Schedule a Meeting

Return to Knowledge Base

How Successful Enterprises of the Future Will Use Automation

Executive Summary

Pandemic-fueled digital transformation initiatives and changing customer demands have altered what the future of work looks like.

One challenge business leaders face is considering what their company’s future will look like. A survey of more than 1,500 executives revealed that the pace of digital transformation is their primary concern, followed closely by improving innovation, modifying business models, lowering cost, and streamlining processes.

Waiting two years for results and struggling with their inability to scale is no longer tolerable— transformative change is necessary now.

At the core of every corporate strategy and future vision sits the most important factor: humans. Behind every form or document is a person waiting for answers. Veterans seeking disability claims, first-time homebuyers wondering if their mortgage loan application will be approved— without people, there is no business.

Transforming your digital transformation strategy to focus on delivering better human outcomes will enable your organization to embrace the future. Business leaders are looking for a strategy (and technologies) that makes this vision a reality—where employees are engaged, manual work is automated across lines of business, and they can provide significant value to their customers (and the world at large).

And yet, previous visions of an automated future either hide or hinder the human role in each step of the automation process. Consider, for example, traditional rule-based solutions that replace the human hand with a bot’s programmed action. Behind the curtain, when the bots break or process changes, humans are brought in to bridge the gap, resulting in days of frustration for employees and the end customers. It’s an unsustainable future, as quick wins for smaller projects create further complexity and cost when the initiative is extended organization-wide.

Organizations that embrace the power of human and machine collaboration will be leaders in their domain in the near future.

It’s time to reconsider what the future of work will look like, and with it, the strategy you’ll use to get there.

Key Insights

  • The key to automation success lies in understanding which steps in a process can be automated and which steps can’t.
  • Humans will remain an integral part of the process for the foreseeable future.
  • The most successful companies will be the ones that invest in human outcomes today, leveraging sophisticated ML technology to simplify, solve, and ultimately, evolve what organizations consider automation.
  • By 2025, humans and machines will collaborate seamlessly, providing employees with the time needed to focus on high value work and better data to make concise and confident business decisions.

What’s Broken in Today’s Processes?

Customers communicate with organizations via documents. Those images, forms, and content types are akin to a human story—the story your customers are communicating with you.

To best help your customers, you need to quickly and accurately understand that story. This starts from the moment content enters the doors of your organization, making data processing the first step of providing a delightful customer experience.

Despite that, the content ingested by your organization is— unsurprisingly—very “human” in its quality, condition, complexity, and variability. The stream of PDFs, mobile uploads, structured documents (e.g. tax forms), semi-structured pages (e.g. bank statements, invoices, identity documents), and unstructured documents (e.g. medical records, emails, contracts) creates a complex processing mess that makes rule-based automation negligible at scale.

Consider a young couple going through their first home purchase. They’re excited, but extremely nervous about embarking on such a large life decision. One mistake in the application process could be the difference between this couple being approved for a loan and growing frustrated with the lender they’ve chosen, or worse, being wrongfully denied a mortgage. In their frustration, they decide to use one of the many digital-only options now on the market—and tell their friends to do the same.

This isn’t unlikely, as the next generation of first time home buyers have grown up in a world that is fast, digitally efficient, and personalized. Long-standing national mortgage lenders need to be sure that their customer experience doesn’t paint them as outdated, digitally inept, unable to provide the personalized customer experience that these customers have grown to expect.

In fact, a recent 2020 survey found that nearly 80% of customers are now expecting a personalized customer experience1.

Classifying data types and extracting actionable data intelligence is directly connected to decision making and delivering outcomes that provide a positive customer experience. The less time your employees spend manually entering data, the more time they’ll have to upskill their abilities and provide customers the experience they’ve come to expect—ensuring your organization stays competitive.

Organizations see value in their data but are struggling to access it:

90% of enterprise data is unstructured and increasingly difficult to extract2. This figure is increasing 30% year-over-year.

If you’re looking to scale the transformation of diverse content types into machine-readable data, a ML-based solution is the only technology capable of bridging that gap and mimicking the understanding that humans are able to provide.

By unlocking better data, you’re opening the gates for more information to flow across your organization.

How To Win: Human & Machine Collaboration

Legacy automation technology solves problems task-by-task. These outdated rules-based approaches look for ways to remove humans from specific parts of your work process and replace them with technology. Some employees are replaced with limited and rigid automation “wins” that are too conditional, while others are treated as cogs in a machine, surrounded by bookends that struggle with change and variance, both in real-time and in the future. However, this approach is counterintuitive to how employees work—and when you consider how complex most enterprise-level business processes are, it’s easy to imagine how this struggles to scale.

Your organization needs to be able to seamlessly synthesize the story trapped in the content your customers have submitted into accurate, actionable data. That’s where ML makes an impact when you’re looking to scale an automation initiative.

ML does more than make your data powerful—it’s also the technology that makes your employees more effective. When taking context into consideration, machines can learn to read and process documents with similar competence to that of humans. This deeper level of understanding unlocks process changes that benefit both your employees and your customers, bridging the gap between human understanding and machine efficiency.

With a human centered approach, you’ll consistently find ways to empower and improve the experience of everyone involved in your business process—and ML is the only technology that can provide this at scale.

At face value, ML might appear “inhuman,” but its ability to empower humans is nearly infinite.

