AI in the lab: How to use artificial intelligence correctly in the lab

Five areas where AI offers real added value

1. Technical documentation at the touch of a button

No more tedious searching through numerous documents. AI systems can:

• Record and understand manuals, SOPs, and records
• Answer technical questions in seconds
• Linking information from different sources
• Provide exact source references

Practical example: Instead of opening five PDFs, ask a simple question and receive the exact answer with a reference to the official document.

2. Intelligent device management

Every piece of laboratory equipment continuously generates data. AI analyzes these patterns and can:

• Detect unusual readings early on
• Identify abnormal device behavior
• Warn of critical trends
• Suggest preventive maintenance

The AI observes the historical behavior of your devices and can predict what might happen next.

3. Your personal technical assistant

An AI solution provides round-the-clock support:

• Knows all the lab documentation
• Recalls every past incident
• Knows the history of every device
• Guides you step by step through complex procedures

Important: AI does not replace your judgment, but provides the right information at the right time.

4. Automation of routine tasks

AI reliably handles recurring tasks that do not require creativity:

• document classification
• Data extraction from records
• Review of basic criteria
• Filling out standard forms

This will free up time for activities that are truly important.

5. Standardization of workflows

AI can help when team members have varying levels of experience, work shifts, or there is high staff turnover:

• Training new staff
• To remind oneself of critical points
• Enforce best practices
• Reducing errors caused by forgetfulness

How AI works in the laboratory

AI is neither magic nor an opaque black box. It combines three key capabilities:

Understands technical language: The AI learns the specific terminology used in your laboratory—units of measurement, abbreviations, chemical concepts, and normative references.

Linking information: While it takes humans time to collate logs, manuals, calibrations, and maintenance reports from different systems, AI can do this in milliseconds.

Think contextually: When asked "Why is the HPLC baseline unstable?", AI checks historical patterns, consults technical notes, analyzes previous interventions, and suggests probable causes—based on your lab's data.

Three practical use cases

Case 1: Quick problem solving

Before: Device displays an error. You search the manual, call colleagues, contact customer service. Loss: half a day.

With AI: The system responds in seconds: "Error E-104 has occurred three times in the last 60 days. In all cases, it was resolved by restarting the lamp module and checking the nitrogen flow."

Case 2: Immediate access to information

Before: You need the stabilization time for your HPLC. Where was this documented? In the manual? In the SOP? In an email?

With AI: "According to SOP-HPLC-02, Section 4.3: The system requires 20-30 minutes of stabilization time before injection."

Case 3: Automated training

Before: New staff, different interpretations of the same procedures, variability between shifts.

With AI: The system provides real-time guidance: "Don't forget to flush the valve and check the vial volume before starting the injection."

Limits and risks: The honest perspective

AI is powerful, but not perfect. You should be aware of these limitations:

No autonomous decisions: AI makes suggestions and analyses, but should never independently interpret test results, authorise product approvals or validate analytical reports. Your judgement always has the final say.

Dependence on data quality: If logs are incomplete or manuals are outdated, the AI will reflect this inconsistency. The solution: Keep your data up to date and complete.

Potential for error: Even the best systems can misinterpret questions or confuse concepts. Always validate critical decisions yourself. AI is your co-pilot, not your autopilot.

Security and compliance

In regulated environments, these security aspects are essential:

• Complete data isolation between customers
• No use of your data for public models
• European infrastructure for EU operations
• Encryption during transmission and storage
• Role-based access control

The following applies to GMP, ISO 17025, and ISO 15189: Every action must be traceable. Professional AI solutions integrate into these compliance frameworks.

The paradigm shift: From data to knowledge

Traditional lab software records data, generates reports, and meets audit requirements. AI transforms your system into a knowledge platform that interprets, links, and delivers information at the right time.

Laboratories with well-implemented AI report:

• Drastic time savings when searching for information
• Fewer operational errors
• Faster and more informed decisions
• More autonomous staff with less dependence on individual experts
• Better use of accumulated knowledge

Conclusion: Reinforce, don't replace

The promise of AI in the lab is not to replace your team, but to multiply its capabilities. Experienced employees focus on complex problems, while AI handles routine queries. New staff become productive from day one. Years of accumulated experience are transformed into knowledge accessible to all.

The laboratory of the future does not automate everything—it integrates intelligence where it is most needed.

Are you ready to take this step?