Digitize or Fall Behind

Bioprocessing companies risk slowing scientific progress unless they embrace digital-data capture and greater collaboration, according to Alexander Seyf, CEO of Autolomous, a company developing digital manufacturing solutions for cell and gene therapies.

Speaking about the industry’s biggest challenges, Seyf describes poor data management as the “elephant in the room,” arguing that too much crucial information remains trapped in paper records, spreadsheets, and isolated systems.

“Everybody wants to have AI,” Seyf says. “But where do you have your data? If it’s in binders, there’s not much you can do.”

According to Seyf, the path toward more efficient manufacturing, stronger clinical outcomes, and meaningful AI applications begins with digitizing information from the earliest stages of research. He believes many organizations make the mistake of waiting until their science is mature before investing in digital infrastructure. “The sooner you start, the better it is,” he says. “Pen and paper do not prevail, and pen and paper do not transfer.”

Seyf argues that the consequences extend far beyond operational inefficiencies. When data remain inaccessible or fragmented, researchers lose opportunities to learn from past experiments, identify patterns, and accelerate scientific discovery. He stresses that the industry must become more willing to share non-commercially sensitive knowledge, particularly in areas such as rare diseases and advanced therapies, where patient populations are limited. “We are all here to serve patients,” he says. “Protect your intellectual property, but also share the learnings.”

One of his strongest criticisms is directed at the scientific community’s tendency to focus almost exclusively on successful outcomes. Seyf believes failed studies and unsuccessful trials often contain lessons that could prevent others from repeating the same mistakes. “A lot of publications want to publicize only the good news,” he says. “That’s fundamentally wrong. We need to learn from failures.”

To illustrate his point, Seyf compares the biotechnology sector with the aviation industry. Modern airlines routinely share information about incidents and technical problems to prevent future accidents, creating a culture of collective learning and safety. “If something goes wrong, everybody in the world knows about it and knows how it was managed,” he says. “We are also dealing with people’s lives. The only way for us to improve is to share.”

Seyf also highlights the growing role of AI in healthcare. Although consumer AI systems have benefited from vast amounts of publicly available information, healthcare still operates with a relatively small pool of accessible data, he says. Expanding that foundation, he argues, could unlock major advances in diagnosis, drug development, and personalized medicine. “Imagine what we could do,” he says. “The progression of science is unlimited.”

For commercial bioprocessors, his recommendation is straightforward: digitize from day one. Capturing research, development, manufacturing, and clinical data in digital formats not only improves collaboration but also preserves institutional knowledge when employees move on. “Every time a scientist leaves, the knowledge goes with them,” Seyf says. “But when it is digital, the knowledge stays with the company.”

As cell and gene therapies continue to evolve, Seyf believes the industry faces a choice. It can continue operating in silos, or it can embrace transparency, digitalization, and collaboration to speed innovation and deliver better outcomes for patients. “The reason humanity has progressed,” he says, “is because we shared.”

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