini, and Claude. He notes that Claude has been particularly popular because it features a model specialized for life sciences that has been utilized by the FDA. 

Jaenisch identifies the most significant use case for generative AI in the industry as the management of deviations, specifically by turning traditional paperwork into AI-fillable forms. He explains to Christopher Cole, associate editorial director at 

, that this application can save a writer anywhere from one to four hours per report, which is especially valuable since manual typing is often slowed down by multitasking and distractions. 

In more complex investigations, Jaenisch describes how AI assists teams in gathering disparate data and summarizing video transcripts to support root cause analysis. However, he warns against the rise of "work slop," which he defines as low-effort AI output that can easily be identified through pattern recognition. He points out that this "slop" creates a calculable economic loss because reviewers must spend more time on the back end fixing errors than the original writer saved. To combat this, Jaenisch argues that companies must implement training that goes beyond basic "read and understand" instructions.

Finally, regarding manufacturing, Jaenisch observes that data silos and regulations like the EU’s Annex 22—which restricts probabilistic models in GxP environments—remain significant hurdles. While AI is already established in early drug discovery, Jaenisch is looking toward a future in which "AI-native" pharma companies fully integrate these technologies into the production lifecycle.

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In this clip from episode 1 of Manufacturing Intelligence, Richard Jaenisch of Open Biopharma discusses step 1 in adopting AI in pharma manufacturing.

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 

. Despite this high interest, many industry professionals find themselves in a position in which they understand that they 

 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 

-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.

Richard Jaenisch and Christopher Cole discuss AI in pharma manufacturing, covering learning steps, output evaluation, hallucinations, and FDA/EMA guiding principles.

Manufacturing Intelligence: AI Integration in Pharmaceutical Lifecycle Management

In this inaugural episode of Manufacturing Intelligence—a biweekly, conversational series taking deep dives into all things digital technology in pharma manufacturing and development, Christopher Cole, associate editorial director at 

, sits down with Richard Jaenisch, senior director of Education, Outreach, and Digital Experience at Open Biopharma, to explore the practical integration of AI within pharmaceutical manufacturing and development. 

This discussion is specifically designed for industry professionals seeking to move beyond the hype and toward technical proficiency and begins with a foundational inquiry as Cole asks about the essential first step for professionals learning to use AI in their specific contexts. Jaenisch shares insights into the educational frameworks required to build internal expertise. 

A core focus of the conversation is the necessity of human oversight. Cole poses critical questions regarding how to train professionals to critically evaluate AI outputs and how to determine if a system is operating within its competency or merely “hallucinating confidently.” Jaenisch further explores the pedagogy of limitation—teaching staff to recognize exactly when a model is incapable of answering a question. 

Finally, the dialogue shifts to the regulatory environment, specifically an analysis of the EMA and FDA guiding principles for good AI practice throughout the drug lifecycle. Jaenisch evaluates the applicability of these principles and highlights the specific areas where current guidance may be missing key details. Click the video above to join a comprehensive roadmap for pharmaceutical professionals navigating the intersection of cutting-edge technology and rigorous regulatory standards.

Richard Jaenisch explains how digitally interactive SOPs and Human-AI Training Parallelization help build, measure, and continuously improve AI-era workforce skills.

Digitally Interactive Training for the AI Era

 session exploring how to integrate generative AI into biomanufacturing without sidelining human expertise. Richard Jaenisch, senior director of Education, Outreach, and Digital Experience at Open Biopharma Research and Training Institute, and David Jaenisch, director of Technology at Prompting Integration Consulting, LLC (PICLLC), discuss the Human-AI Training Parallelization: Learning (HATPL) system and its real-world impact on workforce development.

They describe how digitally interactive SOPs (DISOPs) support HATPL, turning procedures into adaptive, competency-focused training modules and assessments. At Open Biopharma, DISOPs are made available to all training courses and also deployed internally to track competency assessment pass rates over time. Because the system asks one question at a time and adjusts difficulty based on each learner’s answers and validation content, instructors gain a kind of “Rosetta Stone” view of how skills develop across sections and sessions.

In their pilot on AI literacy, staff already showed strong baseline competence after several years of human–AI training, but regular DISOP-based assessments plus small, project-centered trainings revealed measurable growth and clearer risk mitigation. The parallel use of DISOPs and hands-on projects creates a traceable record of individual capabilities, helping leaders align people to the right initiatives and identify where targeted upskilling is needed.

