Key takeaways

• Data readiness is the biggest divider between leaders and laggards
• Unified data environments create faster AI adoption
• Standards and interoperability matter as much as infrastructure
• AI success depends on groundwork done long before AI arrived

In life sciences and healthcare, AI rarely fails because of a lack of ambition. More often, it stalls because the data underneath it is fragmented, inconsistent or hard to trust.

That is why, when organizations ask why some AI programs scale and others do not, the answer is often simple: look at the data.

Across the industry, the gap between leaders and laggards is increasingly a data gap. The organizations moving faster with AI are usually the ones that have already done the harder, less visible work of cleaning, connecting and governing their data. In many cases, they did not build those foundations for AI specifically. They built them because good data has always mattered for analytics, operational performance, compliance and decision-making. Now, as AI moves from experimentation to enterprise execution, they are seeing the payoff.

The market is telling the same story. Deloitte’s 2026 Life Sciences Outlook found that nearly half of leaders expect accelerated digital transformation to materially shape strategy this year, yet only 22% say they have successfully scaled AI and only 9% report significant returns. Gartner has warned that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data. And recent healthcare research points in the same direction: interoperability is rising rapidly as a boardroom priority because leaders increasingly recognize that data fragmentation is what prevents AI from moving beyond isolated pilots.

That is why data readiness is no longer a background issue. It is becoming the real differentiator.

Unified data is where AI starts to work

AI becomes useful at enterprise scale only when it can operate on connected, reliable and accessible data.

The organizations ahead of the curve are the ones that have started breaking down silos across clinical trial data, real-world evidence, supply chain data, lab data and other enterprise sources. Once those environments are connected, AI can do something much more valuable than generate isolated outputs. It can produce stronger inference, support better decisions and help drive more consistent action across the organization.

By contrast, fragmented environments make AI harder to trust and harder to scale. Teams can still launch local pilots. They can still prove narrow use cases. But they struggle to turn those wins into enterprise capability.

In life sciences and healthcare, that challenge is even more acute because the data is not only large and complex; it is also highly regulated, highly sensitive and often dispersed across legacy systems, labs, devices and ecosystem partners. That is why interoperability is not a technical detail. It is a strategic requirement for scale.

Data readiness is not theoretical. It is operational

This becomes much clearer when you look at large-scale transformation programs in practice.

One of the strongest examples is HCLTech’s work with a global pharmaceutical leader to develop a precision drug delivery solution and a next-generation oncology research platform. The objective was not simply to modernize lab infrastructure. It was to create a comprehensive research ecosystem that could connect 34 pharmaceutical and diagnostic labs worldwide, integrate 140-plus applications, connect LIMS and ELN environments and support global collaboration in cancer research.

The challenge was substantial. Workflows were fragmented across 34 labs and more than 600 oncology experiments. Data could not be collected or shared efficiently across regions. Lab environments were inconsistently digitized. That fragmentation slowed the development of targeted therapies for cancers such as leukemia, lymphoma and sarcoma.

The solution required more than infrastructure. It involved instrument categorization, custom connector development for legacy environments, workflow mapping, pilot validation and intelligent scheduling and error-prevention capabilities so experiments would not start if equipment malfunctioned or reagents were missing. The result was a far more connected, data-driven ecosystem that delivered roughly 20% productivity gain in the first year, enabled global data collection and collaboration and created a stronger foundation for precision medicine.

That is what data readiness looks like in practice. It is not a back-office clean-up exercise. It is the condition that makes scientific acceleration possible.

Interoperability creates compounding value

Another reason this matters is that data readiness does not only support AI. It improves the enterprise more broadly.

In a case study with one of the world’s leading diagnostic services providers, HCLTech helped modernize patient and clinician engagement, clinical trials, analytics, contact center and IoT-related platforms on cloud-native architectures. The work included application development, integration and interoperability, business analysis and the creation of a testing center of excellence. The broader outcome was not just modernization. It was better patient matching, stronger retention through improved outreach and improved regulatory and compliance adherence.

That is the larger point many organizations are now recognizing. Clean, connected and interoperable data does not just help models run. It strengthens patient engagement, care coordination, research productivity and compliance performance. AI then amplifies the value of those stronger foundations.

This is also consistent with broader market signals. Recent healthcare research found that leaders are prioritizing interoperability not simply for data-sharing efficiency, but because they see it as essential to scaling AI into mission-critical workflows and delivering measurable operational value.

Standards create acceleration

One of the most underappreciated accelerators for AI is standards adoption.

When organizations adopt common structures and stronger interoperability across clinical, operational and research environments, they make data easier to share, interpret and use. That matters within the enterprise, but it also matters across the ecosystem, where collaboration between sponsors, providers, manufacturers and regulators increasingly shapes outcomes.

This is not glamorous work, but it is strategic work. It is also where many organizations still have more to do. Deloitte’s broader 2026 life sciences and health care insights report found that 48% of respondents said their boards lacked representation in areas such as AI and data science. That is not just a leadership issue. It is a signal that many organizations are still building the governance and strategic muscle needed to turn data into a true enterprise asset. 

Data as the differentiator

AI will not eliminate the need for data discipline. It will intensify it.

That is why I believe the next wave of competitive advantage in life sciences and healthcare will come from organizations that treat data as a strategic asset, not a technical afterthought. The more unified, standardized and governed the data environment becomes, the faster AI can move from experimentation to enterprise value.

And when that happens, the impact goes beyond internal productivity. It improves collaboration across research networks, sharpens operational decisions, strengthens compliance and helps accelerate the delivery of more personalized and more effective care. In the years ahead, the organizations that lead on AI will very often be the organizations that first led on data.

FAQs

Why is data readiness so important for AI?
AI is only as strong as the data behind it. Poor quality, fragmented or poorly governed data limits model performance, trust and scalability.

What separates organizations that are getting ahead?
The leaders are usually the ones that have already unified critical data sources, improved interoperability and built the governance needed to support enterprise-wide use.