Modern businesses are seeking every opportunity to automate processes so employees can work faster and more effectively. In other words, they apply automation to transform a process or task that is performed manually into one a machine can handle. While organizations have been automating business processes for decades, they can now take advantage of more advanced technologies than ever. The prevalence of AI-related technologies for this purpose has given birth to the term “hyperautomation.”
What is Hyperautomation
Industry analysts have floated various terms to describe this concept. What Gartner calls hyperautomation, IDC calls Intelligent Process Automation, and Forrester calls Digital Process Automation
As IDC says, “Intelligent Process Automation (IPA) technology and services market. IPA is a strategic business imperative for enterprises to accelerate digital transformation by focusing on business process transformation and automation.”
What Forrester refers to as Digital Process Automation (DPA) it once called Business Process Management (BPM). In The Forrester Wave™: Digital Process Automation For Wide Deployments, Q1 2019, the analyst firm defines DPA as “Platforms that provide low-code features to deliver digital process applications with strong process-modeling capabilities and the ability to create and manage complex, long-running processes.”
While definitions vary, we agree with Gartner’s explanation that hyperautomation isn’t just about technologies. It’s about rapidly and systematically identifying, vetting and automating as many business processes as possible, with a focus on making increasingly AI-driven decisions. Gartner also believes hyperautomation is inevitable and that “everything that can and should be automated will be automated” -- as long as the automation is business driven.
Perhaps most importantly, Gartner puts hyperautomation in the people-centric category of evolving technologies offering transformational opportunities to businesses. Playing up the concept that no single tool can replace humans, the analyst firm also puts democratization in this category. The notion being that AI-based expert systems or virtual assistants “provide advice or take actions on behalf of people to extend their knowledge or expertise.” In other words, this technology helps even the playing field, giving employees the ability to learn or do more. It’s a humanistic approach to automation that makes it more approachable in everyday business.
How has hyperautomation evolved?
Hyperautomation isn’t an end goal that organizations achieve; it’s an ongoing journey to automate more processes and tasks over time. This fits perfectly with the way businesses work. The reality is that most established industries haven’t significantly changed how they operate in decades -- in some cases, in over a century. The way businesses are organized hierarchically, by business units, and by geography remains largely intact. The same is true for the decision-making process and how organizations use data.
While mechanical and computer technologies have modernized businesses to some extent, it is today’s AI-driven technologies that are having game-changing impacts.
It’s true that early AI promises did not live up to the hype. In the 1960s and 1970s, overzealous researchers boldly predicted that computers would think like humans by 1980. While such predictions sidelined the use of AI-driven technologies for some time, we are seeing an upsurge in interest driven by the need to innovate and produce at incredible speeds. Fortunately technologies are keeping pace and proven to work effectively.
It’s just that businesses still often take the wrong approach to applying them.
One of the main reasons for the failure of automation to date has been that organizations struggle to identify the processes or tasks ripe for automation. Instead, they apply automation broadly, overly focused on the technology instead of the business use case. Technologies that drive automation also tend to be expensive, complicated, and time-consuming to implement. As a result, it’s difficult to show a meaningful business impact.
The key is to zero in on a high-impact business use case where automation can easily be applied to show a nearly immediate impact. Take today’s knowledge workers who work with a variety of connected and disconnected systems. They copy and paste data between applications, reconcile information between different programs, and send emails with file attachments. In essence, they serve as the connectors of disparate systems to make workflows happen. These mundane, repetitive tasks are a perfect fit for automation. It’s a matter of choosing the right automation approach.
Consider these automation approaches to date and how they have fallen short.
This approach is focused on improving processes, usually for the sake of maximizing efficiency and/or reducing costs. Think Tableau for better dashboarding, offshoring a help desk, calling upon ServiceNow for ticketing, and deploying ERP to integrate multiple processes.
To enable process optimization, most enterprises hire a large consulting firm to conduct an assessment and make recommendations. The actual project can last months – even years – and often costs considerable sums of money. Though businesses assume process optimization will alleviate their pain, they’re often simply shifting the burden -- such as by outsourcing the work -- instead of addressing the underlying operational inefficiencies.
In an ideal world, a workflow occurs seamlessly from end to end as applications talk to each other and share data in the background. Say a sales rep closes a deal in CRM. The ERP system could be triggered to automatically send out the invoice. Or when an organization is onboarding a new employee in its HR system, role-based accounts could automatically be created in relevant systems.
