Could your next colleague be AI?

AI is after something you have. Not your job, but your messy data.

Across biotech startups, research institutes, and Contract Research Organizations (CROs), a quiet transformation is underway. The way research teams capture, connect, and act on scientific knowledge is being fundamentally reworked by AI.

The infrastructure was always the bottleneck#

The timing is not accidental. Life science R&D has long been held back by the infrastructure surrounding it. A typical research team today juggles multiple tools and repositories of data that do not efficiently connect or communicate with each other. Stitching everything together manually, copying results between platforms, reformatting outputs, and hunting down the right protocol version can consume hours of precious time that a researcher could instead spend on driving real innovation and impact forward. The McKinsey Global Institute put the cost of that at close to a fifth of the workweek back in 2012, time interaction workers spend just hunting for information that already exists somewhere in their own organization.

What is missing is somewhere the work adds up#

Scientists and R&D teams have long wanted one place where the work adds up rather than scattering. In the past that was a bureaucratic nightmare. Manual annotation and documentation consumes your time and energy. Strict and rigid schemata were created to facilitate inter-system communication. The result, however, was to make work inflexible and to restrict the space to pursue and derive creative solutions. This made maintaining such a system infeasible for all but the largest companies.

AI has the power to close this gap by acting as an intelligence layer that connects everything a research team already does. This means no more strict schemas, no more documentation headaches, more flexibility to innovate and, perhaps most importantly, more time and energy to pursue the hard problems that scientists thrive on solving.

For a company, maintaining and accessing company-wide knowledge suddenly becomes feasible. For scientists, it means a well-read, never-sleeping research partner that can reach back into what the team has already written, run the analysis rather than just describe it, and help you reason through what to do next. It does not know things the way a colleague does; it retrieves them, and it shows you what it retrieved. That is what makes the work connected and, on a good day, fun again.

What that looks like in practice#

This is what we are building Biocompile for. Rather than adding another window to an archaic tool stack, it gives the work one home: the literature, the files, the analysis and the reasoning behind a decision stay connected inside the project they came from, and every answer can be followed back to what it rests on. An assistant does the searching and the running. A person decides what goes on the record.

A researcher can query their own experimental history alongside the context of all their company or team data, cross-reference the published literature and the patent landscape, run analysis without writing the pipeline themselves, and document as they go, without switching windows, reformatting exports, or losing context.

For researchers worried about their role#

The argument is a simple one. Time a scientist spends reformatting data is time not spent interpreting it, and the reformatting is the part nobody chose to do.

In a 2018 survey of more than a thousand US employees by Panopto and YouGov, roughly 42% of institutional knowledge was found to be unique to a single employee. When such a person leaves the team, hard-won process understanding often walks out the door with them. When knowledge is captured, shared, and searchable rather than stored in the head of one person, teams become more resilient.

When documentation happens in real time rather than retrospectively, reproducibility stops being a chore owed to your future self, and data can be reused across projects. A study carried out by PwC for the European Commission put the cost of research data that cannot be found or reused at a minimum of €10.2 billion a year for the European scientific system, much of it from experiments being needlessly repeated. AI can certainly help with this.

Your next colleague may well be digital, but it will not be after your job. It will instead be doing the parts of it nobody ever liked in the first place, and collaborating with you and your team so you can get back to the parts only you can do.

If you are excited to explore how AI could enable your own organization, try Biocompile now for free.

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Sources

• McKinsey Global Institute, The social economy: Unlocking value and productivity through social technologies (2012). Interaction workers spend an estimated 19% of the workweek searching and gathering information.
• Panopto and YouGov, Workplace Knowledge and Productivity Report (2018). Survey of 1,001 US employees at organizations with 200 or more staff.
• PwC EU Services for the European Commission, Cost of not having FAIR research data (2018). Minimum annual opportunity cost of €10.2 billion for the European scientific system.