Cognitive Automation

Augmentation is the near term approach here, but I expect augmentation to become more automated over time.    Checking, validation and  Numbers presented here are interesting, dependent much on the specific kind of tasks that are involved, even within an industry.  The tasks may then be changed to better fit with the augmentation.

Cognitive Automation is the Immediate Future of Team Management    By Srini Murali / 04 Nov 2019 / AI / Productivity / Work in ReadwriteWeb

Cognitive automation
   
For all the anticipation of increased automation at work, commentators have spent a lot of energy trying to convince people it can only handle easy, repetitive processes. It’s time to finally confront the truth: Per the McKinsey Global Institute, today’s robots can handle up to a quarter of the average CEO’s job and 35% of management tasks.

While 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, judgment-based tasks — what we now call “knowledge work.”

Using specific AI techniques that approximate the way our brains work, cognitive automation helps us make better decisions, complete tasks faster, and meet goals more easily — and it’s swiftly gaining traction. 

KPMG predicts spending on intelligent automation will hit $232 billion by 2025, up from $12.4 billion in 2018.

Of course, we’re a long way off from managerial jobs being fully automated, but these findings indicate that automation can — and should — play a bigger role in how we lead the 21st-century workforce.

Where Cognitive Automation Fits Into the Workforce

At Exela Technologies, our managers wouldn’t be able to support our global workforce of more than 22,000 employees without the help of cognitive automation. Among other things, this technology enables us to obtain information from scattered sources, conduct deep analysis, and collaborate more easily.

We’re not the only ones, either. Deloitte found that increased reliance on cognitive automation in the insurance industry improved firms’ recruitment and development processes, removing much of the heavy-lifting that human managers once performed.

Business leadership has a lot to gain from cognitive automation. Here are some ways managers can take advantage of it.

1. Capture and dissect data.
Intelligent systems can gather more data than manual processes, then analyze that data more effectively to uncover trends, detect anomalies, and produce predictive models.

One sector where we see this technology emerging rapidly is healthcare. AI technology can now compare a patient’s medical history with established guidelines for common illnesses to help identify gaps in care and specific opportunities for improved treatment. When done by a human, this analysis could take hours. When done by a machine, it takes seconds.

Attended cognitive automation — where humans work alongside automated systems — enables 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 the prevention of fraud, waste, and abuse. On the provider side, intelligent automated data processing systems are capable of reviewing 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 detect potential fraud, waste, and abuse to limit overpayment.

At Exela, we build and deploy systems such as these to perform services for our healthcare industry partners. We also created similar tools that assist with other areas of our business. As part of the sales lead generation process in our legal arm, for instance.   .... " 

We monitor federal and state court activity for business opportunities, such as large class-action settlements. Given that there are tens of thousands of daily updates to case files, it’s nearly impossible for our employees to efficiently differentiate between the “good” and “bad” leads.

To address this, we developed an AI system that uses machine learning based on exposure to an initial sample set and iterative tuning using continuous feedback. The system detects “trigger events” from thousands of regular updates.

These trigger events are then classified, (e.g., complaint, dismissal, etc.), and the content summarized, it alerts stakeholders and integrates with our existing CRM systems to automatically log the newly acquired data. ... "