As biotech and pharma adopt AI to accelerate R&D, manufacturing bottlenecks constrain health innovation. Biotech AI and robotics emerges to bridge these gaps and drive impacts across health.

The NVIDIA State of AI in Healthcare and Life Sciences: 2026 Trends report found that 70% of biotech, pharma, and healthtech industry organizations are actively using AI, up from 63% in 2025. A majority (65%) are using AI for data analytics and data science, while almost half of pharma say they are using AI agents for drug discovery and biomarker identification. Manufacturing was not mentioned or included in the survey, despite the critical role it plays.

Benchling’s report, which also notes a dramatic increase in AI adoption in R&D, also fails to provide any data on how manufacturing is planning to keep pace. According to the report, over the next 1-2 years, only 38% plan to adopt workflow orchestration and AI in manufacturing and supply chains.

In this report, Filippos Tourlomousis , Founder and CEO, Biological Lattice Industries (BLI), walks readers through biotech technologies poised to impact global health, the AI R&D bottlenecks, the role of automated robotics manufacturing, and the supply chain opportunities for companies that want to help.

Which Biotech Technologies Have the Greatest Potential to Impact Global Health?

Biological Lattice Industries (BLI), tech stack is developed to automate the entire DMTA loop — design, make, test, analyze — the cycle every product innovation depends on, using cyber-physical AI agents.

Working in three layers, Loominus designs and orchestrates, BioLoom prints, and MetaLab formulates. The company recognizes the advances made in different areas of biotech but says that what hasn’t kept pace is the computational and robotic infrastructure to conceive, design, validate, launch, and manufacture products. DMTA loops are still running in manual, trial and error mode, the company says.

“Three biotech technologies stand out for global impact: cell and gene therapy, advanced drug delivery, and engineered human tissue,” says Tourlomousis.

“What will decide how many people they reach is less the science than whether we can make them reliably, at scale, and in more places.”

The first is cell and gene therapy. These treatments have produced lasting remissions in some blood cancers that had stopped responding to every other treatment, Tourlomousis explains. But most are made one patient at a time, in a handful of specialized facilities, at a cost of hundreds of thousands of dollars per patient, says Tourlomousis.

“Today they reach a small fraction of the people who could benefit,” says Tourlomousis.

The second is advanced drug delivery: lipid nanoparticles like those that made mRNA vaccines possible, long-acting injectables that replace daily pills with a shot every few months, and hydrogels and implants that release a drug where it is needed, Tourlomousis explains.

“A drug is only as useful as the material that carries it, and a medicine that is more stable and needed less often reaches far more people,” says Tourlomousis.

The third is engineered human tissue: organoids, miniature lab-grown versions of human organs, and scaffolds and implants that help the body regenerate.

Tourlomousis explains that the FDA Modernization Act 2.0 opened the door for drug developers to use human-relevant test models, known as new approach methodologies (NAMs), in place of animal testing.

“Organoids are among the most promising of them, but most organoid protocols are still nascent and run on flat, 2D culture surfaces, so organoids vary widely from batch to batch,” says Tourlomousis.

“That heterogeneity will delay the promise of NAMs,” Tourlomousis says.

According to Tourlomousis, architectured 3D biomaterial scaffolds can solve it: their architecture is designed in software and printed by additive biomanufacturing systems, so the structure the cells grow on is programmable and identical every time. The same approach produces patient-specific implants fitted to one person’s anatomy, he explains.

Why Biotech AI and Robotics Can Unlock Global Health

Tourlomousis explains that all three mentioned biotech technologies that share the potential to impact global health also share the same one constraint.

“They are made from living cells or complex engineered materials, where how the product is made determines how it performs,” says Tourlomousis.

“That know-how still lives in tacit recipes and in the hands of a few skilled scientists, so these products are slow to develop, hard to reproduce, and hard to transfer from one site to another,” says Tourlomousis.

“Standard lab automation never solved this, because it was built for simple liquids, not viscous formulations, living cells, or engineered fibrous structures,” says Tourlomousis.

