The Best Way to Achieve AI-Readiness for Life Sciences

by Robert Zeigler, Ph.D. | posted on October 22, 2025

Gartner Warns that AI is Ready, but the Data isn’t

TL;DR

The research firm’s latest insights reinforce the need for a unified digital backbone in life sciences.

Generative AI has captured the imagination of every industry, but according to Gartner, most enterprises are still not ready to unlock its full potential. In its recent webinar analyzing the top 10 reasons GenAI projects fail, Gartner identifies a striking truth among the top three: AI is ready, but the data isn’t. 

That statement should resonate deeply across the life sciences. While models are rapidly evolving, Gartner notes that most organizations still struggle with poor data quality, inconsistent classification, and fragmented integration. This leads to even the most advanced algorithms drawing conclusions from data that is fragmented, incomplete, or lacks proper context.

The stakes are high. Gartner’s research shows that 54% of GenAI initiatives now focus on revenue and capability growth, not just cost optimization. Organizations see AI as a strategic driver, making data readiness even more critical.

In life sciences, this gap has real consequences. It means delayed discoveries, prolonged tech transfers, and missed opportunities to apply AI where it could truly accelerate outcomes, from research to manufacturing to quality control.

For example, when a CDMO attempts to implement AI-driven resource optimization, they discover their data exists in fragments: sample tracking in LIMS, manufacturing execution in MES, and quality results in separate databases. The AI has plenty of data but no understanding of how these pieces relate; the context that transforms records into actionable intelligence.

Data Readiness is the True Competitive Moat

At L7 Informatics, we’ve long argued that architecture is the real competitive moat. Data, like AI models, is not inherently valuable. It becomes valuable only when it’s connected, contextualized, and orchestrated. Gartner’s guidance echoes this: to achieve AI-ready data, enterprises must align data, govern contextually, and qualify continuously.

In practice, this is where life sciences organizations often struggle the most. Data lives in silos across research labs, manufacturing sites, and supply chains. Scientific context (the “why” and “how” behind an experiment or process) is often lost as data moves between systems. And each new AI initiative begins as if from scratch, requiring months of manual data alignment and validation before yielding any insight.

AI readiness doesn’t start with the model; it starts with the data. And data readiness isn’t a one-time clean-up exercise; it’s a systematic capability built into the organization’s digital backbone. That’s why the conversation must shift from individual tools to unified architecture.

From Models to Meaning: Building the AI-Ready Digital Backbone

Gartner’s insight that “models are general-purpose technologies” highlights a fundamental mismatch: while AI models advance quickly, enterprise data landscapes have barely evolved. Without a unified digital backbone, even the best models are like engines without fuel.

What does a unified digital backbone actually mean in practice? It’s an architecture where research workflows connect seamlessly to manufacturing execution, where quality data maintains its relationship to process parameters, and where every data point carries the scientific context that gives it meaning. Instead of stitching together disconnected systems after the fact, this approach treats data orchestration and workflow automation as one continuous capability. It provides a single orchestration layer where data, workflows, and context coexist, ensuring that information captured in R&D remains connected to downstream manufacturing, quality, and analytics. The result isn’t just centralized storage, but an environment where data relationships are preserved from creation through analysis. This continuous contextualization transforms fragmented records into AI-ready data that can be trusted, governed, and reused.

When architecture, data, and workflows operate in concert, organizations can:

• Train AI on qualified, context-rich data rather than raw or inconsistent inputs.
• Integrate models seamlessly with enterprise data using modern techniques such as vector embeddings, fine-tuning, and automated chunking, the same best practices Gartner advises CIOs to adopt.
• Reduce the time between data creation and insight generation, turning data from a passive asset into an engine for innovation.

This is the future of AI in life sciences: not another layer of technology, but a foundation that makes AI operationally viable and scientifically reliable.

Closing Thought

As Gartner rightly points out, AI is ready, but the data isn’t. For CIOs and digital transformation leaders in life sciences, this isn’t a reason for hesitation; it’s a call to action. Building AI-ready data isn’t about adopting new tools; it’s about constructing a digital backbone that continuously aligns, governs, and qualifies data.

Those who do will discover that AI isn’t the innovation itself; it’s the amplifier. The true innovation lies in the architecture that enables it.