Life Sciences organizations have made AI a strategic priority. Drug discovery acceleration, clinical trial optimization, pharmacovigilance automation, manufacturing quality, these are not hypothetical use cases. Companies may be investing and moving AI into their day-to-day work, but the results of this work is moving at a different speed. In reality, 71% of life sciences leaders surveyed said AI deployment was at least somewhat better that it was in the previous six months, but only 13% said they saw measurable improvements at scale.1
That’ s a sizable gap between deploying AI and getting value from it.
The problem may have less to do with the AI than with the foundation underneath it. That’s because, for so many life sciences organizations, the data that their AI tools depend on is fragmented. It’s the way their IT environment is, by definition, designed. Research instruments, legacy systems, clouds, and specialized environments may have been fine before the advent of AI. But as the models keep getting better, the foundation is often not able to keep pace.
Drilling down a bit further, research instruments are generating data in proprietary formats. laboratory information management systems (LIMS) may not be communicating well with electronic lab notebooks (ELNs), clinical data often spans multiple clouds and sponsor systems, and manufacturing data may be sitting in completely separate and highly controlled environments.
Modernizing individual pieces of the R&D infrastructure one at a time may improve things here and there but it doesn’t make the problem disappear. A 2025 Lab of the Future survey from the Pistoia Alliance found that 80% of respondents use cloud data platforms and 81% use ELNs. Then why did 57% still say data silos were the top challenge to being able to make better use of lab data.2
It’s no wonder that when AI tries to work across that landscape, it uncovers incomplete information, inconsistent formats, and data that was never designed to be queried together at scale. An AI model can only work with what it can reach.
What is less obvious is what that fragmentation costs in pipeline terms. If a program moves from Phase 2 to Phase 3, processes that worked with less data, fewer sites, and smaller teams can break down at scale. Instead of speeding up analysis with AI, a team may have to spend more time finding, connecting, and validating data across a much larger environment. So not time is saved in that scenario. Instead the staff end up tracking down data, moving it between systems, and confirming it’s actually usable.
The organization faces two scenarios at that point: slow down to fix the foundation or move ahead with data that isn’t ready. Neither will get their program to its next milestone any faster.
This is why the sequence of AI investment matters, maybe more than any other parameter. Because choosing your AI platform before you evaluate your data and infrastructure readiness is like choosing the engine before knowing whether you have a road to run it on.
The consequences of doing that aren’t limited to a stalled pilot.
When approved environments can’t give researchers the capabilities they need, work often finds another route. In that case, data may get copied, analysis may move outside validated environments, and employees may resort to unauthorized AI tools. NexusTek estimates that 25% to 40% of employees prompts entered into unauthorized AI tools contain sensitive information.3 In life sciences, that could mean research data, clinical information, intellectual property, or regulatory content moving through systems without the organization’s expected controls.
The problem is that everything may appear to be working until someone needs to prove how it’s working. Suddenly validation, regulatory reviews, or audits can’t reliably show where information came from, who accessed it, and what happened to it along the way.
The fact of the matter is that there’s no single technology that can fix fragmentation on its own. But giving AI a reliable path to the data it needs without giving up the controls life sciences requires is a great starting point.
1. Map the data environment. Identify the data the use case needs, where it lives, and where access or integration breaks down.
2. Create governed access across environments. Give AI secure access to data across instruments, on-premises systems, private and public clouds, and partner environments without moving everything into one place.
3. Establish data quality and lineage. Know where data came from, how it changed, and whether it’s a fit for its intended use. If you can’t trust the input, you can’t trust the output.
4. Build compliance into the architecture. Design validation, audit trails, access controls, and applicable GxP requirements into the environment form the beginning. Retrofitting those controls later is harder and more expensive.
5. Build for scale and observability. Make sure the infrastructure can scale from pilot to production, with logging, monitoring, and controls needed to understand what the AI is doing .
The life sciences organizations getting the most from AI may don’t necessarily have the latest, most sophisticated model. They just got the order right: building the launchpad before attempting the launch. Prioritizing the data foundation along with the AI investments, work with IT partners that understand GxP validation, and treat data governance as both a compliance obligation and a business advantage.
If your AI initiatives are struggling to move beyond pilot, the next steps may not be another AI investment. Start underneath the AI. NexusTek can help identify where the data and infrastructure fragmentation are creating barriers and build the foundation needed to move AI from pilot to production.
Learn more. https://www.nexustek.com/nexustek-life-sciences
Sources:
1. Deloitte, Confidence under pressure: How life sciences leaders are recalibrating for the rest of 2026, June 2026
2. Pistoia Alliance, Pistoia Alliance survey finds more than three quarters of life sciences labs expect to use AI within two years, but lack of skills is a growing barrier, September 2025
3. NexusTek, Ep. 2: Winning with Secure AI—Unlocking AI’s Potential Without the Data Risks, July 2025