The interview also highlights how near-real-time insight prevents learners from falling behind in intensive workshops. If a participant’s competency scores start to lag, instructors can immediately adjust curriculum and support so that no one is “done” on day one of a two-week program. Ultimately, Richard Jaenisch argues that structured, adaptive tools like DISOPs are essential for making generative AI a force-multiplier for people and processes, rather than a replacement for human expertise.

Be on the lookout for Part 3 of this interview and check out Part 1: 

Human-AI Training Parallelization: Empowering Life Sciences Without Replacing Expertise

Editor's note: This transcript is a lightly edited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.




My name is Richard Jaenisch. I am the senior director of Education, Outreach, and Digital Experience at Open BioPharma Research and Training Institute in Carlsbad, California.

I am David Jaenisch. I am the director of technology at Prompting Integration and

How we deploy DISOPs at OpenBiopharma is two different ways. One way is we provide it to any training course that happens at OpenBiopharma. So anyone hosting their own course, developing a training with us, co-developing a training, whatever they're doing, we will have it on-site at OpenBiopharma, and we will give them the opportunity to take that curriculum, upload it into DISOPs, and run that competency assessment.

So that data doesn't really give us a lot of the ongoing data which we like to see, unless those individuals continue on with that trend or do multiple trainings with us and we can see the growth over time. We deployed this tool in October of last year. So you would imagine that we haven't had a lot of consistent, specific trainings from multiple groups during that time period to be able to read on that.

However, we also deploy it internally. And so internally, what we found is... we really look at the competency assessment pass rates, and we say, "Okay, how are people doing with these things?" And we have noted that over time we do see a lot more progress on them, and I think that that's twofold. One is you're getting used to the structure because the system is more familiar.

But because the system really only asks one question at a time, and because it is adaptive difficulty, changing the flow according to what you are doing, and how you are answering and how your answer matches with things in the validation material and the validation questions, then, you know, the pass rates really kind of can change from section to section. And so we have a kind of Rosetta Stone, if you will, that allows us to see and assess how those competency assessments trend over time.

And generally speaking, we have found that a lot of our folks are learning a lot more because the first tool we deployed this with was our AI literacy campaigns because it made the most sense, and because I had a lot of the material right and ready to go, and that's how we did our pilot with it, and it worked out very well. We found that not only did most of our staff already have, probably because we've been deploying human AI training parallelization for three years now, but, we found that they already had a good level of competency ahead of time.

But the benefit of deploying DISOPs with regular intervals and also interjecting occasional small, very practical trainings, that are really based around their projects, really meant that the combination of these two, having that hands-on project and having this be the certification that kind of is that traceable record that allows us to understand it, really sees that parallel track so that we can understand their growth, have risk mitigation because we understand where their skills are and where their competencies are. It's very helpful for us in terms of not only figuring out who aligns with what projects, but also who may be needing more certain skills.

There are certain skills that maybe there's a deficiency, maybe we're in the middle of a workshop, and we're deploying this, and we find that there's an individual who's kind of starting to lag behind. But the benefit is then we can then change our curriculum to make sure that they don't fall behind. Because being able to have them know that early means that we don't have to worry about that. In a two-week workshop, if you fall behind on the first day, you're done, man. That's going to be really difficult. So when we deploy it in that situation, we see that very quickly that the individuals benefit because we don't see them lag in that competency assessment in the future days ahead.

In regard to their presentations at PDA Week 2026, Richard Jaenisch and David Jaenisch discuss balancing AI integration with human expertise, compliance, and IP protection.

In this insightful interview, Richard Jaenisch, senior director of Education, Outreach, and Digital Experience at Open BioPharma Research and Training Institute, and David Jaenisch, director of Technology at Prompting Integration and Consulting LLC (PICLLC), discuss their groundbreaking Human-AI Training Parallelization (HATP) framework, a practical approach to integrating generative AI into life sciences workflows without compromising human expertise or regulatory integrity.

At its core, HATP works by parallelizing existing systems rather than replacing them, embedding AI only where it adds value within a given workflow. The framework centers on two key methods: digitally interactive SOPs (DISOPs), which consolidate SOPs and training materials into a single transparent, auditable platform for competency-focused training; and project-based HATP (PBHATP), which captures tribal knowledge and enhances project efficiency through AI assistant development.

Critically, the DISOPs system operates outside of GxP workflows—functioning as a pre-GxP training tool—while being built for full transparency and audit-readiness as organizations approach compliance requirements like 21 CFR Part 11. The platform currently runs on Google's Gemini API, with options for cloud-based client silos or fully local deployment to protect sensitive intellectual property.

