AUSTIN, Texas, January 26, 2026: Vasu Rangadass, Ph.D., Founder and Strategy Officer at L7 Informatics, is challenging the assumption that AI-ready data is sufficient for AI adoption in life sciences. Becoming AI-ready was the necessary first step. AI-actionable operations are the next phase, and they will determine whether AI can scale in regulated environments.
Over the last few years, many pharmaceutical and biotech organizations have invested heavily in AI-readiness by improving data quality, standardization, and traceability. That progress has enabled models that surface meaningful patterns and recommend interventions faster than humans can. Yet in practice, many AI outputs still stop at alerts, dashboards, or tickets because execution remains fragmented across disconnected LIMS, MES, and quality systems, along with spreadsheets, email chains, and manual handoffs.
“AI-ready means your data is good enough for AI to analyze and learn from,” said Dr. Rangadass. “AI-actionable means AI can participate inside the workflow itself in a way that is governed and auditable. Recommendations do not stop at a dashboard. They can be routed, constrained, reviewed, approved, documented, and operationalized as part of the process. That’s why an execution layer is so critical.”
Rangadass defines the execution layer as the architecture that allows recommendations to move through compliant paths, with context and traceability intact. He outlines three core capabilities required to make AI actionable in regulated operations:
● A shared operational ontology, so people and AI reason consistently across lab, manufacturing, and quality
● Workflow state management, so the system understands what can happen next based on current conditions and governance rules
● Controlled execution, so decisions can be translated into governed actions with routing, approvals, audit trails, and controlled records
Rangadass also emphasizes why context-aware orchestration and context graphs matter. Life sciences workflows are rarely linear. They branch, loop, pause, run in parallel, trigger holds, and require rework based on outcomes and state. Context graphs link what is happening now to the lineage and constraints that make it meaningful, including which materials were used, which method version applied, which equipment was involved, what changed, who approved it, and what actions are valid next.
Additional insights on execution layers, context graphs, and the transition from AI-ready to AI-actionable operations are available at: https://l7informatics.com/blog/from-ai-ready-to-ai-actionable-why-life-sciences-need-an-execution-layer/
About L7
Informatics
Founded in 2012 and headquartered in Austin, TX, L7 Informatics is redefining
digital transformation in life sciences with L7|ESP®, the execution layer that
unifies workflow orchestration and contextualized data across lab and
manufacturing. It enables organizations to flawlessly move from AI-ready
foundations to AI-actionable operations across R&D, CMC, manufacturing, QC,
diagnostics, and clinical environments. L7|ESP connects the dots between
disparate instruments, software, and teams by providing a single digital
scientific platform with flexible data modeling, extensive API integrations to
fit seamlessly into any ecosystem, and a full suite of built-in applications,
including LIMS, ELN, MES, Inventory, and Scheduling.For more information, visit
L7INFORMATICS.com.
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Contact
Jessica Tobey
L7 Informatics
jessica.tobey@l7informatics.com