The best first lab workflow to automate is a repeated, stable handoff with clear inputs, a result someone can check, and a safe manual fallback. Measure one normal week, count all human handling and rework, then test one narrow change beside the current process. Choose the workflow before the software.
Why documentation handoffs matter
Multimod founder Deep Majithia spent seven years working in gene editing. While designing and running end-to-end CRISPR experiments, he often captured notes on protocol printouts, paper towels, or his gloves. The details were useful at the bench, but they were scattered when it was time to write a complete electronic lab notebook entry.
The problem was not only typing. It was the handoff between doing the work and formally recording it. Once the context was scattered, documentation became an exercise in reconstruction.
That experience led to Verbex, a private voice lab notebook available on the App Store. Scientists can capture timestamped observations by voice while working, then review and edit a structured ELN-style draft. Transcription and draft generation happen on the iPhone. Verbex is an ELN companion, not a replacement for a laboratory's official ELN.
Verbex is now being refined through direct feedback from researchers in an academic lab who experience this documentation handoff firsthand. That work is exploratory and is not presented here as a client outcome.
Verbex is one response to one workflow. The broader lesson is that a small scientific team does not need a broad AI strategy before it can win time back. It needs one repeated workflow, one measurable finish line, and a safe way to test a better path.
What lab workflow automation means
Laboratory automation often brings to mind liquid handlers, robotic arms, and self-driving experiments. Those systems matter, but they are only one part of the category.
Lab workflow automation can also mean software that handles the repeated work surrounding the science. Common examples include:
- Collecting sample or client intake information.
- Preparing run sheets.
- Moving and naming instrument files.
- Checking that required fields are present.
- Reducing manual lab data entry.
- Preparing a report for human review.
- Routing approvals and status updates.
- Triggering a billing handoff after work is complete.
This guide covers the information work around the science, not robotics or scientific judgment.
The right first fix might use a feature that already exists in a laboratory information management system (LIMS), a simple form, a connection between two systems, or a small custom tool. It does not always require a new LIMS, an AI agent, or a large laboratory automation platform.
Laboratory workflow optimization is broader than automation. Sometimes the best improvement is removing a step, clarifying ownership, or turning on a feature the lab already has.
Start with the outcome, not the technology
"Where should this team use AI?" sounds practical. It is usually too broad to produce a useful answer.
Start with three narrower questions:
- What useful outcome is taking too long, consuming too much attention, or arriving inconsistently?
- Which repeated handoff is causing that friction?
- What would become measurably better if that handoff were fixed?
For a scientific service team, the outcome might be a sample ready to run, a client report sent, an experiment entered into the ELN, or an instrument result delivered to the right person.
An illustrative sample-to-report workflow might look like this:
A request arrives by email. Someone copies the details into a spreadsheet. A second person prepares the run sheet. The instrument exports a file to a local computer. That file is renamed, moved, opened, checked, and partly entered into another system. Someone assembles a report and emails the client for a missing detail.
This is an example, not a Multimod client case study.
No single step looks severe. Together, the steps create delay, rework, and many small chances for context to disappear.
Moving a file faster is not enough if its meaning gets lost. Consider a plate reader export. Moving the data file into the right folder may be simple. The harder part may be preserving what each well represents, including sample identity, condition, replicate, protocol, and operator. If a scientist still has to reconstruct that context from a plate map, a filename, and memory, the automation has only moved the bottleneck downstream.
Count context reconstruction as part of the work.
Map one completed piece of work
Do not begin by cataloguing the entire company. Choose one recently completed outcome and trace what actually happened.
Use a normal example, not the cleanest job of the month or the emergency that broke every rule. Then work backwards:
- Where did the request or sample enter the process?
- Which person touched it next?
- Which systems, spreadsheets, folders, or inboxes were involved?
- Where was information copied, renamed, reformatted, or interpreted?
- Where did the work wait?
- Who checked that it was correct?
- What event marked it as finished?
Write down the real path, including the unofficial steps. If the process relies on a sticky note, a personal spreadsheet, or someone remembering to send a message, that is part of the workflow.
