Most ‘autonomous lab’ projects fail before the robot even moves.
Most ‘autonomous lab’ projects fail before the robot even moves.
It’s tempting to think autonomy is a hardware problem: buy a robot, connect a few instruments, and you’re done.
In reality, autonomy is a feedback-loop problem. If the loop from experiment → data → analysis → insight isn’t reliable, the robot just makes you faster at generating messy, unusable results.
Here’s a simple 4-step climb to make autonomy practical:
Step 1: Digital logging
Autonomy starts with trustworthy data capture. If experimental conditions, methods, and context live in free text, scattered folders, or multiple “final” spreadsheets, you don’t have a foundation, you have ambiguity.
Step 2: Automated analysis
This is where many teams discover the real bottleneck. Experiments run quickly, but turning outputs into comparable, validated results takes forever. Automation here means repeatable pipelines, QC checks, and consistent outputs, so every run becomes a clean learning signal.
Step 3: Closed-loop suggestions
Now an AI algoritm can recommend the “best next experiments” under uncertainty. But it only works when constraints are encoded and recommendations are explainable and executable - otherwise chemists will (rightfully) ignore the model.
Step 4: Closed-loop execution
This is the full loop: the lab executes recommendations, data flows back automatically, the model updates, and the cycle repeats. To make it real, you need orchestration, robust integrations, monitoring for edge cases, and audit trails, otherwise humans end up babysitting.
The uncomfortable truth is that many autonomy roadmaps jump straight to Level 4 while Levels 1–2 are still held together by manual exports and heroic effort. When the feedback loops are solid, hardware becomes an accelerant not a crutch.
Which step is your lab on today and what’s the biggest blocker to moving up one step?
If you’re working on this and want to compare notes, feel free to DM me. I’m happy to share what we’ve learned from building and deploying these feedback loops with partners.
