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AI can preserve laboratory know-how that never made it into an SOP, but it cannot discover and validate that know-how by itself. The dependable pattern is a human-led workflow: elicit experience through interviews, observation and debriefs; use AI to transcribe, classify and connect the material; have scientists approve the result; then publish it as versioned documentation and retrieve it with citations.
This approach keeps the context behind a decision—sample condition, instrument behavior, sensory cues, exceptions and troubleshooting—along with the person, date and evidence needed to audit or update it.
What tacit knowledge means in a laboratory
Tacit knowledge is practical judgment that experienced scientists use but rarely write down in raw instrument files or formal procedures. It includes recognizing when a sample “looks wrong,” deciding whether an instrument reading is plausible, adjusting a step because of a known batch effect, or choosing a troubleshooting path after an unusual failure.
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The OECD describes a related foundation of scientific practice: “Scientific theories rest on tacit knowledge shared by all scientists due to their experience as people in the physical, social and mental worlds.” In a laboratory, preserving that experience means recording not only instructions, but also the conditions and reasoning that make an instruction useful.
What AI should do—and what scientists must still do
AI is well suited to handling the volume and structure of captured material. It can turn a recorded conversation into searchable text, separate procedures from exceptions, identify repeated terms, suggest links between a troubleshooting note and an SOP, and draft a consistent knowledge record. It can also retrieve approved passages when someone asks a context-specific question.
It is not a substitute for elicitation or scientific accountability. A model cannot reliably infer whether an offhand comment was a validated practice, a one-time workaround or an unsafe shortcut. A subject-matter expert must decide what is accurate, in scope and fit for operational use.
| Activity | Useful AI contribution | Required human control |
|---|---|---|
| Capturing experience | Transcription, speaker separation, topic and terminology extraction | Choose the participants and ensure that important context and dissent are recorded |
| Structuring knowledge | Classify statements as steps, conditions, exceptions, failure modes or rationale | Check that the classification preserves scientific meaning |
| Standardizing language | Suggest controlled terms, links to instruments and duplicate detection | Approve vocabulary, units, ranges and scope |
| Publishing guidance | Draft a manual section, SOP supplement or knowledge-graph record | Two domain reviewers reconcile conflicts and sign off |
| Answering questions | Retrieve relevant approved passages and compose a cited response | Verify the answer, maintain permissions and provide a correction route |
A capture-to-assistant workflow
The following sequence keeps the original voice available while turning it into controlled, reusable laboratory knowledge.
1. Select a workflow where lost context has a cost
Start with a process that has recurring handoff, training or troubleshooting failures. Examples include sample preparation with many judgment calls, an instrument method that depends on condition checks, or an analysis pipeline whose interpretation changes with pre-analytical details. A narrow, high-value workflow produces a manageable first corpus and makes review responsibilities clear.
2. Elicit the knowledge from experienced staff
Use more than a questionnaire. Unstructured interviews and focus groups can reveal the story behind a decision; observation can expose physical and sensory cues; debriefs can capture what went wrong and how it was recovered. Storytelling sessions, lessons-learned sessions and “legacy talks” were identified as effective methods in the University of the Philippines Open University thesis Design, Development, and Testing of Tacit Knowledge-Capturing Protocol of PHilMech Scientists (Subaba, 2024).
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Ask for exceptions explicitly. Useful prompts include: “What tells you to stop?”, “Which result looks acceptable but is actually suspicious?”, “What do you check before repeating the run?”, and “When does this rule not apply?” Record the instrument model, sample type, method, date and participants while the conversation is happening. Preserve the raw audio or video; a transcript is a derivative, not the original evidence.
3. Transcribe and segment the material with AI
Run speech-to-text, then divide the transcript into atomic statements. Label each statement as a procedure, prerequisite, condition, acceptable range, warning sign, exception, failure mode, troubleshooting action or rationale. Keep uncertainty and disagreement visible instead of allowing a summary to flatten them into a single rule.
Link every segment to its speaker, instrument, sample, method and date. Those links let reviewers distinguish a broadly approved practice from a personal preference or a workaround used on one instrument revision.
4. Reconcile and approve the interpretation
Have two domain reviewers examine the classified material. They should resolve conflicting advice, mark statements that need an experiment or policy decision, and define the scope of anything that becomes an operational instruction. The approved output can be a manual, an SOP supplement, a job aid or a structured knowledge-graph record. Retain rejected or superseded statements with their reason, rather than deleting the history.
The 2026 Taiho Pharmaceutical case study describes a comparable knowledge-engineering workshop: senior researchers’ experience was extracted through structured group discussion, converted into standardized manuals, and prepared for generative-AI chatbots, training content and safety education. The important control is the workshop and review step, not automatic publication by a model.
5. Attach provenance and laboratory metadata
Trustworthy reuse depends on more than a transcript. The 2024 Learning Health Systems paper by Wittner and colleagues emphasizes precise documentation of pre-analytical conditions, analytical procedures and data processing, with traceable, machine-actionable lineage for data and specimens.
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Use a controlled record for each knowledge item:
| Field | What to record |
|---|---|
| Claim or instruction | The approved statement in clear operational language |
| Context and scope | Project, assay, sample type, population, instrument version or other limits |
| Prerequisites | Training, calibration, materials, environmental conditions and permissions |
| Steps and ranges | Ordered actions, units, tolerances and acceptable results |
| Warning signs and troubleshooting | Observable symptoms, likely causes and safe next actions |
| Evidence or source | Transcript passage, dataset, experiment, SOP or guideline supporting the item |
| Ownership and review | Author, reviewers, approval decision and effective date |
| Change history | Superseded version, reason for change and links to related records |
| Lineage links | Relevant samples, instruments, methods, datasets, analyses and results |
This structure aligns with the 2023 article Ten simple rules for managing laboratory information, which treats laboratory information as experimental metadata, computational processes, inventories and equipment information that must be transformed into useful scientific findings.
