AI as an Enabler for a Sustainable Quality Transformation

June 2025

This episode, Takeda’s Senior Vice President, Head of Global Quality Compliance & Systems, Magaly Aham, joins the podcast to discuss how AI can enable sustainable quality transformation.

• Guest

  

  Magaly Aham

  SVP Head of Global Quality Compliance & Systems

  Takeda - Boston

• Transcript

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  Welcome to the ISPE podcast, Shaping the Future of Pharma,

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  where ISPE supports you on your journey, fueling innovation, sharing insights,

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  thought leadership, and empowering a global community to reimagine what's possible.

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  Hello and welcome to the ISPE podcast, Shaping the Future of Pharma. I'm Bob Chew, your host,

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  and today we have another episode where we'll be sharing the latest insights and thought

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  leadership on manufacturing, technology, supply chains, and regulatory trends impacting the

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  pharmaceutical industry. You will hear directly from the innovators, experts, and professionals

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  driving progress and shaping the future. Thank you again for joining us, and now let's dive

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  into this episode. Our topic today is AI as an enabler for a sustainable quality transformation.

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  To share more about this topic, I would like to welcome Magaly Aham, Senior Vice President and

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  Head of Global Quality Compliance and Systems at Takeda, who recently keynoted at the 2025

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  ISPE Biotechnology Conference in Boston. Magaly, welcome to the podcast. We're glad to have you

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  with us. Thank you so much, Bob, for having me. So, Takeda, like most pharmaceutical companies,

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  has initiatives to explore applications of AI and related technologies. Can you tell us

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  about what your company is working on regarding AI as an enabler for sustainable quality transformation?

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  Sure. We have some good stories going on right now. For example, we have a deviation AI assistant,

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  which facilitates drafting of investigation reports based on input entered by the investigator.

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  It has a series of questions per investigation element, such as problem statement, initial

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  impact assessment, scope, et cetera. The investigator then responds to each question,

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  and then at the end, the AI assistant generates a structured narrative,

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  saving, of course, time from repetitive manual drafting, increasing readability, and quality

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  of the final report. It also has multilingual capabilities, which is actually pretty cool.

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  And right now, we have it in four languages, including English, German, French, and Japanese.

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  We're also working on SOP transformation. It's another one of the tools that we have

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  ongoing right now. And what we have at this moment covers three functions, search, compare,

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  and summarize. And the search function, for example, helps you input a specific process

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  you want to know about, for example, process validation. Then it gives you key details on

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  the topic, and also gives you a link to the most relevant SOP from our document management system.

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  The compare function allows you to input any SOP, and will pull from the document management systems

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  SOPs that are similar, and how similar they are by means of a percentage. So not only pulls

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  a list of SOPs, it actually tells you how similar they are or not, and it explains that.

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  It explains the similarity and the key differences. So it's very helpful. It does the search for you,

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  and it can also help you eliminate redundant SOPs, or when you are trying to work on

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  optimization or standardization, it's very useful in helping you identify all of those

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  related SOPs that may be in scope of your work. The last function is summarize, and this one

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  allows you to input any SOP, and it will provide you a summary of key information from that SOP.

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  So this is very useful when you want to understand certain SOPs, but you don't necessarily need to

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  train on them, so it's an easy way to get overall content. As part of this SOP transformation,

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  we're also exploring other functionalities, including the SOP drafting, and even using this

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  tool to assess if we can evaluate training development content. So more to come on that one.

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  Okay, so a few things come to mind. Change management, for example. So you have a change

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  proposal, and maybe you know that it's going to impact this SOP, or maybe that's part of the

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  change is to you want to alter this SOP. Do you think this system could then kind of

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  go out and say, well, okay, if you're going to change this, it's impacting this and this and this?

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  Well, right now, that's currently embedded in the document management system that we have, because

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  we have linkages, right, to related SOPs. So when you are, to your point, perhaps

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  working on a change control and you pull, I don't know, the process validation SOP,

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  that process validation SOP in its metadata, we have linkages to other SOPs. So when you

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  include that one as an impacted SOP, you will have the information on other related SOPs

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  for change management purposes. Okay. Onboarding of new people. And, you know,

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  you're given this list of SOPs that you need to read and understand, right? Is there any way that

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  some of these tools could help streamline that, maybe reduce the time required for onboarding

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  new people? Absolutely. It's one of the areas that we're exploring, how we can link the SOP

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  transformation with training and really not training, but learning, you know, having a more

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  focused activities in the actual learning piece rather than just reading an SOP.

