AI Can Run the Lab—But Can It Find the Equipment?
Stop managing 21st-century research with spreadsheets. This guide shows how centralizing asset data bridges the disparity between AI tools and lab operations
A single HPLC system in a mid-size pharma lab involves at least three managed components: the physical instrument, the control PC, and the chromatography data system. Lab operations maintains the first, IT owns the second, and a separate informatics group often manages the third.
When an overnight run aborts due to a communication timeout, finding the root cause requires tracing logs across all three groups. Labs routinely accept this diagnostic friction as the cost of doing business. But the same data fragmentation that delays a batch release today can derail the lab’s AI transition tomorrow.
This whitepaper examines the operational and financial costs of disconnected equipment data in R&D labs and demonstrates how a unified lifecycle management layer resolves them. It details the case for connected infrastructure, the shared services model it enables, and a global pharmaceutical deployment spanning 100,000 assets. A concluding self-assessment worksheet helps lab operations teams map where their own equipment data stands today.
Siloed Instruments, Stalled Algorithms
When the technology research firm Gartner published its enterprise AI forecast in early 2025, the analysts delivered a blunt warning: 60 percent of corporate AI projects will collapse by 2026. These initiatives stall not from compute limits or flawed algorithms, but from a chronic lack of AI-ready data.
A 2025 Deloitte survey of pharma R&D executives quantified this gap. It found 65 percent of biopharma organizations operate on fully siloed or partially connected data infrastructure. Only 11 percent described their labs as capable of supporting seamless AI integration.
In most R&D facilities, this data bottleneck has a specific address: the equipment layer.
“Without the right infrastructure, even the most promising scientific ideas get bottlenecked by inefficiencies and data fragmentation,” says Pierre Merea, founder and CEO of newLab®, an enterprise lab operations platform. “For most organizations, equipment is managed manually through Excel spreadsheets and disconnected systems.”
The IT/OT Divide at the Bench
Modern scientific instruments exist in two management domains simultaneously. Labs treat the physical hardware as operational technology, logging maintenance records in external service provider portals. Connected PCs and software are treated as IT assets, managed in separate corporate systems. Those external portals offer limited API connectivity, so equipment data stays siloed at the point of service.
Closing that gap requires an enterprise service layer: a unified data model that forces physical and digital metadata to share the same operational logic. Instead of isolating calibration schedules, software licenses, and instrument bookings, this architecture consolidates them into a single system of record.
While corporate IT already relies on platforms such as ServiceNow to manage workflows and hardware, scientific equipment is typically walled off from this infrastructure due to proprietary vendor protocols and the specialized nature of R&D procurement.
Connecting the Lab to Your Digital Backbone
For Merea, the solution starts with operational visibility.
“You must know what you have, where it is, and how it is being used—from initial request through to decommissioning,” he says.
The IT/OT divide demands a platform that speaks both languages natively, and newLab is the only lab management solution native to ServiceNow. For organizations already running it, extending that infrastructure to cover scientific equipment requires no new systems. Those not yet on ServiceNow can deploy it as a bundled package alongside newLab.
Rather than integrating at the instrument firmware level, newLab operates at the workflow layer above it—capturing what instruments report through existing interfaces and routing that data into the enterprise record.
Lab operations, IT, facilities, and procurement all work from a single record, encompassing initial procurement requests, maintenance history, service provider work orders, and ERP financial data.
The Metadata Bridge to AI Readiness
The mismatch between raw experimental data and actionable AI insights often comes down to one question: what was the instrument doing when it generated that data?
“We bridge the scientific equipment world to provide an end-to-end view,” says Merea. By automating the collection of instrument metadata—equipment models, firmware versions, calibration dates, and configuration setups—newLab attaches operational context to every experimental run.
When an AI model interrogates a dataset, the hardware’s precise state at the time of the experiment is already part of the record—giving research teams a complete operational history behind every result.
What This Looks Like at 100,000 Assets
When a top-10 global pharmaceutical company set out to standardize 100,000 assets across 12 international R&D centers, it hit a data wall. Years of independent operation meant each site ran its own systems and classifications—identical instruments across three continents were digitally invisible to each other.
Deploying newLab gave the company a single operational layer across all sites. The rollout enforced a unified global taxonomy, integrated three external maintenance providers into one system of record, and linked instrument data to SAP for financial reporting.
The initial impact was measurable:
• Scheduling conflicts eliminated: With booking and maintenance logs drawing from the same record, scientists can only reserve instruments confirmed available for use.
• Capital savings: Enterprise visibility into utilization projects 10–20% reductions in capital expenditures through fewer duplicate purchases, and 5–10% in operating expenses.
• Time recovered: Lab operations teams and researchers expect to recover 10–20% of daily time previously lost to administrative overhead.
From Shared Infrastructure to Shared Science
“Modern labs are moving away from siloed ownership toward a shared service model,” says Merea. The newLab portal puts that model into practice—a single interface where scientists book instruments, request analytical services, and follow jobs through to completion.
Every request routes through the same system, giving central leadership real-time visibility into global capacity. Lab directors can identify underused assets and reallocate them before approving new capital purchases.
Shared utilization carries a sustainability dividend. Manufacturing and shipping account for the majority of a scientific instrument’s carbon footprint. Every procurement decision the shared model prevents is a direct reduction in environmental cost.
Three Things to Take Back to Your Lab
1. Equipment data dictates AI readiness. Fragmented instrument records are the primary reason R&D pilots stall. Structured operational metadata is the prerequisite, not a byproduct.
2. Visibility rewrites procurement. Enterprise-wide utilization data stops duplicate capital purchases. The financial return arrives before the first AI model deploys.
3. Daily workflows build the AI record. Every booking and service request logged through a unified system contributes to operational history.
