#4 How machine learning could save your employees thousands of hours on repetitive tasks

During what is being called “The Great Resignation”, making the most of skilled employees and your existing talent pool is critical to business success, and potentially survival. Doctors, researchers, analysts, engineers, scientists, and other professionals require years of education and training. This puts them in high demand, meaning their time is often limited.  

There are also many tasks – such as reviewing medical scans – that require skill to interpret and detect anomalies but are also time consuming and repetitive. It is here that machine learning (ML) can make a difference. While ML (and AI) cannot replace skilled individuals, such technologies can augment expert talent by taking over repetitive tasks – often with greater accuracy than even the most skilled professional. This not only helps improve the accuracy of outcomes, but also ensures that professionals are able to put their time into high value cognitive tasks that are better suited to the human mind.  

What is machine learning and why does it matter?  

ML algorithms automate the gathering and processing of limitless information, analyse datasets, and detect patterns in a variety of forms. These scalable models employ computer vision, natural language processing (NLP), voice-to-text and advanced analytics capabilities, automatically providing the information that users require in real-time.  

ML has several advantages over humans in some contexts. For starters, ML has virtually no limit to its memory. It can process and ‘remember’ millions of images, or other pieces of information and instantly recall those memories as needed. Machines also have no need for sleep, they run 24/7 at the same level of performance – unlike humans.  

ML can be trained to be the ‘eyes and ears’ of a human – ML allows humans to create their own assistant and transfer their knowledge. What’s more, this assistant gets better over time and will not then leave to get another job. That knowledge and skill stays within the organisation. Further, with what’s known as unsupervised machine learning, the algorithm can start to analyse and signal to patterns that the human eye would miss.  

ML in action –  

Kainos has worked with leading research organisations to build ML solutions that help to improve the efficiency and accuracy of observations for scientific investigation. For instance, researchers often spend hours monitoring for anomalous behaviour in subjects during experiments.  These changes can be subtle and difficult to spot, requiring round the clock observation. Knowing what to look for requires skill and training, but the task itself can be very time-consuming and monotonous. Not to mention the risk of error, as humans are subject to feeling tired, bored or otherwise distracted, meaning important events might be missed.  

Kainos has developed ML solutions that act as researchers’ eyes and ears to overcome these challenges. The team built a system that can be trained on what to look out for by painting a picture of what “normal” looks like. It then observes and alerts the “human in the loop” when an anomaly is identified and requires investigation. This way, instead of having trained scientists effectively watching paint dry, they only have to look when there is a change in situation. In an unsupervised ML environment, the system can go even further by detecting and signalling other anomalies it has not been specifically trained to spot – like a new irregular pattern or signal that it can apply “learned” knowledge to and flag as a risk. There is the added benefit that, as mentioned above, ML does not tire, it performs consistently, does not leave to get another job, and it continues to improve over time.  

ML can also help to alleviate the burden of admin tasks from researchers. Researchers have to make very detailed and thorough notes during an experiment or observation, by handwriting or dictating their findings. Transcribing these notes for analysis is time-consuming, laborious, and error prone. ML can do the heavy lifting here by integrating voice-to-text and computer vision elements that automatically transcribe handwritten and recorded voice notes from lab teams in real-time. The ability to have live voice notes transcription is a particularly useful functionality for high-contamination lab environments where note taking is a challenge. This could be taken to the next level and functionality expanded with a ML algorithm analysing transcribed notes for trends e.g., comparing notes with those previously recorded/transcribed. 

Supporting human talent with AI – keeping the human in the loop 

The same ML features that are invaluable for scientific research can be applied to other fields. For example, object capture can be used in agricultural work, to track deforestation and climate change, or could even be installed in street-side monitors to detect road incidents in a smart city. In medicine, algorithms can compare limitless datasets, whether millions of eye images for detecting patterns of disease or comparing records of illness and treatment. This gives healthcare professionals the information they need alongside recommendations for potential courses of treatment to consider. 

Because ML can automatically handle mundane or time-consuming jobs, it’s application in jobs where the processing burden is high holds great potential. In insurance, ML’s limitless computer power and memory can be used to automatically compare and analyse historical and real-time data, claim records, and trends. Armed with this information, human claim handlers are better equipped in decision-making.  

Smart Audit, our ML-driven risk management platform for Workday, is another example of how smart technologies augment existing teams, as well as eradicate compliance risks. The system can handle repetitive tasks, manage zero-trust access controls, and monitor for compliance issues. In only 12 months, Nasdaq has been able to save employees 11,000 hours of work by implementing Smart Audit.  

Optimise your workforce, accelerate growth with ML 

ML has untold potential as a tool to help existing workforces. With professional help in short supply, every hour saved through smart solutions – and spent on critical business tasks – can contribute to better outcomes, reduced costs, and a considerable return on investment.  

Kainos can help. We were shortlisted in the Outstanding AI ML Project Awards category of Computing AI & Machine Learning Awards for work with our previous clients and have years of experience in developing the right solutions to help you achieve your business goals.  

If you would like to know more about making machine learning work for your organisation, contact us today.