6. Publish only approved artifacts and index them for retrieval
Place the reviewed records in the systems where staff work: a controlled manual or SOP repository, an electronic laboratory notebook, an electronic laboratory record, a laboratory information management system, or a permissioned knowledge graph. Keep immutable links to the source transcript and the approved version.
For question answering, index those approved artifacts in a retrieval-augmented generation (RAG) system. RAG searches the laboratory’s own documents for relevant passages and gives them to a language model before it drafts an answer. Configure the assistant to show the source passage, document version and confidence or uncertainty, and to say when no approved source answers the question.
An ARUP Laboratories and University of Utah proof of concept assembled job aids, SOPs, multidisciplinary guidelines and classification systems, then used RAG with locally run open-source language models. The reported preliminary use case covered questions about sample handling, testing protocols and data interpretation. It demonstrates a feasible pattern, not a guarantee that every laboratory question will be answered correctly.
7. Monitor corrections, drift and retirement
Log unanswered questions, user corrections, conflicts between documents and cases in which a result exposed a missing condition. Review those logs on a defined cadence. When an instrument, reagent, method or policy changes, mark affected knowledge items for review and retire obsolete versions from normal retrieval while preserving their history.
Comparing implementation options
There is no single “AI capture” product. The practical choice is a combination of elicitation method, data model, deployment model and review process.
| Approach | Capture richness | Standardization and provenance | Retrieval and privacy | Typical burden and maturity |
|---|---|---|---|---|
| Interview, observation and workshop followed by a manual | High: preserves stories, exceptions and sensory cues | Strong when reviewers use a controlled template and version history | Can support cited search; access follows the document repository | High human review; established practice |
| Form-only or free-text collection | Lower: efficient for discrete observations but can miss context | Forms improve consistency; free text needs later normalization; lineage depends on metadata design | Easy to index, but answers may lack the rationale behind a decision | Lower initial effort; review still required |
| Locally run RAG over approved laboratory documents | Depends on the source corpus rather than the model alone | Can preserve document versions and source passages | Local deployment can keep proprietary or unpublished material inside the laboratory boundary; answers should include citations | Integration and evaluation work; demonstrated mainly in proof-of-concept studies |
| Multimodal inference and continuously updated knowledge graphs | Potentially very high, including video and instrument context | Requires reliable ontology, lineage and change controls | Privacy, access and audit requirements become more complex | Emerging; autonomous conversion into validated SOPs is not established |
Evaluate options against eight axes: richness of capture, strength of standardization, depth of provenance, privacy and deployment location, retrieval quality, reviewer workload, integration with electronic laboratory notebooks or LIMS, and the change-management effort required from staff.
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Governance controls that keep an AI assistant safe
Protect sensitive source material
Apply least-privilege access to recordings, transcripts and source documents. Patient information, proprietary methods and unpublished results should be visible only to authorized roles. A model should inherit the permissions of the underlying repository rather than expose a passage merely because it is relevant to a question.
Require sign-off before operational use
No AI-generated summary should become an SOP step without expert approval. Separate draft status from effective status, identify the responsible owner, and require reviewers to confirm units, ranges, instrument models and safety implications.
Make every answer auditable
Store the retrieved passages, document versions and answer timestamp. Show users which sources support the response. Microsoft Research’s CLeAR framework calls for documentation that is “Comparable, Legible, Actionable, and Robust”; that documentation supports responsible use, evaluation and auditing of AI systems.
Provide correction and escalation paths
Give users a visible way to flag an incorrect or incomplete answer. Route the report to a named domain owner, record the resolution, and update or supersede the knowledge item. The assistant should direct users to a human when sources conflict, the requested condition is outside scope, or a safety-critical decision lacks approved guidance.
Preserving knowledge when a senior scientist leaves
Offboarding should be a knowledge-capture event, not just an account-closure checklist. Schedule legacy talks and focused interviews before the departure date; walk through high-risk workflows; record exceptions and troubleshooting stories; and have peers review the resulting artifacts while the expert is still available to clarify them.
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The PHilMech protocol integrated this type of capture into retirement offboarding. Its pilot outputs included manuals, podcasts, videos and an online repository. A laboratory can use the same principle with its own controlled repositories, linking each artifact to the relevant SOP, instrument and sample workflow.
A physical laboratory notebook also remains useful for recording observations, deviations and troubleshooting context at the bench. It complements electronic laboratory records and provenance systems; it does not replace them or provide the versioned, searchable source required for a governed AI assistant.
What is established and what remains early
Interviews, debriefs, structured documentation, provenance requirements, version control and human review are established components of knowledge management. They do not depend on a language model and should remain in place if an AI project is paused.
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Systems that infer procedural nuance from video, continuously update a knowledge graph, or autonomously convert expertise into validated SOPs remain emerging. Published work identified for this topic does not provide a generalizable controlled statistic for productivity or reproducibility gains. A laboratory should therefore measure its own unanswered-question rate, correction rate, review time and training outcomes rather than claim a universal percentage improvement.
Quick Recap
A practical starting checklist
- Select one workflow with repeated handoff or training failures.
- Identify experienced staff, reviewers, document owners and access roles.
- Record interviews, observations and debriefs while preserving the raw files.
- Use AI to transcribe and classify statements, but retain uncertainty and conflicting accounts.
- Have two domain experts reconcile the material and approve a versioned artifact.
- Attach metadata and lineage for samples, instruments, methods, data and decisions.
- Index only approved artifacts in a permissioned RAG system that returns source-linked answers.
- Track corrections, unanswered questions, document drift and obsolete procedures.
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