Using Generative AI For Smarter, Faster, More Predictive Biopharma Ops
By Rohit Harve, Srinivas Rowdur, and Erik Moen
The biopharma industry is rapidly evolving. As such, biopharma companies are facing a variety of operational challenges stemming from a high volume of siloed data coupled with regulatory scrutiny that impedes the application of new technologies. This has resulted in a high degree of manual effort to extract insights from data. A recent client situation exemplifies these challenges.
At the company’s midsized biologics plant, three consecutive manufacturing runs had missed their yield specification by double-digit percentages, yet nothing obvious in the process history differed from the high-performing campaigns of the previous month.
The raw data was exhaustive — second-by-second sensor feeds, detailed LIMS (Laboratory Information Management System) assays, sprawling free-text historian reports — but siloed systems made it impossible to easily aggregate and analyze the data. Instead, to diagnose the problem, the team were exporting CSVs, stitching timestamps by hand, and screen-grabbing graphs for slides. By the time a root cause emerged several weeks later, any corrective action was already out-of-date.
Operational excellence is no longer a “nice-to-have;” it is now the difference between profitable supply and expensive waste. The equipment, clean-rooms, and reactors were state-of-the-art, yet the data describing every heartbeat of the process was not insightful.
The Rise Of AI In Biopharma Manufacturing
As illustrated above, many biopharma organizations struggle with operational complexity and process variability, which can cause substantial fluctuations in yield and product quality. The rise of Gen AI and advanced analytics offers biopharma significant opportunities to enhance operations, reducing costs while also improving product quality and yields.
GenAI — which can generate new content based on patterns learned from vast datasets — can quickly scan volumes of batch records, deviations, and sensor data to detect root cause, predict outcomes, and generate mitigation strategies. Agentic AI takes this a step further by initiating and managing tasks without continuous human input: for instance, monitoring MES (Manufacturing Execution System) data in real time and triggering interventions when specific quality thresholds are breached. Autocorrecting algorithms also play a role by learning from prior production cycles and adjusting critical parameters within pre-approved operating ranges. These algorithms can, for example, detect early signs of a drift in pH or temperature and recommend setpoint changes to stabilize yields. These technologies shift from reactive QA to predictive and autonomous optimization, freeing up humans for strategic innovation.
Despite the potential of AI, adoption in GMP (Goods Manufacturing Practices) settings has been slow. This piece will cover recommendations for overcoming barriers to the implementation of AI in GMP environments.
Ensure Your Systems And Datasets Are AI-ready
Many biopharma companies still rely on outdated infrastructure and siloed systems, such as LIMS, QMS (Quality Management System), MES, and BMS (Business Management System). The associated data sets — often a mix of structured and unstructured data — are underutilized, rarely combined, and seldom tapped for real-time, predictive decision-making.
According to an MIT Sloan survey, 93% of Chief Data Officers emphasize the importance of a strong data strategy to derive value from AI. Since AI both learns and produces outputs based on patterns identified through data, low-quality data can cause AI models to generate incorrect conclusions. These types of errors would be particularly disastrous in GMP settings, resulting in quality concerns.
To effectively implement AI into their day-to-day operations, biopharma companies must ensure their systems and datasets are “AI-ready.” The first step in this process is assessing the existing technology stack to see if any systems can be retired, replaced, or integrated. Technology stacks often grow organically over time, resulting in legacy systems that do not seamlessly share data. Assessing existing systems can help identify opportunities to replace archaic systems and improve integration among existing systems.
The next step is building a unified data foundation organized on the basis of biopharma ontology, as siloed datasets limit the utility of AI tools. Pulling LIMS, QMS, MES, and BMS streams into a single, validated data lake is critical for AI implementation. Finally, strong data governance is necessary to maintain high data quality standards across the organization. This may include setting up a data governance council, developing a framework for master data management, and defining policies for data ownership.
Mitigate Regulatory Risk Through Validation, Transparency
GMP environments involve regulatory scrutiny and oversight. These regulatory requirements demand validation, which can be time-consuming and expensive when AI-driven recommendations must be justified and documented in detail. Fine-tuning large language or vision models typically needs domain-specific data and substantial GPU time, although adapter-based methods are starting to reduce that burden.
Moreover, AI models, especially deep learning systems, are often perceived as "black boxes," producing outputs without transparent reasoning paths. This lack of interpretability raises concerns with data integrity and traceability. Models may also produce hallucinations that are plausible sounding, but incorrect, which poses additional risk.