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  We're not there yet, but absolutely it's one of the areas that we're exploring.

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  Okay. Well, great. So I'm sure you've heard of other companies and I'm sure you've been

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  approached by vendors with different AI apps. What stands out about your approach? How is it

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  unique or different from what others may be working on and why?

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  I don't know what may stand out as I don't have that insight as to how other companies are working

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  on their digital strategy. What I do know is that it's a focus everywhere because there's value,

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  right? I can talk about Takeda and Takeda has been very intentional. One of our objectives,

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  obviously, is to become a digital biopharmaceutical company. So we have been

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  very intentional in creating an engine that will drive this digital transformation that we're

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  striving to achieve, including a lot of obviously training. We have many different

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  courses and certifications that allows many of our employees to gain understanding up to the level

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  that they want to get, right? There's some training that allows some of our employees to

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  even develop their own AI companion tools. We also have innovation centers,

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  internal innovation centers, which I think is an advantage for us because we have these

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  innovation centers across Takeda, which what they do is basically we go to these centers and

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  present our ideas for solutions or things that we envision. And we have the technical

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  powerhouse there to allow us to develop those solutions and evaluate whether they're scalable

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  across the organization or not. So I think that's a very useful thing for us to have and allows us

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  to drive innovation faster. The other thing that comes to mind with this question is we also have

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  an AI labs, okay, where people can test their AI ideas for feasibility in a safe environment

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  and without impacting any of our systems. So this allows people to feel not only more comfortable

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  with AI and the possibilities, but also excited on what it can do in facilitating our daily work.

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  So let me go back to this innovation center where you take your ideas.

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  There's always a tension or a conflict as companies try to innovate between letting people

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  sort of play and experiment and try different things versus, okay, here's the vision and we're

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  creating this and then this and then this and then this and anything else doesn't fit in.

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  How do you balance allowing for creativity and experimentation versus

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  descending into chaos and the resulting need to systematize everything?

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  No, that's a very good question because especially if you provide for the environment, right, and when

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  we talk about the AI labs for people to experiment, I think that also helps people

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  feel more comfortable and it helps us demystify AI and gen AI in a way. So it actually helps us

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  augment capabilities and help people feel more comfortable. But to your point, yes, we do have

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  governance, right? So we might have solutions that could be developed. However, not every solution

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  is approved for wider implementation. First of all, we need to understand the problem that we are

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  solving. We are also evaluating for scalability, right? Is this a solution that is scalable

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  across the entire Takeda organization? And what's the level of effort? Also, what's the

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  value creation? Because everything takes time and time represents also cost. So what is the value

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  creation? How is these tools or digital tools that are being created contribute to our vision

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  of value creation of being better, faster, and more efficient? So we have governance in place

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  to determine that. I assume then from what you said that there are companion teams in,

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  for example, MS&T or other groups who are also working with AI, machine learning,

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  or digital twin technologies. Is that true? Absolutely, yes. We have an enterprise DD&T

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  organization that establishes the framework for the enterprise and the strategy that will

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  enable Takeda to identify and address the unmet needs across the different business units that we

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  have through share investments. They steward the DD&T span across Takeda and drive enterprise

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  innovation. As part of that enterprise DD&T organization, we have business partners who sit

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  on the DD&T leadership team, and they represent many T&T organizations across Takeda. And T&T

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  is an acronym for Takeda Executive Team. And what I mean by that to your question is these

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  organizations are like global manufacturing and supply and quality, R&D, Japan, PDT or plasma

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  derived therapies, among others. So we have the enterprise DD&T or data, digital, and technology

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  organization. And then we have DD&T heads in all of these sister organizations or business units

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  across the organization. The structure makes us more agile to drive value for our users and

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  customers. And based on the aspiration I mentioned earlier of becoming a digital biopharmaceutical

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  company, yes, we are working with AI, AI, virtual reality, augmented reality in all parts of the

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  organization. And yes, we are also piloting and using digital twins, not only in manufacturing,

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  but even in R&D. So that's just some of the examples that come to mind.

