Technology Transfer Has A Data Problem, Not A Process Problem
Dr. Siva Samy is the founder and CEO of ValGenesis, an inventor with 8 patents in digital and AI-based validation, and a Ph.D in Pharma.
gettyTechnology transfer is one of the most critical processes in life sciences manufacturing. It is also one of the most difficult.
Every successful therapy eventually reaches a point where product knowledge, manufacturing processes and operational expertise must move from development into commercial manufacturing, a new facility or a contract manufacturing organization (CMO). The goal is straightforward: Preserve quality, consistency and regulatory compliance while transferring years of accumulated knowledge. In practice, however, technology transfer remains one of the industry’s most time-consuming and risk-prone activities.
Most discussions about technology transfer focus on improving the process itself through better governance, documentation or project management. Those efforts have value, but I believe they overlook the underlying challenge.
In most life sciences organizations, the information required to execute a successful transfer is fragmented across development, validation, manufacturing and quality systems. Teams spend weeks or months locating information, reconciling inconsistencies and confirming that transferred knowledge is accurate before meaningful work can begin. The transfer process itself is rarely the primary obstacle. The challenge is assembling a complete, trustworthy picture of the product.
McKinsey has found that technology transfers for sterile dosage forms typically take between 18 and 30 months to complete. Moving a product to an external partner such as a contract manufacturer adds, on average, another 5.8 months, and each transfer draws on specialists from roughly a dozen disciplines across hundreds of individual activities.
However, organizations using best-in-class practices—such as standardized documentation, risk-based execution, stakeholder alignment and clear cross-functional governance—may significantly reduce those timelines and be able to reduce them to just under 11 months, roughly 60% to 70% faster. Much of that difference comes down to how effectively knowledge is captured, connected and reused throughout the transfer process.
Deloitte has similarly noted that poorly executed technology transfers can delay commercialization, slow product launches, create operational inefficiencies and ultimately affect supply availability. As manufacturing networks become more global and therapies become more complex, these challenges continue to grow.
Regulators are moving the same way: Frameworks such as ICH Q10 and rising data-integrity expectations increasingly assume product knowledge is managed as a connected life cycle, not reconstructed transfer by transfer.
Development data resides in one system. Validation documentation is maintained in another. Manufacturing records, quality data and equipment information are often managed independently. By the time a transfer begins, organizations are forced to reconstruct years of knowledge from disconnected sources.
The traditional response has been to generate more documentation. Unfortunately, every new document creates another opportunity for information to become outdated, duplicated or disconnected from its original source.
That approach may have worked when manufacturing environments were simpler, but it is increasingly difficult to sustain as organizations manage more complex products, larger partner ecosystems and greater regulatory expectations. The harder truth is that few organizations have connected this data, because legacy systems, siloed functions and the cost of standardization make the status quo easier to keep than to fix.
The industry does not need more documentation. It needs connected knowledge.
Much of today’s discussion around AI focuses on content generation and automation. Those capabilities can certainly improve productivity, but they are not what I believe to be the most compelling opportunity in life sciences manufacturing.
Modern organizations generate far more operational data than any team can reasonably analyze or connect on its own. AI can help make connected data significantly more valuable by identifying relationships across development, validation, manufacturing and quality information that would otherwise remain difficult to recognize.
Instead of treating each stage of the product life cycle as a separate activity, AI can help create a continuous thread of knowledge that follows a product from development through commercial manufacturing.
Two capabilities make this concrete. The first is automated gap assessment. AI is able to identify missing process parameters, incomplete validation evidence and unverified analytical methods before they become deviations on the manufacturing floor.
The second is knowledge management with context. AI preserves the context behind manufacturing decisions, connecting related information across functions so teams spend less time searching for data and more time applying their expertise where it matters most.
The technology does not replace scientific judgment. It strengthens it by giving experts a more complete understanding of the information already available to them.
One of the biggest misconceptions about technology transfer is that it represents a handoff between organizations or functions. Successful manufacturing does not operate through handoffs. It depends on continuity.
Every decision made during development influences validation. Validation affects manufacturing performance. Every production run generates knowledge that can improve future transfers and future products. When those activities operate independently, organizations lose visibility. When they share connected data across the product life cycle, knowledge becomes cumulative rather than fragmented.
Instead of serving as a discrete project, it becomes part of a continuous digital thread connecting development, validation, manufacturing and quality throughout a product’s life cycle.
Technology transfer has always been about moving knowledge from one team to another. The next evolution is ensuring that knowledge remains connected from the moment it is created.
Organizations that build a connected data foundation are more likely to shorten transfer timelines, improve product quality and reduce operational risk. More importantly, they can create an environment where every transfer contributes to future learning rather than beginning another cycle of collecting, validating and re-creating information.
AI can accelerate that transformation, but only when it is built on connected, trustworthy data. Without that foundation, even the most sophisticated AI models have limited value.
For years, the life sciences industry has worked to improve technology transfer by refining workflows and documentation. Those efforts will continue, but they will not address one of the industry’s most persistent bottlenecks. The greater opportunity lies in connecting the knowledge that already exists, making it accessible across the product life cycle and enabling organizations to learn continuously from every transfer. Ultimately, the future of technology transfer will be shaped not simply by better processes, but by better-connected knowledge.
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