Harnessing AI for Next-Level Quality Assurance

While it can seem like Artificial Intelligence (AI) is a fancy tool only applicable in certain industries, AI is closer to you than you might think. From social media to your streaming service, AI processes are assisting with data processing and management in all sorts of innovative ways.

As the modern lab continues to evolve, AI adoption is becoming more commonplace. The increasing demand for accuracy but also shorter turnaround times has laboratories seeking technological and often digital solutions to help them achieve their business and operational goals. Lab analysts needn’t fear, AI isn’t coming for their jobs, but what it can do is support the work of lab staff to boost efficiency and ensure that quality control is optimised.

Quality assurance in labs

The quality assurance processes in labs are all about ensuring that the laboratory’s procedures, data analysis and results are of the highest quality. Without good quality assurance, there is a far higher probability of errors which can affect the results delivered. This can have a direct effect on product research and development, the development of environmental management solutions, and the manufacturing of products.

In testing labs, the integrity of samples is paramount in the quality assurance process. A good quality assurances process will make sure the samples aren’t compromised, which can lead to costly setbacks. Of course, good quality assurance means that the results from the lab can be trusted and they are reproducible. As laboratories seek to build strong relationships between themselves and stakeholders, good quality assurance provides quantitative and qualitative evidence of why the lab can be trusted.

Finally, safety also forms part of lab quality assurance. The process should make sure all the equipment is functioning properly and that proper procedures are documented and followed for handling samples, hazardous materials, and chemicals. By doing this, labs can prevent minor accidents that could lead to bigger safety risks.

Levelling up with AI for QA

AI opens a world of possibilities for the modern laboratory. Because of the big volumes of data and frequent tests and analyses, labs can benefit quite a lot from AI and machine learning. Traditional lab operations often involve repetitive and time-consuming tasks such as data backups, data review, and preliminary analysis. By automating these tasks, AI allows scientists to focus on higher-value activities such as experimental design, interpretation of results, and innovation.

In terms of quality assurance, there are a few key benefits from utilising AI:

Greater speed without greater risk of errors – The speed at which data can be processed and reviewed using AI significantly reduces the overall time required to complete experiments and projects. This acceleration in the workflow is crucial for meeting tight deadlines and maintaining competitive edges in research and development. Furthermore, AI’s ability to quickly analyse vast amounts of data helps in identifying trends and anomalies that might be missed by human reviewers. This enhances the accuracy and consistency of repetitive tasks, ensuring that data is reliable and free from human error.

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Cost management – Automation of tasks is one of the big advantages of AI and this can assist with cost management by potentially reducing overtime or weekend work hours, which aids operational costs. The resources saved from routine tasks can be allocated to more strategic investments and research, and this includes the brain power of key laboratory staff. Laboratories can also expand their capabilities without a proportional increase in manual workload and this assists labs in scaling their operations up without greater cost pressure.

Optimise resources – AI systems can do real-time monitoring of experiments and equipment to provide immediate feedback should a problem arise. It also means staff don’t have to be in the lab watching over the analytical instruments all the time, particularly if it requires hours before there are results and they could monitor the process remotely. This improves safety and resource management. AI can also assist with efficient resource management to reduce waste and lower the overall environmental impact while simultaneously checking instruments for preventative maintenance.

Labs looking to the future finding success now

Chemetrix is proud to be a local supplier of Agilent innovation. Agilent is on the forefront of leveraging software to fuel lab productivity – testing and proving the value of AI in day-to-day operations. This world-leading brand is seeing results from labs that are testing the integration of AI into their operations.

Agilent 5977C GC-MSD

In pilot testing, data review, a task that used to take nearly an hour to complete, was reduced to a few minutes, using AI capabilities. This type of efficiency gain in any lab would boost productivity and allow scientists to focus on more complex and high-value tasks. This type of result underscores the potential of AI to revolutionise lab operations, making them more efficient, cost-effective, and high-quality.

“Quality control labs rely on analytics to ensure product safety. We’re using new, exciting software approaches to enable faster, more efficient, and more accurate results.” – Tom Lillig, VP, GM, Agilent Software Informatics Division

We want scientists and researchers to dedicate the majority of their valuable time to critical thinking and complex problem-solving. So, embrace the power of technology and boost the efficiency of labs by offloading repetitive and mundane tasks to AI. Whether its through software or through instrument monitoring, there are different ways labs and their quality assurance processes can be improved through artificial intelligence and machine learning to enhance research, product development, and analysis now and in the future.