Takeda Reimagines Biopharma Quality For The Digital Age
A conversation with Elaine Shannon, Takeda
Few functions in biopharma and pharmaceutical development stand to seize the benefits of artificial intelligence more in the near term than quality assurance and control.
AI, even in its current form, is especially good at orchestrating and making sense of massive data sets. Likewise, companies of all sizes are digitizing their quality systems, including Takeda.
Ahead of her keynote at the 2025 ISPE Annual Meeting & Expo, Takeda Global Quality Officer Elaine Shannon shares how the company is evolving its approach to pharmaceutical quality to enable transformation. She highlights the role of digital innovation, AI, and a patient-centric mindset.
She gave us a preview of her talk and discusses the evolution of Takeda’s quality strategy, the challenges of data integrity, and the cultural shifts needed to embrace agile, tech-enabled models.
How has Takeda’s definition of pharmaceutical quality changed over the years? What elements are no longer serving your purpose?
Shannon: I wouldn’t say that our definition of pharmaceutical quality has changed, but rather that it has evolved over the years; however, the one aspect that is always constant is the patient-centric mindset. As science and technology advance, the ways we can apply and deploy pharmaceutical quality are evolving, too.
For example, quality by design improves pharmaceutical quality by shifting from traditional end-product testing to a proactive, risk-based approach, building quality into the product, and process from the start. It leads to enhanced product consistency, a deeper understanding of manufacturing processes, better control over process variables, and fewer costly failures, rework, or recalls.
Ultimately, quality by design aims for "right-first time" manufacturing, ensuring the final drug product consistently meets predefined quality attributes.
Another example is in the space of AI and advanced technology. We see, for example, a shift from lagging indicators traditionally used in quality to real-time monitoring during manufacturing, enabling early detection of defects and deviations and optimizing production processes to reduce waste and improve consistency. AI also strengthens the supply chain by identifying risks, helping fight counterfeits, streamlining regulatory compliance through automated documentation and data analysis, and supporting predictive maintenance to prevent equipment failures.
Overall, AI tools enable the achievement of higher-quality products, increased efficiency, and better adherence to quality standards within the pharmaceutical industry and across the product’s life cycle. Naturally, with the use of AI also comes responsibility and the expected transparency around AI development to ensure trust in systems, particularly in a highly regulated industry like pharmaceuticals. We need to ensure that our AI systems operate ethically and in compliance with legal and regulatory requirements.
Accountability and integrity are deeply ingrained in the way we work and make decisions, as demonstrated by Takeda’s enduring commitment to patient trust, integrity, and a strong values-based culture over its 244-year history.
In summary, quality is no longer seen as mere compliance with regulations but as an intrinsic component of the company strategy, becoming a key driver for long-term value creation across the product life cycle and fundamental for sustainable business growth.
Takeda is shifting toward a more agile, digitally enabled quality model. Can you share an example of a legacy process or structure that simply wouldn’t scale in that model? What replaced it?
Shannon: I would start by saying that we are on a journey. We are tapping into the immense potential of data, digital, and technology across the pharma value chain, across every aspect of our operations to gain efficiencies, shorten cycle times, and improve productivity, among the many benefits that I could cite. Ultimately, this is how we can serve our patients better and faster, improve the experience of our employees, and ensure long-term, sustainable growth for our organization. We are creating conditions to accelerate our transition from a paper-based environment and manual work to a completely digitized environment.
One example I could mention in the quality space is the way we conduct the investigation of deviations. Now, for everyone in pharmaceutical quality, we can appreciate that such a process can be extremely cumbersome and time-consuming and, if overly manual, can lead to many rounds of reviews, comments, edits, and even inconsistencies.
To remedy all that, we have introduced an AI investigation digital assistant tool to enhance report standardization and improve the quality of investigations. This AI tool has been designed and trained to reinforce our internal Investigation quality standard so every person who uses the tool is executing the global process in a way that leads to the desired quality of output.
This tool doesn’t replace our existing quality system for recording deviations but is rather being deployed as a supportive and complementary tool to enhance the overall way we manage deviations, bringing standardization and enhancing high-quality reports. This, in turn, supports the quality of investigations while saving time by avoiding repetitive manual drafting.
Predictive quality and risk-based approaches are gaining traction, but they demand high confidence in data integrity. Describe your greatest challenge in fortifying your data. How have you worked to overcome it?
Shannon: The opportunities for AI and automation are only as good as the data they run on. If we can’t rely on the quality of our data, this will impact any model’s performance, accuracy, and, naturally, reliability.
One of our greatest challenges has been aligning people and processes with the accountability for driving a bold data strategy anchored in our business objectives. In a highly regulated global environment, data comes from many functions and sites and has many purposes. This data diversity is a strength, but it also created fragmented ownership and uneven data quality regarding how data is created, maintained, and used along the data life cycle.
We are addressing this by treating data as a product. We do this through clear governance, a deeper data understanding, shared ownership and accountabilities, and AI-enabled capabilities. It requires a mindset shift: moving from siloed, one-off data delivery to a collaborative model that enables business processes, while, at the same time, supporting secondary use and insights that were not possible before.