Use this simple laboratory workflow analysis template:
- Outcome:
- Starts when:
- Finished when:
- Runs per week:
- People and systems touched:
- Hands-on minutes per run:
- Rework or follow-up:
- Information that must be reconstructed:
- Steps that still need human judgment:
- Manual fallback:
- Measure of success:
Completing this template for one real item is more useful than making a long list of possible AI tools.
Measure the friction for one week
The goal is not a perfect time study. It is enough evidence to compare one workflow with another.
For each repeated workflow, record:
| Question | What to capture |
|---|---|
| How often does it happen? | Runs per day or week |
| How much active time does one run consume? | Approximate minutes of human work |
| How many handoffs are involved? | People, inboxes, folders, or systems touched |
| Where is information entered twice? | Fields, files, and formats being copied |
| How much correction does it create? | Rework, missing information, and repeated checks |
| How long does it wait? | Delay between active steps |
| What does the delay affect? | Turnaround, billable capacity, client experience, or scientific progress |
Keep active work and waiting time separate. A report that waits two days for approval has not consumed two days of labor. The delay may still matter if it holds up a client, an instrument, or the next step in the lab.
Use two simple calculations:
Weekly time spent = hands-on time across all runs and people + rework + follow-up
Estimated weekly time saved = work removed - review time - exception handling - upkeep
The second number matters most. If an AI system drafts a record but a scientist spends the same amount of time checking and correcting it, no meaningful time has been returned.
Worked example
The numbers below show how to use the calculation. They are illustrative, not a client result.
- A lab processes 12 intake packets each week.
- Copying the details takes 15 minutes per packet, or 3 hours each week.
- Preparing run sheets takes another 90 minutes each week.
- Chasing missing details takes about 2 hours each week.
- Total weekly time spent is 6 hours and 30 minutes.
- A proposed process would still need 75 minutes for review and exceptions.
- Estimated weekly time saved is 5 hours and 15 minutes.
That result makes the workflow worth investigating. It does not prove that the automation will pay off. A pilot still has to show that the estimate holds, the output is reliable, and the ongoing cost is reasonable.
Apply the first-workflow test
A strong first automation usually has seven properties:
- It repeats. The work happens often enough for the improvement to add up.
- The path can be described. Someone can explain the normal inputs, decisions, and outputs.
- The needed information is available. It already exists in files, forms, emails, or systems the team can access.
- The result can be checked. A person can quickly tell whether the output is correct.
- A mistake is recoverable. The team can use the current process without damaging a scientific record or client relationship.
- Success is measurable. Time, turnaround, re-entry, errors, or response speed can be compared before and after.
- One person owns the outcome. Someone can make decisions and confirm when the workflow is good enough.
Choose by value and risk
Once several candidates are visible, place each one in a simple matrix:
| Lower risk | Higher risk | |
|---|---|---|
| Higher value | Start here | Investigate with the right technical, quality, and security owners |
| Lower value | Use an existing feature or leave it manual | Leave it alone |
Value can mean time saved, faster turnaround, less re-entry, fewer mistakes, or less delay. Risk includes damage to a scientific record, an error that is hard to detect, difficult recovery, or access that the tool should not have.
Start with the high-value, low-risk group. Good candidates might include:
- Preparing a draft report from approved source data.
- Routing a complete intake form to the right project.
- Checking whether required fields are present.
- Moving an instrument export into a consistent folder and naming structure.
- Sending a status update after a person approves it.
Poor first candidates include:
- Making a scientific interpretation.
- Approving a regulated record.
- Deciding how to handle a deviation.
- Replacing an ELN or LIMS across the company.
- Automating a process that changes every week.
- Running a process that has no clear owner.
For regulated work, the client's quality and validation owners must decide what can be automated and how it must be tested. The FDA's Part 11 guidance provides context for electronic records governed by FDA predicate rules, but it is not a substitute for the client's own quality and validation review.
Define what done means before building
"Implement an AI agent" is not an outcome.
Better finish lines sound like this:
- Remove one round of manual re-entry from sample intake.
- Produce a review-ready report draft from approved data without copying values by hand.
- Acknowledge each new client request within a defined window and place it in one visible queue.
- Return a measurable number of hours each week to the person maintaining the process.
- Shorten the time between an instrument run finishing and its result reaching the analyst.
The finish line should describe a change in the work, not the presence of new software.
