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Every Technology Wave Needs Its Infrastructure: Why AI in Life Sciences is No Different
by Vasu Rangadass, Ph.D. | posted on November 11, 2025
TL;DR
AI is advancing faster than most organizations can support, and life sciences is no exception. Reports from Gartner, Deloitte, and McKinsey all point to the same issue: companies are investing in AI tools without building the infrastructure required to make them work. Every major tech wave has shown that innovation only scales when the right foundation is in place. In AI, that foundation is data contextualization, unified workflows, and a digital backbone. In life sciences, L7|ESP® provides that infrastructure.
Artificial intelligence has captured the imagination of every industry, and the life sciences industry is no exception. From predictive modeling in R&D to automated quality control in manufacturing, AI promises to transform how therapies are discovered, developed, and delivered. Yet beneath the optimism lies a hard truth: AI cannot deliver lasting value without the right infrastructure.
The numbers tell the story. According to Gartner, 60% of AI projects will be abandoned through 2026 if unsupported by AI-ready data, while McKinsey reports that 88% of organizations use AI, but only 1 percent of company executives describe their gen AI roll-outs as mature. The problem isn’t the sophistication of AI algorithms: it’s the absence of infrastructure to support them.
The Pattern Repeats: Infrastructure Enables Innovation
Every major technology wave is built on an invisible layer of infrastructure that makes innovation possible. Cloud applications could not scale until platforms like AWS and Azure provided reliable computing and storage foundations. The explosion of mobile apps only became possible once iOS and Android standardized APIs, data models, and app distribution. The applications were revolutionary, but they succeeded because the infrastructure came first.
AI is following the same trajectory. The current rush to deploy AI tools in life sciences mirrors the early enthusiasm around cloud and mobile applications. There are now hundreds of AI vendors, models, and interfaces promising to accelerate discovery or optimize processes. Yet many of these initiatives are being built on disconnected data, fragmented workflows, and rigid legacy systems. Without an integrated foundation that connects and contextualizes data, these AI tools will struggle to scale or deliver reliable insight.
Life Sciences Complexity Demands More
Unlike consumer applications, AI in pharmaceutical and biotech organizations operates in a world defined by complexity and regulation. Data is distributed across research, development, manufacturing, and quality functions, each governed by its own systems and compliance requirements. Experimental results must be traceable. Process deviations must be recorded. Every data point must carry context and lineage.
For AI to be trusted in this environment, it needs more than access to data: it needs infrastructure that gives that data structure, meaning, and governance. Gartner research reveals that 63 percent of organizations either lack or are unsure about having the proper data management practices for AI. Deloitte echoes the same structural challenges, reporting in its AI Trends 2025 that AI is absorbing a growing share of digital budgets while foundational capabilities struggle for investment, and that integration with legacy systems and governance requirements remain top barriers to scale. The gap is clear: organizations are deploying AI tools without first building the data infrastructure those tools require to succeed.
What AI-Ready Infrastructure Actually Requires
This is where digital unification becomes essential. Instead of layering AI on top of disconnected systems, organizations need an orchestration layer that brings together people, processes, and data. The goal is not to replace existing tools, but to connect them through a common data foundation.
At L7 Informatics, this is precisely the role of L7|ESP®: a digital unified platform that provides the infrastructure required to make AI operational in life sciences. L7|ESP unifies applications such as LIMS, ELN, MES, and scheduling under a single architecture that harmonizes and contextualizes data at the point of generation. It enables FAIR data principles (making data Findable, Accessible, Interoperable, and Reusable) while orchestrating workflows across departments and sites. As a result, organizations can integrate AI and machine learning models seamlessly into daily operations, using high-quality, contextualized data that AI can interpret and learn from.
We have seen this approach in action. At Dana-Farber Cancer Institute (DFCI) and The Jackson Laboratory (JAX), harmonized data models are enabling advanced analytics that would have been impossible in isolated systems. At QIAGEN, orchestrated workflows have improved reproducibility and reduced manual effort, creating the kind of standardized environment where AI can add measurable value. At Cellipont Bioservices, contextualized data supports continuous process improvement across production runs.
These leading organizations demonstrate what happens when AI is implemented on solid digital infrastructure rather than fragmented legacy systems.
Architecture as Competitive Advantage
This principle echoes what I discussed in an earlier article, Architecture is the Moat. True competitive advantage does not come from a single application or model, but from the architecture that allows innovation to scale. In the AI era, infrastructure is that architecture. Without it, even the most sophisticated algorithms are limited to isolated proofs of concept. With it, organizations can operationalize intelligence across the entire value chain, from discovery through manufacturing and quality.
What makes L7|ESP distinctive is that it is not a rip-and-replace solution. It can integrate seamlessly with an organization’s existing applications while offering its own suite of interoperable apps, including L7 LIMS, L7 Notebooks, L7 MES, and L7 Scheduling. This flexibility allows organizations to modernize at their own pace, building a unified digital backbone that supports today’s processes and tomorrow’s AI initiatives.
Assessing Your AI Readiness
Before investing in another AI tool, organizations should ask themselves four questions that will reveal their infrastructure maturity:
- Can we access our data programmatically across all R&D and manufacturing systems? If experimental data, manufacturing metrics, and quality results remain trapped in disconnected systems, AI cannot learn from the complete picture.
- Is our data connected to the context that produced it? Raw results, without experimental conditions, instrument parameters, or process context, are essentially meaningless to AI models.
- Can we trace materials and data from supplier through final product? AI in regulated industries requires complete data lineage, not just for compliance, but also to enable models to understand cause and effect.
- Do we have governance structures that maintain data integrity while enabling AI access? FDA and EMA regulations do not disappear when AI enters the picture. Infrastructure must balance openness with control.
If the answer to any of these is “no” or “partially,” the organization faces an infrastructure gap. And that gap will limit every AI initiative that follows.
Infrastructure First
The lesson from every past technology wave is clear: applications innovate, but infrastructure transforms. Just as cloud infrastructure unlocked the SaaS revolution and mobile infrastructure enabled an entire ecosystem of connected devices, digital infrastructure will determine the success of AI in life sciences.
The future of AI will not belong to those who deploy the most tools, but to those who build the strongest digital foundations. In life sciences, that foundation must orchestrate data, unify workflows, and preserve scientific context. Infrastructure is not the end of the AI journey; it is where it begins.
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Sources:
Gartner | Lack of AI-Ready Data Puts AI Projects at Risk | February 26, 2025
McKinsey | The state of AI in 2025: Agents, innovation, and transformation | November 5, 2025
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
McKinsey | The state of AI – How organizations are rewiring to capture value | March 2025
Deloitte | AI trends 2025: Adoption barriers and updated predictions | September 15, 2025
https://www.deloitte.com/us/en/services/consulting/blogs/ai-adoption-challenges-ai-trends.html