| LIMS vs ELN: Converged on Features, Not on the Unit of Work  A LIMS is built around the sample, an ELN around the experiment, and every shared feature sits on one of those two spines. A 2017 survey synthesis found 98 percent awareness of electronic notebooks against 11 percent use, with recurring cost the barrier. Many labs need both; the expensive mistake is expecting one to do the other's job.[Read More]
Top 10 Signals Your Lab Is NOT Ready for AI  The post identifies 10 warning signs that a laboratory is not ready for AI, including biased data, paper-based workflows, siloed systems, inconsistent naming, and weak leadership support. It also outlines four steps labs can take to build a FAIR-compliant, AI-ready informatics foundation.[Read More]
The Future-Ready ELN: A Strategic Guide to Selecting the Right ELN System for Life Sciences  With 71% of ELN initiatives failing to realize users’ objectives, selecting the right system requires more than tactical software acquisition. A vendor-agnostic framework helps organizations evaluate interoperability, scalability, and regulatory rigor while aligning scientific informatics with business objectives and building a unified digital strategy for long-term ROI.[Read More]
From Legacy Workflows to Paperless Labs: Track Every Sample, Process, and Decision  Missed the live webinar? Watch the recording to understand how connected digital lab workflows help streamline sample tracking, automate data capture, and enhance traceability across the laboratory. Featuring a real-world use case, this session demonstrates how paperless processes can improve efficiency while supporting regulatory compliance.[Read More]
How AI Is Cutting Protocol Amendments and Accelerating Clinical Trials  Protocol amendments add costly delays to drug development, yet roughly 23% are considered preventable. AI helps sponsors catch design flaws, assess feasibility, and simulate operational performance before enrollment begins, reducing costly mid-trial changes while improving protocol quality and shortening development cycles.[Read More]
S02 E10: Outside the Regulated Bubble: Data Foundations & AI Multipliers at Penn Color  Structuring R&D data turns AI into an organizational "high-speed librarian," multiplying scientists' expertise, drastically slashing formulation trial-and-error, and driving real ROI. Lisa Richard and Kyle Smith explore how coatings and performance materials companies can digitize faster and smarter while unlocking greater value from their R&D data.[Read More]
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