Scaling AI in life sciences starts with data and governance readiness

Across every industry, a familiar gap is widening: AI ambition is outpacing enterprise readiness, and the measurable returns from digital transformation are beginning to plateau. The life sciences industry is not immune to these challenges. The pressure to compress timelines, unlock capital and modernize is immense. Yet AI value too often stays trapped in pilots that never scale.

The instinct may be to reach for a more advanced model. But the model is often not the primary constraint. The greater challenge lies in the foundation beneath it. The most revealing number in this year's CGI Voice of Our Clients research illustrates the issue. Among health and life sciences leaders, 63.5% say digitization is significantly reshaping their operating models, which is almost exactly the cross-industry rate. But only 13.2% rate their operating model as highly agile in addressing digitization and integrating new technology. That figure is roughly half the cross-industry rate of 25.5% and is lower than the rates reported in sectors such as banking (40.9%) and manufacturing (18.9%). Life sciences organizations are highly exposed to digital change and have less flexibility than many other sectors to absorb it.

That combination of high impact and low agility may help explain why many AI initiatives struggle to progress beyond pilots. Closing the gap depends on two foundations that help determine whether AI can scale: governed, AI-ready data and governance that enables responsible adoption.

The data foundation problem

AI and, increasingly, agents can only reason over data they can read, classify and trust. In most research organizations, the overwhelming majority of R&D content is unstructured, buried in documents, protocols and correspondence that no model can use without significant preparation. The pilot that performs well on a clean, curated dataset quietly collapses when it meets the reality of everyday R&D content.

This is why "AI-ready" means something more demanding than "digitized." Moving paper into a PDF is digitization. Making that content governed, contextual and classified is structuring. A system must be able to understand what it is, how sensitive it is and how it relates to other information. A common barrier to AI initiatives is the absence of that connective metadata. Protocol digitization is one example. Turning a clinical protocol into structured, machine-usable content pays off downstream in faster study setup and cleaner exchange between authors and reviewers, precisely because the foundation was built to be read by machines, not just people.

The temptation is to treat all of this as an IT cleanup exercise. It isn't. Organizations that structure their content early are better positioned to scale their AI investments and build on them over time. In an environment where 49.2% of life sciences leaders rate legacy systems as a significant challenge to the successful implementation of their digitization, data and AI strategies, AI-ready data is not simply a housekeeping task. It is a strategic capability.

Governance as enabler, not brake

The second foundation is the one most likely to be misunderstood. Governance is often framed as the thing that slows AI down. In practice, strong governance can help organizations move faster and more safely. It provides a blueprint for responsible adoption rather than acting only as a barrier.

Consider what happens without it. When people adopt unsanctioned tools, "shadow IT" can quietly become "shadow AI." The initial reflex may be to police it. A more constructive response is to recognize it as a signal that approved systems may not be meeting a real business need. Organizations can close that gap by providing capability, not only by restricting it.

The stakes rise again with agentic systems. Agents are non-deterministic by design; they may not complete the same task the same way twice, and "always-on" agents can introduce security and intellectual property exposure without strict guardrails. Organizations need to be able to validate their work. That raises the governance bar further: validation, clearly defined failure points and human accountability become prerequisites, not afterthoughts. These considerations are especially important in life sciences. 

Leaders in the sector weigh cyber and compliance requirements more heavily than those in many other industries. Security and cyber risk mitigation are a high-priority cloud criterion for 74.6% of them compared with 64.5% across industries. Compliance, audit alignment and data sovereignty are also important considerations. In a regulated industry, the maturity of your governance increasingly determines who you can partner with and what you can actually deploy. Governance is not simply a constraint on AI. It is a foundation for using it responsibly.

Two sides of the same coin

Data readiness and governance readiness are not separate workstreams. Both exist to help make AI trustworthy and scalable rather than merely impressive in a demo. The readiness gap they address is very real. While 54.1% of life sciences organizations report having a holistic data strategy for the entire internal enterprise and 42.1% report having a holistic enterprise-wide AI strategy, advanced implementation remains early. Generative AI is in production or continuous improvement for just 9.5% of organizations, and agentic AI for 1.6%. 

The findings suggest that strategies and pilots are often advancing faster than the organizational, data and governance foundations needed to scale them.

The bottleneck, in other words, has moved. The question is no longer "can we build it?" It's "can we trust it and run it everywhere?" That is a readiness question, not a technology one.

How leaders can strengthen AI readiness

The organizations closing the gap tend to share a few habits:

• Start with the business problem, not the tool. The winning question is "What are we trying to solve?" rather than "Where can we apply AI?"
• Treat data structuring as strategic infrastructure. Governed, contextual and classified data enable downstream AI applications.
• Design governance to enable speed. Build guardrails that let teams move quickly within safe boundaries rather than gates that stall pilots at the seams.
• Close capability gaps so shadow AI never starts. Meet the underlying need before workarounds take root.
• Require validation and human accountability for agentic systems. Organizations need to verify the work before they can scale it confidently.

The readiness dividend

This year's CGI Voice of Our Clients research points to a conclusion that holds across industries: organizational and foundational readiness appears to be at least as important as technological ambition in realizing AI value at scale. Life sciences offers a clear illustration of the principle, given its regulatory stakes, its data complexity and an agility gap wider than almost any other sector's.

The advantage will not necessarily go to the organizations with the most ambitious AI roadmaps. It will go to those who build the foundations needed to put those roadmaps into practice.

Ready to learn more? Explore our life sciences expertise and solutions for building a stronger foundation for AI at scale.