Headcount is a proxy for complexity—but it's an imprecise one. What actually stresses a lab management platform is parallel programs, cross-functional handoffs, multiple sample types, and concurrent workflows. Ask vendors to demo the platform with multiple active programs running simultaneously, not a single linear workflow. A tool that looks clean with one project often fragments under three.

When a lab doubles in size, the bottleneck is rarely technology—it's people getting up to speed. A platform that requires weeks of training per user, or that depends on informal knowledge to use correctly, creates a recurring tax on growth. Look for platforms with structured templates, enforced metadata fields, and clear role-based interfaces. New scientists should be able to produce comparable data from their first week.

Some platforms are optimized for a specific team size or regulatory context and hit a ceiling beyond it. Ask directly: "What does it look like when a customer transitions from early-stage to IND-enabling studies on this platform?" If the answer involves significant reconfiguration, data migration, or a new product tier with different architecture, that's a hidden cost to factor in.

Audit trails—who did what, when, and to what record—should not be something you enable later or pay extra for. They should be automatic and retroactive from day one. If a platform treats audit logging as an enterprise add-on, assume it was not architecturally designed with traceability in mind. In regulated environments, retrofitting governance is significantly harder than building on a platform where it's native.

Basic platforms offer role-based access at the system level: admin, editor, viewer. That's insufficient for labs running multiple programs, CROs with client data segregation requirements, or teams preparing for GMP-adjacent work. Governance-ready platforms offer access control at the program, project, and record level—so that a scientist on Program A cannot inadvertently view or modify Program B data.

Governance is not just about storing records. It's about capturing what changed, why, and what was done about it—in real time, inside the same system where work happens. If a team has to log deviations in a separate CAPA tool, changes in an email thread, and protocol updates in a shared drive, the governance picture is never complete. Look for platforms where deviation capture, review, and resolution happen within the operational record.

Most early-stage labs don't operate under 21 CFR Part 11. But the labs that don't plan for it often face an expensive system replacement when they reach clinical or manufacturing stages. A platform with Part 11-ready architecture—electronic signatures, controlled records, validated workflows—can be activated progressively, without data migration or re-implementation. Ask vendors not just "do you support Part 11?" but "what does the transition look like for an existing customer?"

AI and analytical workflows don't fail because labs lack data. They fail because the data isn't structured. PDFs, free-text notebooks, and untyped spreadsheet exports are storage—not data infrastructure. A platform that enforces typed metadata fields, consistent naming, and linked records between samples, experiments, and results produces data that can actually be queried, modeled, and fed into AI pipelines without manual preprocessing. Ask vendors to show you what a data export looks like—not what the UI looks like.

AI readiness is not a feature you toggle on. It's a consequence of how the platform stores and exposes data. A platform with a robust, well-documented API—covering not just file retrieval but record-level access to experiments, samples, results, and metadata—gives your data science and ML teams a foundation to build on. A platform where "integration" means CSV export is a ceiling, not a foundation. Ask for API documentation and reference customers using the API programmatically in production.