Artificial intelligence (AI), machine learning (ML), and automation in diverse expertise have transformed the digital workforce. While this has significantly minimized the need for human labor and proliferated profits, this shift in trends has instilled a fear of losing jobs and relevancy of personnel in the workforce.
There exists fear in the minds of people that age or culture will restrain them from staying up-to-date with the latest technologies and the workforce of the future.
However, change is inevitable and has always been present for workers in any industry. While the advent of AI and ML technologies is stirring up a rapid change, it is evident from history that people must not fear change.
Any organization that intends to implement process automation must realize that machines without technical human intervention can yield less relevant and underwhelming results, while humans without automation can never escalate productivity levels equivalent to an automated workspace.
There must exist a balance between the two in the following manner -
Humans must perform three critical roles when operating machines -
Smart machines aid humans in expanding their abilities in three distinct ways, namely -
Humans-in-the-loop implies that no task shall get completed solely by a machine or a human working; instead, each project will have a human and machine collaborating on shared responsibilities.
As the transition to the workplace of the future takes place, training will have a significant role, where information from subsequent actions, assisted by human oversight, will improve the model of automation.
Here are listed some factors that determine the necessity as-well-as the degree of human-machine collaboration in any work environment -
The implementation of robotic process automation in various industries has scaled down the probability of human error, ensuring consistency, adherence to processes, compliance, as-well-as reinforced security by mitigating mistakes that resulted in misconfigured systems.
By implementing traditional methods, web data extraction involves creating custom processing and filtering algorithms for every website and then using a separate tool to consolidate the scraped data with your IT infrastructure.
By implementing automated web data extraction, transformation, and transport tools, you can keep your critical data running with no need to perform laborious manual tasks or custom script writing.
Despite ML and AI handling laborious tasks and yielding error-free outputs, it is imperative that a human reviews all the reports generated via automation and ensures proper decision making.
For instance, when ML models in your email inbox, examine every message which you mark as spam or revive from junk to reveal new patterns and mitigate the frequency of such errors in the future.
This increase in the accuracy and sophistication of such models for businesses implies less human intervention, better business process automation, and more time to emphasize value-add activities.
To clearly understand the ROI and economic gains, let’s dive into some numbers. The following calculation is merely a representation to act as a template for your understanding of the ROI of automation. The expenses of custom automation equipment might differ depending on your needs.
This representation of expenses does not consider quality, interest, throughput, maintenance, depreciation, training, or other factors.
Let you have a partially manual system that requires three skilled operators who work three shifts per day. Consider that the charge per operator is $50,000 a year, summing to a total annual labor expense of $450,000.
On the other hand, assume you have installed a fully automated system that costs $400,000 to design, build and install. This system requires one unskilled operator at $35,000 a year for two shifts per day, amounting to $70,000 a year.
The total expense for automation is $400,000 summed with $70,000, amounting to $470,000. This would translate to a first-year ROI of -$20,000 ($470,000 spent in the automated system minus the $450,000 for the partially manual system).
However, the payoff begins in the next year as there is no capital investment for the automated system. With reduced labor expenses, yearly production costs are substantially lower contrasted to the manual system.
Over the course of two years, production with the automated system amounts to $540,000 ($400,000 summed with $140,000 which is the operator expense for two years), while the manual system would cost $900,000.
The final calculation represents a return of $360,000 ($900,000 from the partially manual system minus $540,000 from the fully automated system), amounting to a 90% ROI.
Manufacturers must compare the cost of automation and labor in their operational expertise to make their final choice. While automation might be economical down the lane, it will not be necessary to automate your mundane tasks if the labor is cheap.
For instance, the food processing industry holds high automation potential; however, adopting automation will require considering the manual processing cost, the abundance of labor, benefits produced by automation, and if they can outweigh the benefits of cheap manual labor.
The final factor concerns remain the social, legal, and ethical approval of autonomous technology. This step is one of the most crucial factors which will determine the pace of change.
The legal pace of change is a lot slower relative to technological change, and social prejudices can be equally reluctant to adapt. This can lead to years being added up in the roll-out of any new technology, despite it being technologically and economically ready.
The application of AI in mundane tasks will boost the effectiveness of human-machine collaboration, leading it to perform better than a manual workforce. With a plethora of perks offered by intelligent process automation, you must not entirely automate your workplace and ensure a healthy human-machine collaboration.
It is also imperative to consider the factors that might push you to implement automation over cheap and abundant labor and ensure that your net profitability yield is high and data extraction is error-free.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
With an automated data extraction solution, loan documents can automatically be processed end-to-end without any human errors and delays. Automation in loan document processing prevents downtimes, eliminates data redundancy, and allows companies to respond faster to client queries. By combining machine learning with deep learning and OCR, companies can eliminate huge costs, derive actionable insights, and streamline loan processing and approvals through efficient data extraction and analysis.
Mortgage lenders receive multiple identity and income verification documents along with different forms from loan applicants in a variety of formats and styles. Traditional OCR solutions fail to extract data from these semi-structured documents and that’s why more and more lenders are adopting intelligent document processing solutions. IDP solutions not only extract data correctly, they are able to validate extracted data against predefined rules in order to improve accuracy.
Intelligent Document Processing is an automation technology that captures information from a myriad of documents and data sources, extract data, and organizes it for further processing. IDP solutions enable businesses to seamlessly integrate with core processes, eliminate manual labour, address challenges faced in reading different document layouts, and meeting legal & compliance requirements. Accurate data is the foundation of every organization, and IDP assists businesses in dealing with the complexity of processing huge volumes of documents, helping them automate manual data entry processes, and move away from traditional semi-automated OCR workflows.