> From Data to Decisions: Enabling AI in the Digital Laboratory
Introduction
In collaboration with CSols Inc., ZONTAL explored a critical question facing life sciences organizations today:
Why do so many AI and machine learning initiatives struggle to deliver value?
The answer is not a lack of ambition—or even technology. It is data.
Across the industry, organizations are investing heavily in AI, yet most efforts stall before meaningful outcomes are achieved. The reason is simple: AI does not run on vision. It runs on structured, harmonized, and accessible data.
The Hidden Cost of AI Initiatives
There is a common misconception that AI projects are primarily about models and algorithms.
In reality, the majority of effort happens long before any model is trained.
Based on industry experience, roughly half of the work in AI initiatives is spent preparing data—harmonizing it, structuring it, and aligning it to FAIR principles (Findable, Accessible, Interoperable, Reusable). Another portion is spent on the software and infrastructure required to manage that data, while the remainder falls on internal teams navigating processes, governance, and execution.
What this reveals is a fundamental truth: AI success is not a modeling problem—it is a data readiness problem.
And today, most organizations are not ready.
The Strategy Gap
Despite growing investment in digital transformation, many companies still lack a clear data strategy.
Even fewer have a defined execution plan.
This gap creates a disconnect between ambition and reality. Organizations want to become AI-driven, but without a foundation for managing and harmonizing data, those initiatives remain isolated experiments rather than scalable capabilities.
Closing this gap requires shifting focus—from tools to strategy, and from strategy to execution.
Understanding Analytics Maturity
To move forward, it helps to understand how organizations evolve in their use of data.
Most follow a progression across three stages of analytics maturity.
The first is descriptive. At this stage, organizations can access their data, generate reports, and analyze past performance. Dashboards and KPIs provide visibility, but the perspective is backward-looking—focused on what has already happened.
The second stage is predictive. Here, historical data is used to build statistical models that forecast future outcomes. Organizations begin to anticipate events, optimize operations, and make more informed decisions based on patterns in their data.
The final stage is prescriptive. This is where AI operates at scale—leveraging models, automation, and large language systems to recommend or even execute actions. At this level, systems are no longer just informing decisions; they are helping drive them.
But reaching this stage requires more than advanced tools. It requires a foundation of high-quality, connected data.
Why Data Harmonization Comes First
A key takeaway from the session is that automation and AI cannot exist without harmonized data.
Before workflows can be automated, data must be standardized. Before models can make recommendations, they must be trained on consistent, reliable inputs.
In laboratory environments, this is particularly challenging. Data is generated across instruments, systems, and workflows, often in incompatible formats. Without alignment, even basic analysis becomes difficult—let alone advanced AI use cases.
Harmonization is not an optional step. It is the starting point.
From Data-Centric to Decision-Centric Labs
As organizations mature, there is a shift in how data is used.
Early stages focus on collecting and organizing data—creating a data-centric view of operations. But over time, the goal evolves toward a business-centric or decision-centric approach.
Scientists and teams are not just interested in data itself. They want to know what to do next.
This is where AI begins to deliver value. By connecting past experiments, workflows, and results, systems can recommend next steps, suggest experiments, and guide decision-making.
However, this only works if the system understands what has already been done.
Without historical context and structured data, automation cannot move forward.
The Evolution Toward Smart Laboratories
The next step in this journey is the transition to truly intelligent labs.
In traditional environments, scientists act as intermediaries—manually configuring instruments, interpreting results, and managing workflows across systems.
In a more advanced, connected lab, this dynamic changes.
Scientists define the objective, and the system handles execution. Methods are pulled from centralized repositories, workflows are automated, and instruments operate based on integrated data and models.
A simple analogy can be found in everyday life. In a traditional setup, adjusting room temperature requires manually changing a thermostat. In a smart system, the environment adjusts automatically based on preferences, patterns, and context.
The same shift is happening in laboratories—from manual control to intelligent orchestration.
Closing Thought
As discussed in partnership with CSols Inc., becoming an AI-driven organization is not about adopting the latest technology.
It is about building the right foundation.
AI is the outcome—not the starting point.
And that foundation begins with data that is harmonized, FAIR, and ready to be used.
Because only then can organizations move from understanding the past…
to predicting the future…to actively shaping what comes next.
