The pharmaceutical industry is increasingly exploring Artificial Intelligence (AI) to accelerate and de-risk technology transfer activities. I’ve heard it said, a couple of times now – We won’t be needing any technology transfer specialist, we will be able to use AI to perform Technology Transfer from now on. Well, I’m not sure if that means they didn’t understand technology transfer, or didn’t understand AI, but I’ll be ready to pick up the pieces when they try.
While AI offers significant opportunities to improve decision-making, reduce timelines, and enhance knowledge retention, AI is not a substitute for scientific understanding or quality systems. In fact, the effectiveness of AI depends heavily on the availability of structured, high-quality knowledge and data.
Data, Data, Data
AI systems need large amounts of data to be of any use, Not just any data – data that has been organised and “structured” so that AI can understand it.
Organizations seeking to leverage AI for technology transfer must first solve a more fundamental challenge: converting fragmented organizational knowledge into a structured, digital, and machine-readable form. A phrase often used is: “Digitize knowledge before you operationalize AI.”
A typical technology transfer process often involves assembling information from numerous sources such as:
- Process development reports
- Analytical method validations
- Batch records
- Process characterization studies
- Risk assessments
- Deviations and CAPAs
- Regulatory filings
- Subject matter expert (SME) knowledge
Much of this information usually exists in unstructured formats such as PDF reports, PowerPoint presentations, spreadsheets, emails, and personal experience. Consequently, critical knowledge is often difficult to locate, interpret, and reuse. The issue is not usually a lack of information but the inability to efficiently connect and utilize it.
AI is only as good as the data it operates on, poor-quality und unstructured data inevitably leads to poor-quality outputs. AI cannot compensate for missing or incorrect scientific knowledge or data.
Benefits of AI
When supported by structured knowledge and robust governance, AI can deliver substantial benefits to technology transfer processes:
- Faster transfers by reducing the time spent searching for information and preparing documentation.
- Improved Consistency
- Standardized outputs reduce variability between projects and sites.
- Better decision-making by using data-driven recommendations to improve process understanding and risk evaluation.
- Enhanced Knowledge Retention, critical expertise becomes less dependent on individual employees.
- Reduced Costs, more efficient transfers can lower development and manufacturing costs.
- Stronger Lifecycle Management as AI can continuously learn from commercial manufacturing data and support ongoing process improvement
As examples
AI can help and assist with the documentation
- Draft Technology Transfer Protocols
- Generate validation documentation
- Summarize development knowledge
AI can help with process monitoring
- Analyze engineering batch data
- Detect process drift
- Identify out-of-trend parameters
It can help with analytical method transfer
- Assess instrument variability
- Analyze chromatographic data
It can also assists with risk identification and mitigation planning :
- Enhancing risk tools like Failure Mode and Effects Analysis
- Assess risk outcomes and prioritizing the risk mitigation strategy for transfer.
- Perform site gap analysis, equipment comparability, process sensitivity analysis, scale-up risk evaluation
AI Risks and Limitations
Despite its promise, AI presents important challenges.
if a manufacturer uses AI as an aid in document creation, it “must review the AI generated documents to ensure they were accurate and actually compliant with cGMP,” and that failure to do so is in itself a violation GMP.
- Generative AI can produce plausible but inaccurate information (it has a tendency to hallucinate. In a regulated environment, unsupported conclusions can create significant compliance risks.
- Some machine learning models operate as “black boxes,” lacking in explainability making regulatory justification difficult. Explainability remains an important consideration in regulated manufacturing environments.
- Validation, AI systems used within GMP environments require validation approaches demonstrating fitness for intended use.
- An over-reliance on automation. AI should support expert judgment—not replace it. Recent FDA communications have emphasized that AI-generated outputs require appropriate human oversight and quality review. The lack of such appropriate human oversight and quality review has lead to the first (to my knowledge) to the FDA’s first warning letter (to Purolea Cosmetics Lab) for inappropriate use of AI in pharmaceutical manufacturing – process validation process validation had never been carried out because AI never mentioned it was required.
Regulatory considerations
No single global regulation specifically governs AI in pharmaceutical technology transfer, but it is expected that it will be used within the current regulatory framework. GMP expectations remain unchanged, Regardless of whether AI is used or not.
- ICH Q10 – Pharmaceutical Quality System, explicitly identifies technology transfer as the process of transferring product and process knowledge.
- ICH Q12 – Product Lifecycle Management, promotes knowledge management, science-based decision-making, and lifecycle approaches to pharmaceutical quality. These principles align closely with AI-enabled knowledge systems.
FDA has now firmly established that the output of an AI system is not a substitute for the judgment of a qualified human, and that relying on it as one is a citable violation. The following principles must be observed:
- Process understanding must be demonstrated.
- Decisions must be scientifically justified.
- Data integrity must be maintained.
- Quality oversight remains essential.
- Human accountability cannot be delegated to AI.
Under EU GMP principles, Chapters 1 and 4, Annex 11 on computerized systems, and data integrity guidance apply and ,a manufacturer must be able to demonstrate:
- The system is fit for its intended use.
- Risks have been assessed and controlled.
- Outputs that affect product quality are appropriately reviewed.
- The computerized system is validated.
- Changes are managed through change control.
- Data integrity is maintained (ALCOA+ principles).
- Personnel remain accountable for GMP decisions.
As such, if an AI system was used to generate information that influenced GMP systems or processes regulatory inspectors will likely examine:
- What the model can do
- What it cannot do
- Who reviews outputs
- How errors are detected
- How model changes are controlled
In Conclusion
AI has the potential to transform pharmaceutical technology transfer by improving knowledge accessibility, accelerating documentation, enhancing process understanding, and strengthening lifecycle management.
However, organizations should resist the temptation to view AI as a standalone solution. The greatest barrier to successful AI implementation is often not the AI technology itself—it is the absence of structured, trusted, and connected knowledge.
The path to effective AI begins not with sophisticated algorithms, but with organizing and structuring what the organization already knows.
Technology transfer has always been about moving knowledge. AI simply provides a new and powerful way to capture, connect, and apply that knowledge—provided the foundation has been built correctly.
References
- Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products [https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-artificial-intelligence-support-regulatory-decision-making-drug-and-biological]
- CDRH Issues Guiding Principles for Transparency of Machine Learning-Enabled Medical Devices [CDRH Issues Guiding Principles for Transparency of Machine Learning-Enabled Medical Devices]
- FDA warning letter Purolea Labs [https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/warning-letters/purolea-cosmetics-lab-722591-04022026]
- EU – Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (“Artificial Intelligence Act”). [Regulation – EU – 2024/1689 – EN – EUR-Lex]
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About The Author:
Trefor Jones is a technology transfer specialist with Bluehatch Consultancy Ltd. After spending over 30 years in the pharmaceutical / biopharmaceutical industry in engineering design, biopharmaceutical processes, and scale-up of new manufacturing processes, he now specializes in technology transfer especially of biotechnology and sterile products.
He can be reached at trefor ”at” bluehatchconsultancy.com.