Why Orchestration—Not Integration—Is the Future of Digital Life Sciences

by Teodor Leahu | posted on April 17, 2025

Despite years of digital transformation, many life sciences organizations are still held back by an invisible force: the technical debt of integration Every new tool, every system upgrade, or every workflow tweak introduces friction. What began as a best-of-breed architecture has become a brittle web of point-to-point connections. Integrations break. Projects stall. Innovation slows to a crawl. But let me tell you: it’s not a software problem—it’s an architecture problem. And solving it requires a strategic shift—from connecting systems to orchestrating business processes.

Most digital roadmaps in life sciences were built with good intentions: deploy the best IT system for every job. But as systems and scientific modalities grew, so did the complexity of the IT landscape. Each system required dedicated integrations. Each change demanded careful coordination. And as the ecosystem expanded, the cost of change skyrocketed. Because every system evolves on its own timeline—driven by independent release cycles and departmental tweaks—maintaining point-to-point connections becomes increasingly fragile and unsustainable. This is the trap of traditional integration. And it’s why many organizations find themselves slowing down just when they need to move faster.

Now imagine a different approach—one where systems don’t talk directly to each other but instead communicate through a shared orchestration layer. This layer doesn’t just relay data—it understands processes. To be precise, it decouples the underlying systems, allowing each to evolve independently, and more importantly, it manages the HOW of your scientific operations—standardizing, routing, and contextualizing tasks across your ecosystem. In short, orchestration isn’t a technical fix. It’s a strategic foundation. It becomes a control system for the ecosystem.

Systems don’t talk directly to each other but instead communicate through a shared orchestration layer.

At the most basic level, the orchestration system eliminates the need for 1,000 brittle connections. Each system connects once—to the orchestration layer—and that’s it. You need to replace your ELN? Just update the single connector. You want to scale out your QC tools across regions? Go for it; there won’t be any ripple effects. The same logic applies to integrating instruments and equipment in the ecosystem into software systems. The result is a more agile, resilient IT landscape—one that doesn’t crumble with every change or modification.

If technical decoupling gives you flexibility, data decoupling gives you control and an onramp to AI.

But the benefits go further. If technical decoupling gives you flexibility, data decoupling gives you control and an onramp to AI. In most vendor-led ecosystems, the data model is fixed. You inherit someone else’s schema, someone else’s assumptions about how your science works. This applies to your data orchestration system as much as it does to your individual point solutions (LIMS, ELN, MES etc). That rigidity doesn’t just force costly mappings—it holds your hostage to the vendor data model. Let me clarify this point for you: if your scientific data has been configured around the data model of System A, switching to System B becomes nearly impossible. If replacing one ELN is painful because of the need to replicate hundreds of point-to-point connections, replacing your orchestration system with its off-the-shelf data model becomes impossible. We need to flip that dynamic. You start with your science—your terminology, your scientific modalities, your scientific processes—and build a system-agnostic semantic data model. This model becomes the single source of truth, independent of both the orchestration system and the systems (and instruments) surrounding it. And because that data model is yours, it stays consistent—even when your instruments and software systems change. As your science and modalities evolve, the semantic data model adapts because YOU control it, not the vendor.

The data model is yours, it stays consistent—even when your instruments and software systems change.

This architectural shift has immediate practical implications. Take something as seemingly simple as sample registration. Today, the process is often hardwired not just to a specific system—but to multiple systems. For example, ELISA samples might need to be registered in Vendor ELN-1, while HPLC data has to go through Vendor LIMS-1. Scientists are left navigating a fragmented landscape: they must remember which test maps to which system and follow a different registration process each time. It’s not uncommon to register samples five or ten different ways in a single week. With the orchestration layer, that complexity disappears. The scientist simply says, “Register this sample.” The orchestration layer routes the instruction to the appropriate system, formats the data accordingly, and ensures it’s logged in the correct place. The underlying vendor systems and instruments do not matter. The process stays the same. That’s business agility—built right into the architecture.

This is precisely the philosophy behind L7|ESP. We didn’t build another integration platform. We built a composable, regulatory-compliant, low-code orchestration system where your organization defines the data model (using the L7 Reference Data Model), the workflows, and the business logic. Before you can do anything in L7|ESP, you start by modeling your science—your way. Then, you layer in the systems, the instruments, and the automation. L7|ESP becomes the digital brain, routing tasks, contextualizing data, and preserving scientific meaning from start to finish. It’s not just a platform. It’s a new way to think about digitalizing and automating your scientific operations. It also scales seamlessly as you enter the world of GenAI and Agentic AI by contextualizing your data and generating knowledge graphs by semantically linking disparate pieces of data.

True digital transformation in life sciences means building an architecture that’s flexible, sustainable, and ready for what’s next. 

This isn’t just theory. According to a recent McKinsey & Company article, life sciences organizations that consolidate software and migrate to cloud-based architectures can free up as much as 30% of R&D IT spending—resources that can then be reinvested into innovation, advanced automation, and AI.¹ Orchestration is what makes this level of efficiency possible. It reduces the burden of legacy integration, unlocks agility, and creates the architectural runway for scalable digital transformation.

If you’re still operating on a patchwork of fragile integrations, you’re spending more time maintaining than innovating. And in a field where speed and time to market matters—whether you’re discovering new therapies or scaling production—that’s a risk you can’t afford. True digital transformation in life sciences means building an architecture that’s flexible, sustainable, and ready for what’s next. Because the systems will change. Science will evolve. The questions (e.g. Root Cause Analysis) that need answering will get more complex. And when they do, you’ll want an IT foundation that’s ready—and not reactive. Orchestration is that silver bullet.

To learn more about L7|ESP, please contact us or explore our website at L7Informatics.com.

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(1) Jeffrey Lewis, Joachim Bleys, and Ralf Raschke with Moritz Wolf, “Boosting biopharma R&D performance with a next-generation technology stack” (January 9, 2025)