Bridging the AI Chasm in Life Sciences: Why Orchestration Platforms Are the Key to Intelligence

by Vasu Rangadass, Ph.D. | posted on February 19, 2025

The life sciences industry stands at the edge of an AI revolution. We hear about Generative AI (Gen AI) and Agentic AI every turn we make. But despite the hype, the hard truth is that most organizations are not ready for AI—at least not in a way that delivers real, tangible value.

I’ve seen firsthand how companies that invest heavily in AI, but struggle with the same fundamental problem: data fragmentation. Scientific and operational data remain trapped in disconnected systems—ELNs, LIMS, MES, scheduling tools, bioreactors, testing instruments—the list goes on. Without addressing this foundational issue, AI initiatives will remain expensive, ineffective, and riddled with unreliable outputs.

So, the real question isn’t just how to use AI. It’s how to get AI-ready data in the first place. And that’s where orchestration comes in.

Knowledge vs. Intelligence: The Missing Link

A fundamental distinction often gets overlooked in AI conversations: knowledge vs. intelligence.

Intelligence requires structured, contextualized knowledge—but many life sciences organizations don’t have that. Instead, they often have scattered and siloed databases, spreadsheets, and paper-based records. When data is isolated like this, there’s no efficient way to extract meaning, let alone train AI models without the risk of hallucinations (a problem many organizations are starting to experience firsthand).

For AI to be truly intelligent, we must first create a structured representation of enterprise knowledge. The best way to do that is through semantic networks—specifically, Knowledge Graphs that dynamically organize relationships between key data points (samples, experiments, manufacturing processes, test results, etc.). 

This structured approach eliminates ambiguity and ensures AI models operate with clean, contextualized data—not just raw, disconnected inputs.

The Orchestration Approach: Automating Knowledge Creation

Many organizations have tried to solve this challenge by spending more money on data lakes, warehouses, or integration projects. But those approaches are static, expensive, and difficult to maintain. Every time a system changes, the entire data pipeline risks breaking down, forcing teams into an endless cycle of fixes and patches.

There’s a smarter way.

Instead of attempting to force fragmented systems into alignment manually, we can use orchestration platforms to dynamically structure and contextualize data as workflows are executed. This changes the game.

With workflow orchestration, every process—whether in research, manufacturing, or diagnostics—is automated in real time. As these processes run, they generate a Knowledge Graph on the fly. This means that, rather than scrambling to harmonize siloed data after the fact, we capture and structure knowledge at the moment of execution.

Real-World Applications: How Orchestration is Changing the Game

At L7, we’re already seeing orchestration transform operations in real-world settings. Here are a few examples: 

• At a biomedical research institution and NCI-designated Cancer Center, we’ve automated proteomics workflows, seamlessly integrating instruments, data pipelines, and analytical processes.
• At a multinational molecular diagnostics solutions company, orchestration now manages highly complex companion diagnostics workflows, ensuring precision across sequencing, bioinformatics, and regulatory requirements.

What do all these examples have in common? They eliminate data fragmentation at the source—so AI isn’t operating in a vacuum.

Agentic AI: The Next Evolution

Most of today’s AI models, including Gen AI, generate insights based on pre-trained data. But Agentic AI is where things get interesting.

Agentic AI is dynamic, continuously learning, and capable of autonomous decision-making. In a manufacturing setting, for example, an Agentic AI system could detect deviations in a bioreactor’s pH levels and automatically suggest (or even execute) corrective actions—without human intervention.

But here’s the catch: Agentic AI can only function with real-time, structured, and reliable data. That’s exactly why orchestration platforms are so critical—they ensure AI models aren’t guessing based on incomplete or outdated information.

The Future of AI in Life Sciences: A Call to Action

AI will not deliver on its promise in life sciences until organizations fix their data problem. It’s that simple.

Rather than continuing to invest in costly, unreliable data pipelines, the future belongs to companies that embrace orchestration platforms as the foundation for AI. These platforms do more than connect systems—they unify, automate, and contextualize data in a way that makes AI genuinely actionable.

The companies that get this right today will have a massive advantage tomorrow. The ones that don’t? They’ll find themselves stuck in an endless cycle of expensive AI experiments with little to show. The choice is clear: Without orchestration, AI is just a costly experiment. With orchestration, AI becomes a strategic advantage.

To learn more, contact us at info@l7informatics.com.