Run the new process beside the old one
The safest first version runs beside the current process. It can use archived or synthetic data before it touches live work.
Let the new process create an output, but keep human review and the existing workflow in place. Compare the two. Record where they disagree. Test normal cases and known exceptions. Pay close attention to missing information, naming differences, unusual inputs, and steps that experienced staff handle without thinking.
A useful pilot should leave behind:
- A reliable process for ordinary cases.
- A clear list of cases that still need human review.
- A manual fallback.
- A simple before-and-after measure.
- Documentation the team can keep and operate.
The best fix may contain very little AI
Some workflows need a model to interpret unstructured notes, classify a document, or prepare a draft for human review.
Others need a form, a folder watcher, a mapping table, or a connection between two systems that should already be sharing information.
Use the simplest mechanism that can produce the outcome reliably.
The goal is to reduce work that no longer deserves a person's time, not to maximize the amount of AI in a lab.
Find the first five hours
The process can be reduced to six steps:
- Choose one completed outcome.
- Trace the real workflow behind it.
- Measure one normal week.
- Find the repeated handoff creating the most friction.
- Choose a low-risk change with a measurable finish line.
- Test it beside the current process before removing anything.
Five hours is not an industry benchmark. Multimod uses it as a screening threshold across a team's week, not per person. The baseline and pilot still determine whether automation will pay off.
Time is not the only form of value. An infrequent workflow that holds up a shipment, a client deadline, data integrity, or safety may deserve attention even when it consumes fewer than five hours. This exercise is specifically for finding recurring capacity.
Frequently asked questions
What laboratory process should a lab automate first?
Start with a repeated, stable handoff that has clear inputs, a result someone can check, and a safe manual fallback. Sample intake, file routing, required-field checks, and review-ready report preparation are often safer first candidates than scientific interpretation or final approval.
How can a lab identify workflow bottlenecks?
Trace one recently completed item from start to finish. Count every person and system it touched, each place information was copied, time spent on rework or follow-up, and any context that had to be reconstructed. Measure a normal week before comparing possible fixes.
How should a lab calculate automation ROI?
Start with estimated weekly time saved after review, exceptions, and upkeep. Multiply that time by a realistic value for the capacity returned, then compare it with build, software, support, and maintenance costs. Treat returned time as capacity, not automatic cash savings. Use pilot data instead of vendor promises.
Does a small lab need a LIMS to automate a workflow?
No. A LIMS can be useful, and its existing workflow features should be checked first. A small lab may only need a form, a file rule, a system connection, or a focused custom tool. The right choice depends on the workflow, data, quality requirements, and growth plans.
Is lab workflow automation the same as laboratory robotics?
No. Robotics automates physical lab work. Software-based lab process automation handles information and coordination around the science, such as intake, instrument files, reports, approvals, and status updates. Some labs need both, but they are different projects.
What does a lab workflow automation consultant do?
A lab workflow automation consultant maps the current process, measures its cost and delay, finds a low-risk starting point, defines success, and helps test or build the improvement. The consultant should also identify when an existing feature is enough and when a workflow should remain manual.
When Multimod may be a fit
Multimod may be a fit when a small scientific or technical team has a repeated software or data handoff, wants a focused improvement around its current tools, and needs human review plus a manual fallback. Multimod builds focused tools that run on the client's own hardware, keep the client's data under its control, and are designed to avoid lock-in.
For a company-wide LIMS replacement, laboratory robotics program, autonomous scientific decisions, regulated sign-off authority, or a large delivery and support team, another provider may be a better fit.
About Multimod Labs
Multimod Labs is a Boston-based, one-person AI and workflow automation studio. Founder Deep Majithia spent seven years in gene editing before starting the company.
Publicly shipped iPhone products include Verbex, a private voice lab notebook for bench scientists, and Rep x Reset, an on-device exercise app that uses computer vision to count work and time rest. These products are public examples of Multimod's product work, not client outcome case studies.
The fixed-price Multimod Workflow Assessment includes one team conversation, one week of analysis, and a prioritized plan the client keeps. If Multimod cannot find at least five hours a week worth reclaiming, the client does not pay.
To ask Multimod to review a workflow candidate, email contact@multimod.ai with the task, how often it happens, the people or systems it touches, and what finished looks like.