Though better than process optimization because systems are communicating, essential processes are still bookended by humans in workflow automation scenarios. In other words, people at both ends of the process interact with the tool while the software automating the workflow handles the middle portion of the process. So, enterprises see no change to the quality of data entering or leaving their tools and processes. Plus, they must stitch together their backend systems to enable this – a herculean undertaking.
Robotic Process Automation (RPA)
Some compare Robotic Process Automation to advanced macros. While RPA is intended to mimic and automate mundate, repetitive tasks (much like macros), it can handle a high volume of complex processes while macros excel at simplistic tasks. However, RPAs can only carry out the steps they’re programmed to handle.
RPAs don’t require much technical knowledge and can stitch together data in the background, so they’re a step up from workflow automation. Users simply record what they’re doing and the software learns and repeats this.
However, each user must install RPA software on their computers, and all users working together must be on the same software version. For instance, in a marketing department relying on RPAs, every team member must have the same RPA software installed.
Once RPA records a process and then subsequently kicks it off on its own, it must spin up a virtual environment each time. It must then shut down the virtual environment once it performs all necessary functions. As a result, while an RPA might work incrementally faster than an employee, it’s a drain on enterprise systems and expensive to run.
Plus, an RPA cannot work dynamically. Employees must ask separate requests of it, which it processes separately. For example, “What is marketing working on in healthcare?” and “What is marketing working on in the automotive industry?” are separate requests.
As we’ve shown, there are drawbacks to each of these technologies. But what is similar across all of them is that they require people to learn a completely new technology.
So, where does that leave us?
What does hyperautomation technology mean today?
SKAEL has tackled this head-on by calling upon AI technologies to enable people to more easily and quickly do their daily jobs. Because we focus on the human interaction with AI and map to the way employees already work, our approach -- which we call Digital Employees -- is more humanistic. Perhaps most importantly, by eliminating the most repetitive, boring aspects of jobs, our Digital Employees help humans be more productive and engaged. Simply put, our Digital Employees act as colleagues with their human counterparts, working side by side so humans can do more valuable, fulfilling work.
Not to be confused with digital workers -- which simply means empowering employees with the digital tools they need to be productive -- SKAEL’s Digital Employee is an effective new approach and solution to the automation challenge.
Digital Employees eliminate drudgery and upscale human teams by seamlessly integrating with an organization’s current infrastructure, information repositories, and methods of communication. For instance, Digital Employees, trained to perform a typical sales function, can take care of running reports, generating forecasts, analyzing data, and creating quotes. In turn, the sales team can focus on what matters most -- closing deals and growing business -- by making the best use of their time and talents.
Anything an employee could ask someone via Slack, email, instant messaging, phone, and other common tools, they can ask of a Digital Employee. Digital Employees correctly interpret the request that employees make, access appropriate systems to take care of the request, and provide immediate answers in a human-friendly format.
Digital Employees complement and upgrade people to be more agile, efficient, and resilient. Designed for specific business roles, Digital Employees can work with other systems and people, and learn on the job -- as their human counterparts do. As a result, they are far easier to train and deploy than other AI-based technologies like RPA. Employees find it intuitive to collaborate with and retrain Digital Employees as business needs change. Combined, all of these make it easier to establish value for specific use cases.
And, unlike RPA, Digital Employees are easy to use and deploy, making them the undiscovered gem in the hyperautomation schema.
Key questions to ask when evaluating automation technologies
- How quickly will we see results?
- Does implementation require a third party consultant, an extended service engagement, or specialized internal skills?
- Do our core users need to be very technically savvy to use this, or will they require extensive training?
- How do our users engage with the technology? Can they use existing communication tools like email and chat, or do they need to log into a separate platform?
- Please describe what we need to do to make changes when systems and processes change. Does the technology learn on the fly or do we need to remap?
This is directly in line with Gartner’s recommendation in its Move Beyond RPA to Deliver Hyperautomation report: “Enterprise architecture and technology innovation leaders lack a defined strategy to scale automation with tactical and strategic goals. They must deliver end-to-end automation beyond RPA by combining complementary technologies to augment business processes.”
SKAEL’s humanistic approach to automation pairs Digital Employees with humans so organizations can truly reimagine business functions and eliminate the drudge work to drive new levels of productivity and innovation.
If you are ready to see it for yourself, you can request a demo.