Scientific Workflows as Organizational Memory: How Pharma Encodes What Its Best Scientists Know

There is a category of knowledge that pharmaceutical organizations consistently underestimate until it is lost. It is the scientific judgment that experienced researchers accumulate over time through practice, the implicit standards they use to decide which evidence to trust, how to weigh conflicting signals, and when an analysis is strong enough to support a conclusion. Trial databases, knowledge management systems, and SOPs are not well designed to capture and preserve it.

This knowledge transfers poorly, resists codification, and often goes unnoticed within organizations until a decision fails.

Consider a senior scientist who retires after two decades in early oncology research. Her colleagues consider her thorough, as she's been known to strategically query genetic databases, weigh human evidence over animal data, and treat certain safety signals as disqualifying regardless of how rarely they appear. These workflows, the ones she runs instinctively, were never written down. To her, they were simply part of how good science was done: judgment refined through experience.

Within six months of her departure, two programs she would have rejected passed a review gate.

Process knowledge survived her departure. But her calibration, which sources to trust, which signals to take seriously, when the evidence was enough, did not.  

Why can't documentation solve pharma's knowledge loss problem?

SOPs, knowledge management systems, and institutional wikis exist to preserve and transfer scientific practice. But these systems are built around processes. They tell scientists what to do, which steps to follow, and in what order. They have no mechanism for the decisions that happen beneath that: how experienced researchers weigh conflicting evidence, which databases they trust for a given question, or how much genetic evidence they require before reaching a safe conclusion. That knowledge accumulates through years of practice and has no fixed address outside the person who holds it.

In 2025, 17 of the largest pharmaceutical companies reduced headcounts by more than 22,000 (FiercePharma, 2025). Many of these scientists were carrying judgment frameworks built over 15 or 20 years of practice. When they left, that judgment left with them, because most organizations have no mechanism designed to capture it.

What remains when those scientists leave is process without the calibration that made it effective.

How do scientific workflows encode scientific judgment?

A well-designed scientific workflow does something documentation cannot. Before a single step is written, it defines what the output must contain, what evidence dimensions must be addressed, and what the output must demonstrate before it is considered complete. That definition is a commitment about what constitutes sufficient scientific work for a given decision. And that commitment is where judgment lives.

When a preclinical safety workflow specifies that human genetic evidence must be present before a conclusion is drawn, it encodes the standard a senior safety scientist would apply based on experience. When a target biology workflow requires contradictory signals to be surfaced and acknowledged, it encodes a standard that informal practice would leave to individual discretion. The workflow preserves scientific judgment and makes it repeatable.

What separates a useful workflow from a performative one is whether the output is decision ready. The evidence basis for the conclusion is explicit. Contradictory data is surfaced rather than smoothed over. Confidence is calibrated honestly, particularly in areas where the evidence is exploratory and conclusions can sound more mature than the underlying data supports.

The result is a framework any scientist can apply, on any program, and produce an output that meets the same standard regardless of who ran it. That consistency is what makes encoded judgment useful at scale.

The Standardization Gap: Why do two teams in the same organization reach different conclusions?

Inconsistent outputs across teams are more common than most organizations acknowledge. Two teams, same company, same question, different conclusions. Both teams were capable, but the implicit standards behind their analysis differed.

One team searched three databases. The other searched seven. One weighted human genetic evidence heavily. The other relied primarily on published in vivo mechanistic studies. When a portfolio committee reviews both assessments side by side, the variation can look like scientific disagreement rather than methodological inconsistency. That distinction is important for which programs advance.

The most consequential output of standardization is interpretability. When scientific workflows establish a minimum evidence standard that applies across teams and programs, variation in conclusion reflects genuine differences in what the evidence shows, rather than differences in how the analysis was conducted. Reviewers at decision gates can focus on what the evidence says. Their attention goes to the science.

What does tailoring a workflow reveal about an organization's scientific standards?

Not all workflows are built the same way. Some address processes that are structurally similar across most large pharmaceutical companies: target biology assessment, mechanism of action analysis, and competitive landscape mapping. These deploy as templates with high fidelity to their intended purpose. A well-designed target biology workflow looks broadly similar whether it runs against an oncology target or a central nervous system (CNS) one. The core evidence gathered remains consistent: expression data, mechanism, and genetic precedent.

Other workflows require customization. A preclinical safety triage workflow at a company focused on CNS biologics operates under different constraints than one at a company focused on oncology small molecules. The evidence sources, risk tolerance, platform expertise, and standards for what constitutes a concerning signal differ based on the risk-to-benefit calculus specific to that modality and therapy area.

The customization process is where something important happens. To adapt a template workflow to an organization's specific context, someone must articulate what the organization's scientific standards are:

• Which databases do we trust for this question?
• What do we consider sufficient genetic evidence or clinical precedent?
• How do we handle contradictory signals from animal and human data?
• What does decision-ready mean for this kind of assessment, in our context, for our portfolio?

Most organizations have never answered these questions explicitly. The answers exist in the practice of senior scientists, in the implicit standards applied at review gates, and in the assumptions experienced reviewers make automatically. The workflow design process surfaces them as explicit commitments for the first time.  

Where does this leave pharma R&D?

Encoding judgment into a workflow is not a one-time exercise. Scientific understanding evolves, data sources change, regulatory standards shift. A scientific workflow library requires governance: version control, review cycles, and clear records of what changed and why.

When the judgment frameworks of an organization's best scientists are preserved, continuously updated, and applied consistently across every program, team, and therapy area, scientific quality stops depending on who happens to be in the room. It becomes a property of the organization itself.

That is a different kind of institutional resilience. And for an industry where the cost of a misjudged go/no-go decision is measured in years and hundreds of millions of dollars, it is worth building deliberately.

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If the judgment frameworks described here sound familiar, and the gap they leave sounds like a problem worth solving, Causaly works with R&D teams to build scientific workflows that preserve and scale that expertise. Request a demo to explore what that looks like for your organization.

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