“That is where AI and robotics come in, and it is what we build at BLI,” Tourlomousis explains.

BLI’s platform connects AI agents to self-driving lab hardware. MetaLab develops complex formulations, BioLoom fabricates scaffolds and devices, and Loominus Studio orchestrates the full Design-Make-Test-Analyze loop, on our robots or on instruments a lab already owns, so every experiment trains the next one.

The recipe becomes software: development cycles are accelerated compared to the standard Edisonian paradigm of manual, trial&error work, and a process validated in one lab can be reproduced exactly in another.

“We have served clients that grow organoids on programmable scaffolds designed in software and printed on BioLoom, our additive biomanufacturing system, because reproducible biology starts with a reproducible material,” says Tourlomousis.

“One caution: AI models alone won’t get us there,” says Tourlomousis.

“A model can reason, but acting on living and engineered matter takes a harness: memory, data, tools, safety rules, and direct control of machines,” he adds.

“The companies that change healthcare will be the ones that connect AI to the lab bench,” says Tourlomousis.

How Companies Can Help Drive The New Biotech AI and Robotics Era and Supply Chain Opportunities

As Tourlomousis explains, demand is moving toward products that are harder to make: cell and gene therapies, long-acting injectables and other advanced formulations, engineered tissues, and patient-specific implants.

“The supply chain behind them was built for pills and simple liquids, and I see three gaps new partners can fill,” says Tourlomousis.

The first is process development capacity. Turning a promising therapy into a product that can be made reliably is now one of the slowest steps, Tourlomousis explains. Inside pharma companies, that work belongs to CMC teams (chemistry, manufacturing, and controls), the groups that develop the manufacturing process and show regulators it makes the same product every time, says Tourlomousis.

Outside, contract development and manufacturing organizations (CDMOs) and contract research organizations (CROs) do the same work for others. “Both are being asked to take on cell therapy processes, complex formulations, and engineered tissues without matching growth in expert headcount, and much of their work is still trial and error: run a batch, send samples to the lab, wait days for results, adjust, and repeat,” says Tourlomousis.

“Self-driving labs change that cycle,” says Tourlomousis.

Tourlomousis explains that self-driving labs sample the process in real time, using what the industry calls process analytical technology (PAT), measure the properties that matter as the process runs, and decide the next set of conditions on their own, so process development closes its own loop instead of waiting on each answer.

“Partners who bring that kind of autonomous, reproducible process development to CMC teams and their contract partners can take weeks out of every project and every tech transfer,” says Tourlomousis.

The second is reproducible materials. Every one of these technologies depends on inputs that must behave the same way every time: culture media for cells, lipids and polymers for formulations, scaffolds for tissues and organoids, Tourlomousis explains.

“Many are still developed by hand, lab by lab,” says Tourlomousis. Suppliers that deliver consistent, well-characterized materials, together with the data on how they were made, will become the partners regulated manufacturers prefer, because quality teams and regulators increasingly expect that record, Tourlomousis explains. Organoid producers are a clear example: a uniform organoid starts with a uniform scaffold, he adds.

The third is a process that travels. “Cell therapies increasingly need to be made closer to patients, and advanced formulations move between developers, contract manufacturers, and regional producers,” says Tourlomousis.

“Today the knowledge behind a process moves in PDFs and spreadsheets, and context gets lost at every handoff,” Tourlomousis adds.

“When the process lives in software as structured data, it becomes portable, and smaller facilities in more countries can make advanced products to the same standard,” says Tourlomousis.

“That data has to stay with the company that generated it, so our customers keep their data and choose their own AI models,” he adds. “That is how we are deploying our Loominus platform across R&D and manufacturing.”

“For companies that want to take part, my advice is practical,” Tourlomousis says. “Don’t treat automation as buying more robots”.

“Start by capturing what your experts know as structured data, connect your design, lab, and manufacturing systems so results flow between them, and pick one high-value process (a cell therapy step, a formulation, or a device) that you can automate end to end,” says Tourlomousis.

“That’s where new partners can plug in fastest, and where the returns show up first.”