Artificial Intelligence (AI) in laboratories: where are we today and what does the future hold?

Artificial intelligence, also known as AI, is no longer a topic that only concerns large technology companies. Laboratories are also increasingly looking at smart software, image recognition and automation. Not because the lab should be completely taken over by computers, but because many tasks can be carried out faster, more consistently and with better traceability.

For laboratory technicians, this is a familiar theme. Many laboratories are dealing with high workloads, staff shortages and increasingly strict requirements for quality and speed. At the same time, every result must remain reliable. This is exactly where AI, in combination with laboratory automation, can play a valuable role. The technology does not replace the knowledge of the analyst, but supports daily work in areas where repetition, error sensitivity and time pressure come together.

AI in the laboratory: what do we actually mean?

AI sometimes sounds more complicated than it is. In practice, it often refers to software that learns to recognise patterns. Think of a system that analyses images, detects deviations or processes data more quickly. In microbiological laboratories, this can be seen, for example, in AI-driven colony counters. Such a system examines a plate, recognises colonies and helps with counting.

This does not mean that the system decides everything independently. The laboratory technician remains important for checking, assessing and making decisions. AI is therefore mainly a tool. It can take a lot of work off your hands, but it must fit within a well-organised process.

Why AI is especially relevant now

Interest in AI is growing because many laboratories are facing the same limits. There is a lot of work, there are not always enough people and the pressure to deliver quickly is increasing. Manual work also remains sensitive to differences between employees. Someone may be more tired at the end of the day than at the start. Small differences can also occur when pouring, counting or processing results.

Automation helps reduce these differences. A system does not get tired and performs the same action in the same way every time. This ensures consistency, reproducibility and reliability. AI can add an extra layer to this, for example by assessing images more intelligently or recognising trends in results.

From manual counting to AI-driven colony counting

Colony counting is a clear example. In a manual process, plates are checked, counted and processed one by one. Errors can occur during this process. A handwritten 28 can be read as 38. A number can be copied incorrectly. Or an employee may become distracted while entering results.

With an automated colony counter, this process changes. The system creates an image of the plate, links it to a barcode and stores the result. This creates traceability: you can later look back at the photo, the count and the associated data. This is not only useful for checks, but also valuable for quality and audits.

At AAA Lab Service, the development of systems such as the Iris Irina combination responds well to this need. This combination can not only count colonies, but also differentiate between multiple colony types. This means that different colony families can be recognised and counted separately. The focus is therefore not only on faster counting, but also on better differentiation, better recording, less manual input and a more consistent process.

AI does not replace the laboratory technician

A common misconception is that AI and automation are mainly intended to replace people. In practice, the situation is more nuanced. Especially in laboratories, the knowledge of analysts remains essential. A system can assess a plate in a fixed way, but a laboratory technician can include experience, context and doubtful cases in the assessment.

An analyst does not look at just one data point. Sometimes someone assesses a plate again from a different angle, looks at the background of the sample or combines multiple observations. This human perspective remains important.

What does change is the division of work. Repetitive tasks can be reduced. Think of long periods of manual counting, repeatedly performing the same actions or copying results. This creates room for other tasks in the laboratory. AI and automation therefore mainly ensure that people can be deployed more intelligently.

Better quality through less variation

Quality in the laboratory largely depends on control over the process. The less unnecessary variation there is, the more reliable the result. Automation helps with this because actions are performed in the same way every time.

Take filling plates with agar, for example. When this is done manually, the volume per plate can vary slightly. An automated system does this much more consistently. This helps laboratories stay within fixed margins and makes the process easier to control.

This also applies to colony counting. With a full plate, an employee may decide to count only part of the plate and calculate the total from that. A system counts in a fixed way, regardless of whether there are few or many colonies. This makes the process more predictable and easier to compare.

The role of data and LIMS

AI needs good data. That is why the role of a LIMS (Laboratory Information Management System) is becoming increasingly important. When samples, results, barcodes and images are recorded properly, a strong foundation is created for further digitalisation.

A LIMS helps store data centrally and reduces dependence on loose notes or manual input. When equipment is connected to the LIMS, results are processed more directly. This reduces the risk of typing errors and makes it easier to look back later.

This is important for AI. Smart software can only work well if the underlying data is reliable. Laboratories that invest now in clear processes and good data quality will be better prepared for future AI applications.

Where are we today?

Today, we mainly see practical applications of AI. Think of image recognition, automated colony counting, data processing and support with quality control. The technology is therefore not something abstract, but is already being used in concrete laboratory processes.

At the same time, it is important to remain realistic. AI is not a solution to every problem. A poorly organised process does not automatically become good by adding AI. The foundation must be right: clear working methods, reliable equipment, good training and checks by employees.

What does the future hold?

Over the next five to ten years, laboratories will probably continue to automate further. Not every laboratory will go equally far. Some laboratories will mainly automate the most time-consuming steps. Other laboratories will connect multiple systems and automate a larger part of the workflow.

Fully automated laboratories without laboratory technicians are not likely in the near future. The work is too specialised for that, and human assessment remains too important. However, the collaboration between people and systems will become stronger. Equipment, LIMS and AI will become more connected. This will allow the lab to work faster, plan better and detect deviations earlier.

Conclusion: AI starts with a smart foundation

Artificial intelligence offers many opportunities for laboratories, but for now the greatest gains lie in practical improvements. Think of faster counting, less manual copying, better traceability and more calm in the process. AI does not replace the laboratory technician, but supports the work in places where it truly adds value.

Do you want to prepare your laboratory for the future? Then start with a smart foundation: automate repetitive processes, improve your data quality and ensure reliable connections between equipment and systems.

Are you curious which step best suits your laboratory? Please contact AAA Lab Service. We will be happy to think along with you about a more efficient, reliable and future-proof laboratory!