AI in life sciences is no longer theory. It's here, quietly embedded in systems that deal with compliance, quality, and regulatory risk. Not yet as flashy chatbots or moonshots, but we are starting to see AI introduced as practical, controlled applications that solve long-standing bottlenecks in GxP-regulated ERP environments.

A couple of examples that we have seen in practice.

AI for Predicting Trial Protocol Success

This is probably one of the most widely used examples, as there are obvious gains for the Life sciences industry in this part of the value chain. Designing a trial that fails to recruit or meet endpoints is a very costly mistake (average total costs of trials that reach phase III, is 40 million dollars, source: Sofpromed dec 2024).

So already very early on, even with AI’s technological predecessors, solutions for these challenges were highly sought after. Platforms like Medidata (Dassault Systèmes), with its Trial Design Optimizer, or IQIVIA now use massive real world data sets and predictive models to flag risk factors in protocols before the trial begins.

AI algorithms review parameters like: trial complexity, inclusion/exclusion criteria, and therapeutic area, and provides a success likelihood score, along with suggested study design improvements.

The real value: fewer protocol amendments, better trial feasibility, higher chance of hitting endpoints, more accurate patient/site enrolment forecasts.

Trend Detection in QC Lab Data

Manual outlier detection still dominates in many labs. But some clients now use AI-assisted modules (e.g., StarLIMS with ML plugins, SAP QM extensions) to flag early trends, before specs are breached.The model alerts are versioned, auditable, and reviewed by analysts (so not replacing them, just accelerating what matters).

It looks perhaps like a small step for AI, but this type of functionality now being part of the standard LIMS application and therefore complying to stringent GxP requirements is a big step for ‘IT-mankind’.

The real value: Faster insight, lower human error, no loss of compliance integrity.

Complaint Triage with Natural Language Processing

Complaints come in unstructured, free-text form via various channels. Sorting by severity, region, or product is slow and risky.

We’ve seen implemented NLP-based classifiers (e.g. using Salesforce Einstein or Atlassian Intelligence) to pre-sort and tag complaints in real time. Critical ones are escalated faster. QA oversight remains.

The real value: Shorter response time, fewer misroutes, and full audit trail of every decision.

AI-Supported Batch Record Review

In modern Cloud MES systems like Tulip and SAP Digital Manufacturing (although, in my view, there are still several reasons not to adopt it as the MES for Life Sciences just yet) AI is now being used to flag anomalies across execution logs. This is typically done by leveraging data pulled into a platform (e.g. AWS, Azure, BTP), where trained anomaly detection models can analyze it in near real-time.

Instead of reading 100+ pages of records line-by-line, QA sees AI-suggested highlights, anomalies, sensor irregularities, step delays, across multiple batches.

The real value: Accelerates review cycles, helps spot recurring issues early, improves batch traceability.

What Makes These Use Cases Work?

The common thread of these examples is that these aren’t AI experiments. They’re targeted, auditable, and embedded in validated ERP or QMS landscapes. Small perhaps, but important.

Because to work in a GxP environment, AI must support:

• Traceability: Which model made the decision, on which data, under which conditions?
• Feedback: How to detect and prevent algorithm drift or hallucination.
• Version control: Just like software, models need lifecycle tracking.
• Human override: Final judgment stays with qualified personnel.
• Structured integration: Into SAP, LIMS, QMS, not in shadow IT.

These basic starting points aren’t nice-to-haves. They're what makes AI usable in a regulated business. Without it, no matter how promising, the use of AI is experimenting not implementing.