The EU has put in place the world’s first comprehensive, horizontal AI law — the AI Act — to combat these challenges. Conversely, the U.S. continues to rely on a patchwork of state rules. Recently, the FDA proposed a non-binding draft guideline titled “Considerations for the Use of AI to Support Regulatory Decision-Making for Drug and Biological Products” (Jan. 2025). This is a seven-step, risk-based credibility framework that lays the onus on biopharma to:
- Define the question of interest
- Define the context of use (inputs, outputs, workflow)
- Assess model risk (influence × decision consequence)
- Draft a credibility assessment plan
- Execute the plan
- Document results and any deviations
- Decide whether the model is adequate for its context of use
While this is a step in the right direction, the grey areas remain – specifically for fully autonomous control in GMP.
As a result of this regulatory ambiguity, many biopharmas are reluctant to apply these tools directly to GMP, opting instead for constrained use cases to avoid potential regulatory actions. In order to safely and effectively leverage Generative AI tools, biopharma should:
- Treat validation and transparency as design requirements. Model-agnostic interpretation tools, continuous audit logs, and human-in-the-loop review workflows turn “black-box” anxiety into confidence. For example, AI could identify discrepancies in a large volume of documentation and suggest remediation, but an operator should be accountable for batch decisions.
- Apply explainable analytics first, autonomy second. Using machine-learning models allows biopharma to surface previously invisible multi-parameter drift. Layering GenAI on top will help translate those patterns into plain-language summaries.
- Introduce agentic control only within the approved design space. Self-correcting algorithms can safely adjust pH, feed rates, or temperature if the guardrails are pre-defined per specifications. This keeps regulators comfortable and avoids re-validation cycles.
- Support cross-functional alignment between digital, quality, and manufacturing teams. Decisions to implement AI should not be made in a functional silo. Bringing together teams creates a chance to discuss risks or concerns with integrating AI into their workflows.
Help Your Teams Build Trust And New Skillsets
The 2025 Edelman Trust Barometer survey found that only 32% of the American public trusts AI, a figure that is likely even lower among those working in heavily regulated environments. Many biopharma employees may have reservations about integrating AI because they are unclear on how to use AI, lack trust in AI outputs, or have security concerns related to AI.
AI implementation must be accompanied by change management and training efforts. To ensure successful adoption, biopharma must first ensure leadership buy-in. Not clearly linking AI to business strategy goals leads to muted leadership investment in AI, resulting in low adoption. Biopharma leaders should be communicating the benefits and expectations across their teams.
Additionally, biopharma should develop tailored training programs to meet the needs of different teams. A survey of 300 decision makers found that 85% of leaders believe new skills will be essential to manage AI. Programs should seek to not only establish a baseline understanding of AI but to upskill each function on the skills they will need to use AI effectively.
Finally, it is critical to clarify the value that AI can deliver to the teams “on the ground.” This could include freeing up more time for professional development activities, helping teams build the skills they will need to thrive in an AI-driven world, or reducing the time spent on manual and repetitive tasks.
Biopharma embracing generative and Agentic AI can achieve a step-change in operational performance. These technologies empower teams to manage complexity, reduce variability, and build a self-optimizing manufacturing environment. A recent report estimates that GenAI could enable a 90% efficiency gain in biopharma operations.
The path forward isn’t about replacing human expertise but augmenting it. By uniting domain science with AI, companies can unlock the full potential of their data, transforming biopharma from reactive to proactive.
About The Authors:
Rohit Harve is an Advanced Manufacturing & Supply Chain expert at PA Consulting. He leads transformative initiatives, from continuous improvements to process scale-ups, leveraging advanced technology such as process mining and automation. His work spans greenfield facility planning, supply chain innovation, and driving operational excellence. With a commitment to creating meaningful change, he helps organizations navigate complexity and achieve sustainable growth.
Srinivas Rowdur is an AI & Digital Strategy expert at PA Consulting. Srinivas worked with the world’s leading brands to transform the challenges of innovation in the digital age into opportunities. He works across all aspects of digital transformation ranging from digital strategy, consumer experience, mobility, content management, IOT and consumer analytics using technology platforms to help clients understand the big picture and the fine details to deliver insight-driven digital experiences.
Erik Moen is a Healthcare & Life Sciences expert at PA Consulting. Erik supports clients across the healthcare ecosystem with digital- and data-related initiatives. He brings a background in communications along with insights from his work in healthcare technology to help pharmaceutical and biopharma clients unlock the value of real-world data and drive digital innovation.