To make this real, we are introducing, for example, a detailed data quality standard that ensures consistent meaning, clear roles and responsibilities, and quality level expectations, all overlaid with AI to continuously raise data quality. We also apply Big Data techniques to check and verify information, using human- and AI-generated business rules that create a feedback loop between data creation and use.
In short, we are applying the same disciplined principles we use in producing medicines — testing against standards and controlling processes — to how we build and manage the integrity and trustworthiness of our data along its life cycle. It will be a journey, but we are well underway, and our progress is already strengthening the foundation for predictive quality and risk-based decision-making.
Where do you see friction when it comes to teams adopting digital tools, i.e., skills gaps and learning capacity, adaptability, or recruiting? What strategies have you found to help alleviate it?
Shannon: I think one of the biggest challenges lies in the speed of change. Technology advancements evolve faster than ever; what’s cutting-edge today may be obsolete tomorrow. This rapid adoption imperative means our digital upskilling programs must be agile, proactive, and deeply embedded in our culture. Another challenge is ensuring accessibility and relevance — making sure every team member, regardless of background, has the opportunity and support to develop meaningful digital skills that align with the business’ evolving needs and individual development opportunities.
At Takeda we have been strategically deliberate with investing in the digital upskilling and reskilling of our people. Research shows that organizations with digital dexterity are 3.3 times more likely to succeed in digital transformation.1 Our digital learning is a key component of the agile learning culture we want to enable and promote across the company. This is critical — not just to remain competitive but to drive innovation, adaptability, and resilience, as technology evolves rapidly and reshapes how we operate. At Takeda, we see digital acumen as foundational for every role, from research to patient engagement and everything in between.
As an example, we have put in place a learning program in which each employee has three hours per month to develop digital skills and invest in their continuous professional development, designed to empower them to succeed in a rapidly evolving digital landscape. This is in addition to day-to-day training activities connected to roles. We want our people to feel they have what they need to thrive and succeed in an environment that is changing at pace.
By improving digital capability at all organizational levels, we boost productivity and adaptability such that we can effectively navigate change.
For good reason, the pharma industry often talks about speed and compliance. In practice, those goals often conflict. Where do you see the most tension between agility and regulatory conservatism? How are you managing that at scale?
Shannon: Speed and agility are what enable healthcare innovation to reach the patient safely, timely, and effectively. Over the years, we have seen great progress by regulatory agencies as they develop pathways to expedite review and approval of highly innovative treatments in the space of unmet medical need.
As science and technology advance at an unprecedented pace, streamlined regulatory approaches will be of the essence to ensure that regulation does not impede innovation and that, respectively, regulation can enable and safeguard the availability of safe and efficacious healthcare innovation for the patient, as timely as possible.
One of the areas where we see tension between agility and at times regulatory conservatism arises in the management of post-approval changes. These changes — whether to improve manufacturing, enhance testing, or reduce patient risk — are essential for maintaining a state of control and driving continuous improvement. Yet, because each country requires its own regulatory approval before implementation (partially due to limited regulatory convergence), even well-justified changes can take years to roll out globally. Such delays not only slow innovation but also increase the risk of drug shortages and compliance issues.
At scale, we manage this complexity through a combination of strategic planning, risk-based frameworks, enhancements to organizational structure, technology-enabled solutions, and forward-looking regulatory collaboration and engagement. Compliance can be continuously improved without sacrificing agility through consistent execution of documented approaches to mitigate and resolve pressing challenges. Consistency in execution allows teams to allocate more time to value-added activities, ultimately boosting both productivity and compliance.
Importantly, it is recognized that sustainable progress requires industrywide collaboration to help regulators understand the scope and urgency of these challenges. We engage with regulatory bodies worldwide to advance pharmaceutical manufacturing science and regulations. Additionally, efforts such as the EMA’s reliance pilot and WHO’s Good Reliance Practices (GRelP) are paving the way for global regulatory convergence. These programs demonstrate that reliance on trusted regulatory assessments can significantly reduce approval timelines — by up to 56% in some cases — without compromising safety or quality.
It is a balancing act — one that requires strong internal governance, cross-functional coordination, and a commitment to quality systems that regulators can trust. But it’s also a shared journey, and we’re proud to be part of the broader industry movement advocating for smarter, risk-based regulatory models that serve patients better and faster.
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About Elaine Shannon:
Elaine Shannon is the global quality officer of Takeda where she oversees the enterprise quality organization that establishes consistent quality management systems and programs across the network. Prior to her role as global quality officer, Elaine held several roles at Takeda related to quality, manufacturing, and oncology. Most recently, she served as site head for Takeda’s Massachusetts Biologics Operations (MA Bio Ops) manufacturing site. Elaine also served as global head of quality oncology and external supply small molecules and global head of quality audits and supplier management. She joined Takeda’s Global Quality Compliance and Systems group in 2016 to develop the company’s knowledge management processes. She holds a master’s degree in pharmaceutical manufacturing technology and a bachelor’s degree in GMP.