For all the anticipation of increased automation at work, commentators have spent a lot of energy trying to convince people that it can only handle easy, repetitive processes. It’s time to finally face the truth: According to the McKinsey Global Institute, today’s robots can handle a quarter of the average CEO’s job and up to 35% of management tasks.
Whereas robotic process automation refers to using robots to speed up concrete processes, cognitive automation takes a more advanced version of the same underlying tool set and applies it to more conceptual, decision-based tasks – what we now call It’s called “knowledge work”.
Using specific AI techniques that approximate the way our brains work, cognitive automation helps us make better decisions, accomplish tasks faster, and accomplish goals more easily — and it’s gaining traction rapidly. receiving.
KPMG estimates that spending on intelligent automation will reach $232 billion by 2025, up from $12.4 billion in 2018.
Of course, we are far from having managerial jobs fully automated, but these findings indicate that automation can – and should – play a big role in leading the workforce of the 21st century.
Where cognitive automation fits into the workforce
At Excella Technologies, our managers would not be able to support our global workforce of over 22,000 employees without the help of cognitive automation. Among other things, this technology enables us to access information from scattered sources, conduct in-depth analysis, and collaborate more easily.
We are not alone, either. Deloitte found that the increasing reliance on cognitive automation in the insurance industry improved firms’ recruitment and development processes, removing the heavy-handedness once performed by human managers.
Business leadership has a lot to gain from cognitive automation. Here are some ways that managers can take advantage of this.
1. Capture and dissect the data.
Intelligent systems can collect more data than manual processes, then analyze that data more effectively to uncover trends, detect anomalies, and generate predictive models.
One area where we see this technology emerging rapidly is healthcare. AI technology can now compare a patient’s medical history with established guidelines for common diseases to identify gaps in care and specific opportunities for better treatment. When done by a human, this analysis can take hours. When done by a single machine, it takes seconds.
Participated in cognitive automation – where humans work with automated systems – enabling great advances in accuracy and productivity.
Another area in the healthcare ecosystem where we see cognitive automation adding significant value is clinical documentation improvement and fraud, waste and abuse prevention. On the provider side, intelligent automated data processing systems are able to review large volumes of healthcare records to identify potential information gaps and coding errors, so providers are more likely to be paid in full and on time.
On the payer side, cognitive automation can help flag anomalous transactions to limit overpayments to detect potential fraud, wastage and abuse.
At Exela, we build and deploy such systems to perform services for our healthcare industry partners. We’ve also built similar tools to help with other areas of our business. For example, as part of the sales lead generation process in our legal branch.
We monitor federal and state court activity for business opportunities, such as large class-action settlements. Given that case files contain thousands of updates daily, it is nearly impossible for our employees to efficiently differentiate between “good” and “bad” leads.
To address this, we have developed an AI system that uses machine learning based on an initial sample set and exposure to iterative tuning using continuous feedback. The system detects “trigger events” from thousands of regular updates.
These trigger events are then categorized, (for example, complaint, dismissal, etc.), and the content summarized, this alerts stakeholders and syncs with our existing CRM system to automatically log newly acquired data. integrates.
Thanks to automation, the heavy lifting is done long before humans enter the picture.
Within your own organization, you can use an automated data management system to compile, classify, and clearly display data from different sources.
Not only can you process a lot of data this way, but you will gain a more complete understanding of current market conditions and historical trends as well as the trajectory of key indicators.