Richard and David also address pressing concerns around data privacy and IP ownership. Using Google's API allows organizations to opt out of model training on their data, and existing case law generally supports the position that human-edited, system-maintained content remains owned by the individual or organization that created it.

DISOPs itself is a life sciences-specific customization of PAIR, PICLLC's underlying AI framework, tailored to meet the unique regulatory and operational demands of the industry.

Check out Part 2: 

, and be on the lookout for Part 3 of this interview!



My name is Richard Jaenisch. I am the senior director of Education, Outreach, and Digital Experience at Open BioPharma Research and Training Institute in Carlsbad, California.

I am David Jaenisch. I am the director of technology at Prompting Integration and Consulting LLC, PICLLC for short.

So for the most part, human AI training parallelization will is just effectively parallelyzing whatever system you're already deploying. So generally speaking, it is functionally what you are already doing, but it's enabling AI in the specific components in the workflow where it is pertinent. And so it really depends on whatever AI deployment you're using.

So in that concept, conceptually, for the for that system, it really depends on what you're using. In our case, what we're using is DISOPs, or digitally interactive SOPs, as well as using another piece, of basically Gemini.

But what we are doing though is not in the GxP workflow. So in this case, it's very external because it's all pre-GxP. So in this case, it isn't really hitting that area just because it's mostly on the research side in terms of what we've deployed. We are getting to the point now where we're actually entering 24 CFR Part 11.

However, DISOPs as a program, while its name does imply that it is interacting with the SOPs, it is external. So the idea is that it's taking the SOP, taking the materials that you're working with and putting them all together into one system, and that system is entirely transparent. So it can go through, it can be validated through any method that's already in play. But the thing is, it's generally not as part of your GxP component. So it's more of the training element. So usually if you have a situation where you need to have retraining, where you need to have that, correction, then this is an opportunity to be able to use that.

And then in that sense, you have a completely transparent record of not only the training itself, but how the exact training was deployed. So the benefit of the tool is that it is completely transparent in its entire methodology.

So it is awkward for a generative AI tool to have that, but because of the method of how it breaks down each component and where the actual user specifically has the articulate control of the components, all of that produces a digital record that can then be audited at any time.

In terms of how our product works, we currently work with Gemini on the cloud. It can technically work with any cloud computing instance, but it can also run locally. So if your concern predominantly is about IP theft, locally will obviously make that literally impossible. And if you are running it on the cloud, we create silos for each of our individual customers, so that way they are completely separate from any other client, and there will be no data sharing between them.

If your concern is predominantly about the idea of the model being trained on it, Google explicitly allows you to disable this functionally when you use the API. Certain API use, they allow you to just say, "I'm not training on this." This is also true for their enterprise client. To my knowledge, it's not true for Open AI currently, due to an ongoing lawsuit with The New York Times. I'm not 100% sure about that. Don't quote me on legal.I don't know legal. What this means is you're safe from the perspective of Google getting anything from you or from other companies as well.

And in terms of ownership of IP, that is a complex ongoing legal question in terms of generated content in general. However, Google definitely doesn't own it. Yes. We've decided that. It is either, no one or it is the company, depending on the situation and level of editing. And because these are edited after the fact and they are maintained within a system that's maintained by people and turned into a holistic list of questions, I believe that it would be more likely they'd be owned by the person making them as opposed to being un-copyrightable in that case.

Generally speaking, there have been court cases so far that explicitly state that the human owns the copyright, not the AI tool. So anything generated is generated by an individual, that individual who created the system. And our system is done in such a way that it would not be in that same facet.

But I will say this though, the project-based component of project-based human AI training parallelization on the project's basis, that is owned by the process owner. So whoever the process owner has that IP of whatever the project is and how that's developed, that is a separate contract and a separate system that we generally do at Open BioPharma. And so we have those types of projects that are deployed frequently from time to time. And so in that case, those are deployed and owned by the process owner, which is typically the company we work with. Although occasionally there is a partnership collaboration of some kind.It's not usually a straight partnership. It's usually a situation where IP is developed and there's a lot of discussion around how that works. That being said, that is project-based, and so it's very unique in that way.

The DISOPs component is what David is speaking to. And so those elements, he's talking specifically as PICLLC created PAIR, which is basically the underlying layer of DISOPs. So DISOPs is a customized version of PAIR built specifically for life sciences to handle the unique situations that occur within life sciences. Just so I have some clarification on those pieces.

Richard Jaenisch

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