Every biotech lab in the world is quietly leaking money. Through lost time. Not broken equipment or failed experiments, but something much less visible: information that exists somewhere in the organization, but cannot be found when needed.
Every day, scientists scroll, search and cross-reference information. They delve into old reports, slides, and regulatory documents looking for a missing link, which might be the result of an earlier test of a molecule, a formulation note from an aborted trial, a data model buried in someone’s notes in a laptop. It’s a painful irony that often the answer already exists, but it’s hiding in plain sight, buried in the details.
For an R&D group of ten people, these invisible frictions cost about a million dollars in lost productivity per year. When applied enterprise-wide, that number reaches tens of millions. It’s a $10 million problem that no CFO has a line item for in their annual report, and yet every biotech industry leader feels the burden of missed milestones, delayed filings and growing fatigue.
The Hidden Discovery Tax
Biotechnology has become a data-rich but answer-poor industry because every process, whether formulation, validation, submission, creates more documents and notes than a human team can reasonably navigate.
Traditional software was designed for storage, not understanding. It’s good for keeping records, but not for linking them. All relationships between data elements must be programmed by database experts well before anyone can begin using the system. These assumptions about how information is related lock the system into a fixed way of thinking.
Once these rules are defined, the software cannot easily adapt when new types of connections appear later. Therefore, the knowledge of how data actually comes together has to happen in people’s heads. Scientists, technicians, and managers become the “connective tissue” of the organization, mentally piecing together fragments of information to make meaning.
This human knowledge leads to breakthroughs. But manually sifting through vast and scattered data takes years. It’s no wonder that it takes 12 to 15 years for an effective drug to hit the market. Not because science is slow, but because knowledge is trapped.
Teams duplicate efforts, repeat tests or make conservative choices, which constitutes a quiet but incessant drain on innovation capacity, distorting strategic decision-making.
In a mid-sized preclinical company, analysts may spend up to 40% of their week simply going through old protocols and test results to confirm previous results before designing new ones. A regulatory team needs six months to reconcile historical data for a filing, which could have taken days if internal knowledge was searchable and contextualized in the right way.
Why traditional software can’t solve this problem
To understand the scale of the problem, imagine the data landscape of a biotechnology company in which medicinal chemists store structures and reactions in one format, the clinical team keeps trial data in another, and regulatory affairs manages the stories in long-form text.
Conventional databases and search systems operate within these walls. They work well for structured data or predefined queries (“find compound ID 123”). But the real scientific questions are often relational: how did compound X behave in previous analog tests under thermal stress? What clinical signals correlate with this pattern?
Answering questions like these requires more than just retrieval. This means being able to connect meaning through formats such as text, tables, images, figures, and bring them together into a coherent idea. This is where most enterprise tools fail, and where some of the heavy “reasoning” work that scientists and doctors need to do can be made easier by AI.
The limits of cloud AI in a sensitive sector
Over the past two years, generative AI has promised to revolutionize R&D. Yet most cloud-based systems remain a failure for biotech leaders who must protect intellectual property.
Uploading libraries of in-house compounds, clinical notes, or proprietary methods to a cloud-based model presents unacceptable risk. Even anonymized data can reveal strategic intentions or formulation clues. For organizations whose valuation depends entirely on molecular intellectual property, such exposure is existential.
Additionally, many generative cloud models are known to produce plausible but incorrect, i.e., “mind-blowing” answers. Relying solely on extended language models (LLMs) with large yet limited pop-ups, they tend to provide answers where knowledge gaps exist.
In a scientific context, this is dangerous as decisions regarding assay dosage, stability, or endpoints depend on factual accuracy without any margin for error.
The future of biotechnology cannot rely solely on remote LLMs, but must rely on smarter, locally deployable AI systems that combine LLMs with knowledge networks, capable of eliminating hallucinations by indicating what is real and what is not.
From data repositories to knowledge networks
Imagine a system that automatically transforms every new document, data set, or experience note into a dynamic, interconnected knowledge graph – a digital map of how information is related. When a scientist asks, “What previous studies show patterns of resistance to this molecule?” “, the system does not search for file names; it reasons through relationships, and the answer appears in seconds, supported by exact references and traceable logic.
AI architectures capable of analyzing unstructured information, encoding it semantically, and retrieving contextual responses are already emerging in secure on-premises environments, making them viable for most biotechnology computing setups.
Instead of wading through endless files, scientists can engage in a dialogue with their organization’s collective intelligence – an AI co-pilot with access to all internal knowledge.
Saving time
Compressing R&D deadlines is strategic. Even a modest 30 percent reduction in the standard 15-year drug development trajectory through faster knowledge recovery time can reduce time to market by three to five years. The first company to gain approval in a therapeutic class often captures up to 90 percent of the market share. The second rarely reaches the break-even point.
The human impact of smart access
When researchers spend less time doing administrative research, daily work shifts to creative problem solving. AI-powered knowledge management systems give organizations institutional memory – a collective brain that never forgets or tires of being asked the same question twice.
For leaders, this means continuity. For scientists, this means freedom. For the company, this means speed without compromise.
Call for leadership
For CIOs, CTOs and R&D leaders, the competitive frontier in biotechnology no longer lies in cutting-edge chemistry and biology labs, but in speed of insight: how quickly your organization can surface, verify and act on its own data.
AI-powered knowledge networks will transform organizational learning in the same way that human genome sequencing revolutionized medicine. Leaders who act early will not only save time and costs, but they will also redefine how discovery happens.
A quiet revolution to come
The $10 million problem isn’t a mystery: it’s a flaw in the way knowledge is managed, where the greatest discoveries are hidden by the friction between what we already know and what we can find. Fixing it doesn’t require more data; this requires systems that can understand data.
Labs that embrace this change will discover that many of the answers they were looking for never really disappeared. They were just waiting to be connected. And this is where the future of biotechnology lies: faster, safer, more creative and, ultimately, more humane and humane.
Photo: bestdesigns, Getty Images
Swarbhanu Chatterjee, PhD, is the CEO and Founder of Aveti AI, a company developing data- and IP-secure AI systems that run entirely on-premises and are powered by proprietary local models and knowledge graph networks. Its flagship medical AI co-pilot, Answer Seeker AI, allows companies to load all internal documents into a unified AI “memory” with an infinitely scalable pop-up window, enabling instant answering of questions and simultaneous analysis of the entire knowledge base. With over a decade of experience building high-performance AI systems, Swarbhanu has led projects at global startups and enterprises, including PwC and American Express, helping organizations streamline workflows. He is also a member of Explainambiguity, an AI think tank based in Rome, Italy, specializing in the sustainable use of AI in the pharmaceutical sector. The group publishes regularly in peer-reviewed medical journals in English and Italian, advising companies in the pharmaceutical, medical device and related sectors in the EU.
This message appears via the MedCity Influencers program. Anyone can post their views on healthcare business and innovation on MedCity News through MedCity Influencers. Click here to find out how.
:max_bytes(150000):strip_icc()/Health-GettyImages-2203671859-f1cbd514af2141108a0059cde56d6971.jpg?w=390&resize=390,220&ssl=1 "What happens to your body when you add sauerkraut to your diet")
