When Complexity Creeps In: Why Life Sciences Need to Rethink Their Digital Foundation

It’s a pattern I’ve seen again and again in life sciences organizations: a large and growing patchwork of point solutions, each chosen and implemented for a specific purpose, yet required to be integrated together to enable end-to-end processes. An ELN to support scientists at the bench, LIMS for the lab, MES for manufacturing, Inventory Management, Freezer Management, and on and on. Spreadsheets fill some gaps here and there. Suddenly, what was supposed to streamline operations is doing exactly the opposite. Not only that, but critical and valuable data is sitting in multiple silos thus limiting the ability to use it, learn from it, and drive continuous improvement.

This isn’t the result of poor planning. It’s the inevitable outcome of addressing one problem after another as technology and software improve, all the while creating tech debt by layering complexity on top of complexity. There is a better way.

 

A Familiar Pain Point… and a Way Forward

In my experience working with global pharma, biotech companies, and even with other industries like F&B and chemicals, the common challenge isn’t a shortage of digital tools. It’s the lack of orchestration between them.

When systems aren’t aligned, and data lacks context, it becomes difficult to move at the speed that science and business now demand. Valuable time and resources are spent chasing answers across multiple systems where data needs to be aligned instead of acting on them to drive the science and the business. No matter how many “best-in-class” tools you add, there will still be gaps between the data silos.

That’s why I believe the next phase of digital transformation won’t come from another point solution. It will come from starting with the data model and process model first and establishing a solid foundation upon which to build the processes that drive your science and your business.

 

Where Orchestration Meets Intelligence

At L7 Informatics, we’ve built Enterprise Science Platform (L7|ESP®) around this very idea of a configurable, unified platform that orchestrates your processes and contextualizes your data within your existing ecosystem. Once you deploy L7|ESP as a central orchestration node in this new architecture, you’ve established that solid foundation with your data model and your process model, not ours. While L7|ESP’s own business apps (L7 Notebooks (ELN), L7 MES, and L7 LIMS) can be used, application replacement is not always feasible or even realistic, especially in large organizations. The beauty of this approach is that you can swap applications as needed at implementation and in the future, but the overall architecture does not change.  

This is where L7|ESP stands apart. Most solutions treat data as something to move or store. We treat it as part of the scientific process: modeled, contextualized, and orchestrated. That includes experimental data, QC results, manufacturing steps, and instrument outputs, all captured within your own process logic.

L7|ESP gives you full control over your data model with versioned, composable building blocks (entities, protocols, workflows, and workflow chains) that reflect how your organization actually works. And because the platform preserves context even when integrating external systems, you maintain full traceability and alignment.

It’s a flexible, future-proof foundation that supports both operational scale and AI-readiness, without locking you into rigid tools or vendor-defined workflows.

And as demands for cost reduction, speed to market, and AI adoption continue to rise, that kind of architectural agility becomes a real competitive advantage.

This isn’t hypothetical; it’s actually already been deployed at multiple companies we serve. And I’ve seen firsthand how orchestrated platforms can reduce tech transfer timelines, simplify validation, and enable seamless handoffs across functions, whether in a research lab or a GMP environment.

Here’s what that looks like in practice:

• Every step, sample, and action is captured, contextualized, and linked within a Knowledge Graph structure. You’ll need this for Generative AI and Agentic AI

• Batch records, QC results, Inventory, Stability Testing, Environmental Monitoring Data, User Qualification; it all ties together.

• And because it’s modeled from the ground up using FAIR principles, the data is actually usable, not just stored.

It’s a foundation that enables cross-functional alignment, accelerates implementation timelines, and drastically reduces the cost of change.

 

Why This Matters for AI… and for the Bottom Line

I’ll put it bluntly: if your data isn’t contextualized, your AI won’t work.

No algorithm can compensate for missing metadata, misaligned timelines, or siloed context. I’ve seen companies throw enormous budgets at AI initiatives, only to realize that their data foundation was never ready.

That’s why data contextualization is a prerequisite. And it’s also why orchestration platforms like L7|ESP, backed by normalized data models, a shared ontology, and orchestration logic, are becoming central to how life sciences companies prepare for the future. They enable the right foundation to drive faster decisions, better compliance, and scalable innovation, without requiring you to rebuild your tech stack from scratch.

 

The Case for Change

If your teams are still reconciling data manually…
If your digital workflows depend on offline documents…

If your tech transfer process still involves exporting and emailing files…

If every new assay feels like a fire drill…
If every new site rollout feels like a ground-up implementation…

Then maybe it’s time to start rethinking the structure.

The Life Sciences industry is at a turning point. The organizations I’ve seen succeed aren’t the ones with the most tools. They’re the ones with the clearest data foundation and have the agility to evolve with it. Those who move toward a unified, orchestrated approach will gain a competitive edge, not just in operations, but also in how fast they can innovate.

Let’s talk about how that shift could work for you.