In the latest Decoding the Digital Lab, Alec Edgar and Noel Hollingsworth of @uncountable_inc talk about what actually has to change before AI can meaningfully improve R&D. Listen to the full episode: hubs.la/Q04z0Bl10 #CSolsPodcast #AIinRandD #AIinLabInformatics #LIMS

More features do not automatically mean a better LIMS. The real question is whether the system fits your lab’s workflows, requirements, people, and plans for the future. Our LIMS Comparison Guide can help: hubs.la/Q04xmGY30 #LIMS #LabInformatics

More features do not automatically mean a better LIMS. The real question is whether the system fits your lab’s workflows, requirements, people, and plans for the future. Our LIMS Comparison Guide can help: hubs.la/Q04xmFDB0 #LIMS #LabInformatics

What makes LIMS comparison hardest? Too many options? Internal disagreement? Feature overload? Knowing what to prioritize? We’re curious what slows the process down most for your lab. #LIMS #LaboratoryInformatics

A good LIMS demo should answer your questions, not just show off features. Go in knowing which workflows matter, where your current system causes problems, and what success needs to look like. #LIMS #LabInformatics

Shiny new technology is tempting. But before you boldly go toward a new LIMS, get your requirements straight. A great system is only great if it actually works for your lab. #LIMS #LaboratoryInformatics

When comparing LIMS options, what matters most to your lab? Features? Workflow fit? Validation? Integration? Cost? The tricky part is usually deciding what actually matters before the demos begin. #LIMS #LabInformatics

Before you start comparing AI tools, define what success actually looks like. What process needs to improve? What should get faster, easier, or more reliable? What result would make the investment worth it?

What would AI have to improve in your lab to prove its ROI?

One point worth keeping in mind from our AI ROI guide: You do not need perfect data to start. You do need enough understanding of the data, workflow, and business problem to know what “better” is supposed to look like. hubs.la/Q04vYprV0

A useful AI project should have a measurable reason to exist. Not “we should be using AI.” More like: Reduce review time. Cut manual cleanup. Reduce risk. That is where ROI starts. 📘 Our guide digs into this: hubs.la/Q04vZjVM0 #ArtificialIntelligence #LabManagement

What is the biggest obstacle to getting a lab ready for new technology right now? Data quality? System integration? Requirements? Process inconsistency? Something else?

One requirement worth defining before adding AI: What decision or task is the technology supposed to improve? That sounds obvious, but it changes everything. #LabInformatics #ArtificialIntelligence

Curious what people are seeing in practice: When AI projects struggle in labs, is the bigger issue usually data quality, unclear workflows, integration, or something else?

AI readiness is not just a data problem. Before a lab adds new technology, it needs to understand the workflow and the requirements. Otherwise, the tool gets blamed for a problem that started much earlier. #LaboratoryInformatics #AI #LIMS

How many different manual transcription steps does a single sample go through in your lab between sample receipt and final Certificate of Analysis export? If the answer is more than zero, your instrument-LIMS interfaces need a look.

The biggest failure mode in lab informatics implementations? Treating a LIMS rollout as an "IT project" instead of an operational change management program.

Making lab data Findable, Accessible, Interoperable, and Reusable (FAIR) isn't a buzzword exercise—it's what lets you train predictive AI models 3 years down the road. If your instruments are dumping unparsed PDFs into an isolated drive, you're losing data value daily.

Computer Software Assurance (CSA) isn't about skipping validation. It’s about spending 80% of your time testing critical patient risks instead of writing 300 pages of low-risk paperwork. Work smarter on validation, not longer.

Before you sign a contract for a new LIMS or LES, bring two people into the scoping call: 1. The bench analyst running 40 samples a day 2. The site IT admin who has to maintain the API connections They’ll tell you what the sales deck won't.