There has been a consistent push to increase automation rates, but that same push neglected the importance of accuracy in the data being extracted. When it comes to highly regulated industries, a high automation rate with low corresponding accuracy doesn’t just conflict with your overall business objectives—it can be financially and even legally harmful to your organization.

Automation, Continuously Improved

With an ML-driven solution like Hyperscience, you can confirm accuracy first and improve automation over time—continuously improving the ROI of your automation initiative and improving your employee experience.

The Human Factor

When the technology isn’t confident that it will hit your defined accuracy threshold, it will bring in humans to review and resolve edge cases. Each time this act of human supervision occurs, the software improves its ability to accurately understand your data in the future.

Right on Target

This active training results in lower error rates and higher automation over time, tailored to your specific work process, employee, and customer needs.

This learning is intuitive and fundamentally human in nature, similar to onboarding a new employee. On an employee’s first day, they might be unsure of how your organization does certain tasks, but over time they learn and become a regular contributor to their team’s success. An ML-based solution is no different. While it may require help from your employees at first to learn the specificities of your business, it quickly learns what certain forms look like, which data is important, and how to best enrich or validate that information to ensure it’s actionable downstream.

At the same time, your employees are empowered with opportunities to upskill their abilities. Every increase in automation rates saves your employees more time, which can be used to work on higher-value tasks, such as re-engineering workflows or supporting customers.

Machine Learning augments humans with time and higher quality data.

With a human centered approach to automation, you reduce the inefficiency of manual processing—but more importantly, you’re working toward improved customer satisfaction, reduced costs, and happier employees.

ML empowers your employees by its ability to mimic the learning and comprehension of a person, and by its ability to elevate employee potential towards greater goals and deliver better customer experiences.

For organizations facing a skilled labor shortage amidst the Great Resignation, it unlocks the ability to complete higher-value work with the same (or fewer) resources. It also encourages a digital, agile workforce that can evolve to meet future market needs.

A better employee experience always unlocks improved customer outcomes.

Better customer outcomes are essential for gaining and retaining customers, and the best customer outcomes are achieved by providing an experience that outclasses the competition. If you’re able to provide customers with personalized communications, faster answers to their questions, and better service than your competitors—you’ll catch and keep customers.

That level of customer experience isn’t possible without accuracy and efficiency. Customers are expecting more, and faster, which is why so many organizations have looked to automation as the answer.

A human centered approach to automation provides a multi-level competitive advantage from the start, and it begins by putting customers at the heart of your objectives. Human centered automation also takes into consideration your employees, creating a symbiotic relationship that enables your employees to provide better service, your customers to receive better service, and your organization to realize higher return at scale.

How Winning Companies Approach Human Centered Automation

While there are a variety of factors that could hinder scale, one key issue with previous approaches to automation comes in the form of rigidity.

Legacy, rules-based automation solutions lack the flexibility and customizability required to enable seamless change management. Many of these solutions involve lengthy integration timelines, are locked into specific tasks until programmed otherwise, and are essentially “black boxes” that provide little in the form of analytics. The problem with this rigidity is that humans are not predictable. Markets change fast, content varies widely, and if your automation approach doesn’t account for that—you’re setting yourself up for defeat.

A human centered approach to automation adapts to change in the same way humans do. If your customers evolve, or the employees that work at your organization change, your approach to automation can adapt to your business changes.

ML enables this level of flexibility via ongoing employee collaboration, and learning from that collaboration, to improve automation rates over time.

Taking a human centered approach is all about keeping humans in mind; If you’re a mortgage lender trying to keep up with demand and better serve customers, you’ll need to process an endless array of documents—so flexibility in doing so is key to ensure that your customers get to closing without aggravation. If you’re a large-scale government agency, you need to be able to update your processes at-will based on changes within policies, laws, and needs—so customizability is key to maintaining efficiency and accuracy without downtime.

Market changes are often unexpected and variation in your business process can lead to legacy technology failing when you need it most. By considering market variability, an approach that provides the flexibility to change or upgrade business processes with minimal downtime is integral to consistently improving operations.

Modularity and ease of integration are critical to your automation approach where the ultimate goal is delivering future-forward transformation for your organization.

Conclusion

A recent McKinsey study of C-Level executives found that organizational agility directly improved the financial performance of an organization by 20-30%. This same study found that the three most integral focus areas to unlock this agility were employee engagement, customer satisfaction, and operational performance.

Your automation strategy must address all of these key factors in order to succeed:

  • Improve Customer Outcomes: Happy customers that are receiving faster, more accurate service are customers that will continue to return to your organization in the future.
  • Elevate Employee Potential: Engaged employees, upgraded with the power of ML, are happier and more productive.
  • Unlock Organizational Agility: Modularity enables easier change management now and in the future.
  • Decrease Cost and Risk: Bridge the gap between human understanding and machine processing with an automation approach that complements AI with human supervision.

Watch this short platform demo to see how Hyperscience leverages proprietary Machine Learning to extract data from complex documents.

1 Source: McKinsey – Customer Experience Survey 2020

2 Source: Gartner

More Insights

Customer Stories

State Department of Healthcare Policy & Financing Automates SNAP and MAGI Documents

See how one U.S. state implemented Hyperscience’s enterprise AI solution to help process millions of SNAP and MAGI forms.

Customer Stories

ALMAC Streamlines Order Processing with Hyperscience

See how ALMAC streamlined order processing to create faster lead times.

Article

Intelligent Document Processing (IDP)

A comprehensive overview of Intelligent Document Processing, how it works, and how industries are benefitting from it.