The Rise of AI Corporate Citizens: Why Life Sciences Organizations Need Data Orchestration, Not Just More Software

by Vasu Rangadass, Ph.D. | posted on September 03, 2025

McKinsey’s recent research reveals something profound: we’re witnessing the emergence of “AI corporate citizens,” autonomous agents that don’t just execute predetermined workflows but operate, reason, and adapt across entire business processes. These aren’t your typical automation tools. They’re digital colleagues capable of making complex decisions, learning from outcomes, and collaborating with other AI agents to continuously alert and optimize business performance.

At the same time, Gartner highlights a fundamental shift from Software-as-a-Service (SaaS) to Service-as-Software (SaS), where AI-native companies are scaling 40% faster than traditional providers by delivering outcomes rather than tools. The message is clear: the future belongs to organizations that can deploy AI as productive corporate citizens, not just experimental add-ons.

But here’s what both reports don’t fully address: the foundational infrastructure challenge that makes or breaks AI success in science-driven organizations.

The Problem: AI Can’t Reason Effectively Without Context

I’ve spent decades working with life sciences and other Fortune 500 organizations, and I see the same pattern everywhere. Teams invest heavily in AI initiatives, predictive analytics for drug discovery, machine learning for process optimization, and Generative AI for regulatory submissions, yet struggle to achieve meaningful business ROI. The technology works in demos and proof of concepts, but falters in production.

The culprit isn’t the AI models themselves. It’s data fragmentation.

Consider what the world calls “agentic AI systems,” autonomous agents that can assess creditworthiness, adjust pricing, and proactively flag anomalies in financial services. Now imagine deploying similar agents in the life sciences product value chain. One agent monitors bioreactor conditions, another tracks raw material quality, and a third manages batch release testing. For these agents to function as true corporate citizens working in synchronization, they need real-time access to contextualized data across every system, every instrument, and every processing and testing stage.

But in most life sciences organizations, this data is trapped in system silos. LIMS systems don’t talk to manufacturing execution systems. MES and LIMS systems exist separately from quality management platforms. Instrument data requires manual extraction, entry, and reconciliation. The result? AI agents that can’t reason effectively because they lack the comprehensive, contextualized information they need to make intelligent decisions.

The Service-as-Software Paradigm: Outcomes Over Interfaces

The shift to Service-as-Software amplifies this challenge. In the SaS model, customers pay for outcomes, not tools. Instead of licensing software that teams must learn to operate, organizations receive intelligent services that handle complete workflows autonomously.

For life sciences, this represents a massive opportunity. Imagine ordering “clinical trial enrollment optimization” as a service rather than managing separate platforms for patient screening, protocol management, and site coordination. Or purchasing “batch release automation” instead of integrating multiple quality control systems. Or digital tech-transfer between R&D and external CDMOs.

But SaS providers can only deliver on these promises if they have access to unified data models, standardized ontologies, and contextualized data. A service that optimizes clinical trials needs seamless integration with electronic data capture systems, patient registries, and investigator management platforms. A batch release service requires real-time connections to manufacturing data, analytical results, and quality specifications. A digital tech-transfer service needs standardization of recipe meta-models, data models, and ontologies.

This is why AI-native companies are scaling faster. They’re not just building better AI; they’re building on better data foundations.

Data Orchestration: The Infrastructure for AI Corporate Citizens

The organizations that will succeed in this transformation understand a fundamental truth: AI corporate citizens require orchestration on top of standardized data models, NOT consolidating data from multiple systems.

Data orchestration goes beyond traditional integration approaches. Instead of connecting disparate systems after the fact, orchestration platforms capture and contextualize information at the point of generation. Every experimental result is immediately linked to its protocols, reagents (and vendors), and operators. Every manufacturing batch is automatically connected to its raw materials, critical material attributes, critical process parameters, critical quality attributes, and quality outcomes.

This contextualized data foundation enables AI agents to function as true corporate citizens. They can reason across time horizons because they understand the complete history of every sample, every experiment, and every process. They can collaborate effectively because they share a common understanding of data semantics and business rules. They can learn continuously because they have access to the full context needed to correlate actions with outcomes.

L7|ESP®: Building the Foundation for AI-Native Life Sciences

This is precisely why we built L7|ESP as a data orchestration platform, not just another application. L7|ESP contextualizes data at the source of creation, whether from laboratory instruments, manufacturing systems, or scientific workflows. It harmonizes information WITHIN and ACROSS research, development, CMC, and manufacturing, while maintaining the audit trails and governance frameworks that life sciences organizations require.

But orchestration alone isn’t enough. To enable AI corporate citizens, organizations need platforms that can automatically generate the knowledge graphs and structured data models that AI agents require. L7|ESP’s automatic data contextualization creates the AI-ready foundation that allows organizations to deploy autonomous agents without extensive data preparation projects.

We’ve seen the results. Research institutes are cutting document preparation time by 80%. Manufacturing teams are improving operational efficiency by 50%. Organizations are moving from AI experimentation to AI production because their data infrastructure can support intelligent automation at scale.

The Choice: Lead or Follow

McKinsey’s research makes one thing clear: agentic AI isn’t just coming; it’s already here. Organizations that can successfully deploy AI corporate citizens will gain exponential advantages in speed, scale, and precision. Those who can’t will find themselves competing against autonomous systems that never sleep, never make transcription errors, and continuously learn from every interaction.

The life sciences industry stands at an inflection point. The technology exists to deploy AI agents that can accelerate drug discovery, optimize manufacturing processes, and streamline regulatory submissions. But success requires more than deploying the latest AI models. It requires building the data orchestration infrastructure that allows AI agents to function as productive corporate citizens.

The organizations that understand this distinction (and invest accordingly) won’t just survive the AI transformation. They’ll lead it.

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Sources:

McKinsey collaborative article: When can AI make good decisions? The rise of AI corporate citizens by Federico Berruti, Lari Hämäläinen, Oana Cheta, and Venky Anant, with Damian Lewandowski – June 4, 2025 – https://www.mckinsey.com/capabilities/operations/our-insights/when-can-ai-make-good-decisions-the-rise-of-ai-corporate-citizens 

Gartner webinar: How AI-Native Providers Will Redefine the Market https://www.gartner.com/en/webinar/739329/1668831-service-as-software-stay-ahead-of-the-shift-accelerate-your-growth-with-ai-driven-strategies