AI? Again…?! Yeah, we know – the buzz around Artificial Intelligence (AI) is just about deafening. In the medical laboratory and the healthcare ecosystems, particularly. From predictive modeling to automated discovery, the promises are as transformative as they are in the realm of science fiction.
However, a recent industry analysis reveals a concrete reality: despite the hype, the vast majority of laboratories are basically unprepared to deploy various AI tools. In fact, when it comes to laboratory information systems (LIS), the numbers are almost depressing.
The problem isn’t the AI itself; it’s the lack of an AI-Ready Data Infrastructure. So, let’s break it all down and see if your lab is ready to adopt the future.
The Data Infrastructure Gap – by the Numbers
Yes, adopting AI seems to be a top strategic priority for most organizations, but the “readiness gap” is wider than many realize. Every day we read another article or report, claiming that global corporations are investing on the one hand, and getting ready for massive lay-offs on the other, and it seems that no one is actually talking about what to do with the various AI tools and possibilities.
Well, when it comes to the laboratory space, consider these hard statistics on current lab maturity:
- Manual Bottlenecks: As much as 50% of laboratories still rely heavily on manual processes for data capture and transfer.
- Digitization Deficit: It seems that only 15% of laboratories are considered “fully digitized,” meaning most data is still trapped in manual hard drives or paper-based systems.
- Lack of Proper Insight: Despite massive amounts of data, about 50% of lab stakeholders report struggling to extract actionable insights, mainly because of inconsistent metadata.
So, conclusion number 1 – in order to bridge this gap, labs must stop looking at AI as a plug-and-play solution and start viewing it as the final layer of a comprehensive AI-Ready Data Infrastructure. Sounds good, right? Now, let’s talk about what it actually means.
Why Your LIS is the Key to AI-Ready Data Infrastructure
Simply put, if your laboratory information system is nothing more than a digital filing cabinet, every one of your AI initiatives will fail. The reason is very simple – no matter how advanced and “smart” the AI tools can get, they still require structured, high-context, and accessible data.
Most medical labs that still use legacy systems experience one of the following: their live lab data is locked in vendor-specific formats, or their data is scattered across different instruments. No matter the case, if your lab is dependent on a legacy system, your AI models have nothing to learn from.
So, building a true AI-Ready data infrastructure through your LIS requires three critical and basic pillars:
- Harmonized Metadata: Your LIS must automatically standardize all metadata across all tests and instruments – because AI cannot interpret a result without the context.
- Vendor-Agnostic Connectivity: An AI-ready system must allow data to flow from every different instrument type into a centralized cloud environment. At the end of the day, data lock-in is the enemy of innovation.
- Compliance Governance: As regulatory oversight of AI increases, your infrastructure must provide a transparent, traceable path from the raw data to the AI-generated insight. This is a deal-breaker.
So, it seems we have it all figured out, right? Legacy systems – out! Next-Gen LIS platforms – in! Right? Well…
Mapping the Path for AI-Ready Data Infrastructure
The path to AI-readiness and everyday lab use of various AI tools doesn’t start with a chatbot, sad to say. The “experts” on TikTok will say it’s easy. However, the reality is that the road starts with process mapping. You must identify where your live lab data is locked in – and where manual hand-offs are creating dark data that AI can’t see.
And do not be fooled – there’s always dark data that the AI can’t see. If there’s one thing to remember, it’s that AI tools are smart, but they are not mind readers. They will do whatever you want them to, but only if you provide the pepper tools for them to do so.
The labs that win the next decade won’t be the ones with the flashiest AI models, but the ones with the most resilient AI-Ready Data Infrastructure. Our partners that have been using LabOS prove, daily, that Integration is way beyond connecting a pipette to a PC, but creating an ecosystem where every data point is an asset for future discovery. That’s how you properly use AI in your lab.
➡️ WANT TO KNOW MORE?