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  So at the Facilities of the Future conference last January, ISPE's conference in San Francisco,

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  I heard Sanofi make a presentation about a digital twin aseptic filling machine.

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  But it took them four years, and the first three was pretty much just getting the site organization

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  to buy in to doing this at all. So what issues or stumbling blocks have you encountered,

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  and how did you solve them? Well, from the manufacturing perspective, if we talk about

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  digital twins, we're piloting that, like I said, in R&D, in manufacturing.

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  From the quality perspective, one of the stumbling areas that I can share

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  has to do with the data quality, right? And perhaps the inconsistency that we have found

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  in data structure, and particularly when you are dealing with different data sources.

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  So having data that is not standardized or from different systems has been a challenge,

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  at least for the digital tools that we are working on in quality. To that extent,

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  Takeda has put in place a data governance organization that is working on different

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  aspects of data, because obviously that's the raw material, right, for all of these initiatives.

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  So master data, data quality to ensure that the requirements for new systems are in place,

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  and for tools that are being developed that need to leverage whatever systems we have right now,

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  that we have better guidance in terms of what we will need in order to make those initiatives or

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  tools successful in achieving what we want to achieve. And in some cases, that might be

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  data cleanup and standardization. So yeah, I understand what you mean about taking

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  some of these years. We have had some situations where we have tried some tools

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  sometimes, and we have to recall that AI, gen AI, we have kind of learned as we go.

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  And so in many cases, we have designed some solutions with the output and not the input

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  in mind. And that has represented some challenges. So the apps that you have developed and deployed,

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  can you quantify the benefits that you've achieved?

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  Well, value creation is part of what we consider in every project. But I can tell you that

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  so far, overall, in Takeda, it will be millions of dollars. We also have been working with

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  automated visual inspection system using AI, and that has helped us reduce

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  rejection rates from around 30% for parental biologics to less than two, and has allowed us

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  to save at least 16 hours per batch in the places where we have incorporated this, just to name a

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  few examples. Well, that's impressive. And it's great that you've been able to implement that

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  kind of technology. Thank you. Going forward, what innovations do you see possible with AI

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  and related technologies? And what organizational regulatory and resource challenges might exist?

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  And how broadly and in what ways do you see these technologies being used in five years?

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  Oh, wow. I see this applied everywhere, everywhere that is possible, right? I think

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  as an industry, as I mentioned earlier, I think we all see the value of applying these technologies

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  internally. For once, it allows us to be more predictive. And I think it changes the mindset of

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  going from firefighting to more risk awareness and being more predictive on everything that you do.

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  There's definitely a need to augment understanding, because you don't really validate AI,

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  right? It learns. So, it's something that is still kind of an area that we all need,

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  particularly regulators, get into understanding these models a little bit more, because it does

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  require a shift in mindset. Of course, we will have to work with regulatory guardrails to monitor

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  its application. But I see it's used everywhere. R&D, for instance, conducting in silico studies,

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  and perhaps even digital runs to demonstrate process optimization outcomes. And this could

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  perhaps minimize the amount of PPQ batches that you might need to run in the future for

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  submissions and new product launches. So, I think the possibilities are endless.

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  Well, thank you for sharing these initiatives and the success stories. Just to recap,

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  Takeda has a center of excellence where it sort of brings together these different initiatives.

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  Your quality department has created this AI for structuring deviation, adjudication, and

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  development in the reports. You've got something that looks at SOPs, and you're exploring ways

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  this can help with training. That's all really great. I think it's wonderful that these

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  technologies are really getting some airtime. So, this brings us to the end of another episode of

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  the ISPE podcast, Shaping the Future of Pharma. A big thank you to our guest, Magaly Aham, for

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  sharing more about how global quality Takeda is using AI to enable sustainable quality transformation.

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  Please be sure to subscribe so you don't miss future conversations with the innovators,

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  experts, and changemakers driving our industry forward. On behalf of all of us at ISPE,

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  thank you for listening, and we'll see you next time as we continue to explore the ideas,

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  trends, and people shaping the future of pharma.