5 min read
Adam Steinert, Chief Technology Officer
:
Updated on June 11, 2026
For decades, laboratory technology procurement has followed a predictable rhythm. Evaluate the vendor, negotiate the license, validate against your SOPs, and move on. The software is largely static. The validation effort is bounded. The vendor owns the lifecycle. Lab managers know this process well because they've done it dozens of times with LIMS platforms, ELNs, and instrument control software.
Artificial intelligence doesn't fit that model, and labs that learn this mid-project are the ones with expensive pilot programs that never make it to production.
We — James Smagala and Adam Steinert — spoke at the 2026 Lab Manager Leadership Summit and Lab Manager captured our thoughts in this article: "Lab AI Validation: Beyond the COTS Mindset."
In short, the off-the-shelf procurement mindset that has served labs well for years is the wrong frame for AI adoption.
We wanted to explore this idea for our readers as well.
A LIMS comes with a knowable validation cost. You scope it, fund it, execute it, and the system enters a relatively stable operational phase. AI systems work differently because the model you validate today is not the model you'll be running eighteen months from now.
Models get retrained. Instrument configurations change. Data pipelines shift. Performance drifts in ways that aren't always visible until they become a compliance issue or a quality event. All of that requires ongoing monitoring, revalidation, and governance infrastructure — none of which shows up in a software license fee.
Labs that budget AI as a one-time capital expenditure tend to underfund the operational side by a significant margin. The more accurate framing is to treat AI as a program with a cost curve that extends well beyond go-live, and to plan resource requirements accordingly before the first dollar is committed.
The instinct in many AI conversations is to assume that larger, more capable models produce better outcomes. In a regulated lab setting, that assumption tends to fail at the point where a scientist needs to explain or defend an output.
What actually holds up on the bench is purpose-built machine learning architecture: models trained specifically on lab data, scoped to a defined workflow problem, and paired with conventional software for the steps that require full auditability and data traceability. When an analyst needs to understand why a model flagged a batch or generated a particular result, a narrow, transparent system gives them somewhere to look. A large generative model often doesn't — and in GxP environments, regulators increasingly expect that transparency to be documented.
At Yahara Software, when we talk to clients about implementing AI, we often recommend starting small.
The regulatory landscape around lab AI has gotten considerably more concrete. FDA's good machine learning practice (GMLP) guidance and the finalized predetermined change control plan (PCCP) framework for AI-enabled device software functions have shifted the validation question from "did it pass initial testing?" to "how do you manage it when it changes?"
A PCCP requires labs to document, upfront and before deployment, exactly how a model will be monitored, what performance thresholds will trigger recalibration, and how updates will be verified without disrupting the validated baseline. Data provenance requirements under FDA 21 CFR Part 11 add another layer: every piece of data used to train, tune, or test an operational model needs to be fully traceable, with immutable logs of algorithm versions and training-data history tied to each sample.
Many labs start evaluating vendors before doing an internal assessment — exploring use cases and tools before understanding the state of their own data, their instrument connectivity, their governance posture, and their team's readiness to operate an AI system in a regulated environment.
This is actually something our team can help with, so if the state of your data seems like it could be a hindrance, come talk to us. Our team can even assist your organization with cleaning and organizing the data you have and developing SOPs for future data collection.
We built two fixed-fee services aimed at exactly this sequencing problem.
The AI Readiness Assessment takes one week. We score labs across six dimensions — data quality and accessibility, infrastructure, instrument connectivity, AI governance, workforce readiness, and regulatory posture — and deliver a plain-language report that identifies whether a lab is foundation-gap, pilot-ready, or scale-ready, along with the highest-leverage next steps from where they currently sit.
The Lab Prototype Sprint takes two weeks and produces working software built on the lab's actual data. Every sprint ships with an Honest Limits Report that surfaces production gaps upfront, so they don't appear for the first time during a scale conversation.
Both are designed to answer one question before a larger investment is made: does this actually work in our environment?
What's the difference between AI for labs and a traditional LIMS? A LIMS is a relatively static commercial platform that manages sample tracking, workflows, and data records. Laboratory AI tools are adaptive systems that learn from data, require ongoing validation and monitoring, and carry a significantly different cost and governance structure. Treating an AI system like a LIMS procurement almost always results in under-resourcing the operational and compliance side of the deployment.
How do we know if our lab is ready for AI? AI readiness in a regulated lab depends on more than interest or budget. The foundational factors are data quality and accessibility, instrument connectivity, existing governance and documentation practices, regulatory posture (FDA 21 CFR Part 11, GxP, ISO/IEC 17025), and whether your team has the skills to operate and monitor an AI system after deployment. Yahara's AI Readiness Assessment is usually the fastest way to get an honest answer.
Do we need to validate AI the same way we validate other lab software? Not exactly — and that's part of what makes AI procurement different. Traditional software validation follows a relatively linear process. AI validation under FDA's good machine learning practice framework is ongoing. Models can drift, get retrained, or produce different outputs as input data changes. A predetermined change control plan (PCCP) documents in advance how you'll monitor the model, what triggers revalidation, and how updates are managed without disrupting your validated baseline.
What kinds of AI tools are actually useful in a regulated lab environment? The most defensible AI applications in regulated labs are narrow in scope and transparent in their outputs: anomaly detection in instrument data, predictive maintenance, automated spectral analysis, intelligent sample routing, and workflow optimization in specific assay types. Large generative AI tools can support non-critical functions like documentation drafting or literature review, but regulators in GxP environments are increasingly explicit about where they draw the line on LLM use in critical applications.
How long does it take to go from AI pilot to production in a lab? It varies widely depending on data readiness, regulatory requirements, and organizational complexity. Labs with clean, accessible data and existing digital infrastructure can move faster. The more common delay is discovering infrastructure or governance gaps during scale-up that weren't visible during the pilot. Running a structured prototype sprint on real lab data before committing to a larger program is one of the most reliable ways to surface those gaps early. Yahara offers a two-week Lab Prototype Sprint that's a good launch-point for most labs.
What should we ask an AI vendor before purchasing a lab AI tool? Key questions include: How does the model handle data drift and retraining? What does the audit trail look like for every prediction or output? How do you support validation documentation for FDA 21 CFR Part 11 or GxP compliance? What is your predetermined change control plan approach? Can you demonstrate the system on our actual data before we commit? And, critically: what does the ongoing cost and support structure look like, not just the license fee?
Yahara Software builds custom AI and data solutions for regulated laboratory environments. Learn more about our AI Readiness Assessment and Lab Prototype Sprint.
Adam Steinert, Chief Technology Officer: Jun 11, 2026 3:33:29 PM
For decades, laboratory technology procurement has followed a predictable rhythm. Evaluate the vendor, negotiate the license, validate against your...