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Human-Machine Collaboration Can Positively Reshape the 21st Century

March 18 2022

3 min read

Almost 110 years ago, in 1913, Henry Ford’s innovation of the first moving assembly line started operating, allowing for the mass production of the Model T. Thanks to Fords’ initiative and ingenuity, the assembly line established one of the first sequenced relationships between people and technology, capable of reducing overall production time by nearly 90%—going from 12 hours to 93 minutes—and opening the door for future technology

Much like Ford’s invention, the Hyperscience Platform follows the blueprint of dividing tasks between people and technology—based on the needs of the task and strengths of each—creating a symbiotic relationship that demonstrates similar levels of improvement capable of catapulting today’s economy into the new digital era.

Automating for Automation’s Sake

Let’s take a mortgage application as an example. At the start of the process, a person submits an application, and multiple steps later, a loan is issued. Currently, this process takes an average of six weeks to complete, start to finish. With a human and machine working together, this end-to-end process can be reduced to just days.

With improved outcomes like this, amid the rapid advancements of artificial intelligence and machine learning, it’s become clear that we can automate almost anything. But to automate for automation’s sake lacks intention and nuance. People are still far better at many things than a piece of software, and it will be that way for some time. Which is why understanding that technology does have limitations is important.

As unstructured data grows, accurate and complete tasks will become more difficult for machines or humans to complete while working in silos. Instead, every task will have a human and machine working alongside each other on shared responsibilities. When digital transformation and collaboration are prioritized accordingly, this type of co-working relationship can have a significant impact towards unlocking the potential of our future workforce.

As technology takes on the role of ‘helper’ rather than replacement, we expect human-machine collaboration will alleviate some of the greatest workplace challenges facing us today—but only if we move beyond automating for automation’s sake.

Input to Actionable Data

The benefits of automation are far-reaching. Let’s take the United States Internal Revenue Services agency as another example. Heading into this year’s tax season, the IRS faced 10X the usual amount of unprocessed returns from the prior year. While several factors are at play for these backlogs, that fact remains that manually reading and approving documents, alongside mountains of additional unstructured data, grew beyond human capacity alone. Federal agencies—like the IRS—must enact change for the good of all citizens. In addition to relieving employees of repetitive and costly burdens, automation can help serve constituents quicker and more efficiently, reducing the time from claim to decision to a matter of days versus weeks or even months.

Input to actionable data brings humans into the loop to guide machines and simplify the most complex document processes. In the case of the IRS, this could mean implementing automation throughout back-office processes that support the input of data at scale. Ultimately, that can lead to reduced employee workloads and all around greater efficiency.

The Future of Work, Today

The most competitive organizations leverage machines to support document-heavy workflows and arm employees with actionable data to better serve and engage with customers. As automation continues to permeate across all industries, taking stock of balancing the human-machine relationship for work will be vital.

Envisioning a tomorrow, where humans and machines working side-by-side is the norm, a workforce that feels more empowered in their roles becomes well within our reach. If we collectively apply technology and automation in human-centered and collaborative ways, we can make the seemingly impossible, possible.