Feature
In late 2019, in a paper titled Top 10 Strategic Technology Trends for 2020, Gartner coined the term hyperautomation, defining it as “the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans.”
At the time, the research firm predicted that hyperautomation, along with other emerging technologies, would "drive significant disruption and opportunity over the next five to 10 years."
Four years later, the rapid pace of technological progress has already significantly transformed the landscape and digital workplace. The technologies identified in the paper have evolved to such a point that generative AI, for example, can now create or fine-tune automated processes without human intervention.
A look into those developments and what they bring to the workplace.
What Can Hyperautomation Do?
The paper was right: still today, individual technologies on their own cannot scale any process up. This reality explains the need for hyperautomation, where several technologies are pulled together in a framework for the strategic deployment of various automation technologies separately or in tandem, augmented by AI and ML.
In simpler terms, Jason Burian, VP of product at KnowledgeLake, said hyperautomation is bigger, faster and more efficient than regular automation.
“What distinguishes hyperautomation from regular automation is the addition of new technologies, such as RPA, machine learning, AI and low-code application platforms (LCAP),” he said.
From a technical point of view, these tools can be used for regular automation. However, organizations become hyperautomated when they combine these advanced technologies to automate everything holistically.
AI, for example, can be used to analyze data, gain a deeper understanding of all the steps within a workflow and enable continuous improvement. Each newly improved automated process acts as a force multiplier with compounding effects that lead to even faster, more efficient automation.
Burian also pointed out that while traditional automation has focused on automating tasks between people, the goal of hyperautomation is to automate as much as possible within an organization and only involve humans in the workflow loop when necessary.
“When done correctly, hyperautomation not only increases efficiency and productivity, but it can also enhance the employee experience by removing friction within the workplace and allowing people to focus on creative work and decision-making,” he said.
Related Article: So Long Automation, Hello Hyperautomation
Should You Automate Everything?
This may sound like the epitome of an efficient organization, but Alan Pelz-Sharpe, founder of Deep Analysis, an advisory firm focused on disruption and innovation in information management, said the term hyperautomation is misleading, at best, because it suggests organizations should aim to automate everything in sight to achieve the most efficiencies.
He argues against doing that.
“Tech can do only so much, and we have a long history of automating things that should not have been automated," he said, noting that 70% of IT projects fail or fall short of expectations because of that. “Such terms give fuel to the practice of over simplifying complex business problems."
At Deep Analysis, the team has spoken to dozens of automation vendors — including RPA, AI and BPA vendors — over the past few years, and Pelz-Sharpe said not one said that the term resonates well with buyers.
“It sounds catchy in marketing but does not help and can hinder sales," he said. "Enterprises are typically cautious and aware that tech is not going to resolve all their problems as the name suggests it will."
The emergence of task and process mining add weight to this. These have been gaining a lot of traction in recent years and almost always reveal far more complexity beneath the covers than expected.
What seems on the surface to be relatively simple to automate, such as transactional tasks believed to be ripe for automation, once mined turn out to be far messier and more difficult to automate than expected, Pelz-Sharpe said. “In many cases they reveal that some aspects of a task or process can and maybe should be automated, there is much more that needs further analysis, human involvement and potentially change management initiatives to streamline."
Furthermore, projects often get put on hold once the complexity is revealed, as what was thought to be a simple automation project proves to be much more and potentially not worth the investment to resolve.
Pelz-Sharpe said the complexity that tends to arise with automation projects takes place in two key areas:
1. Legacy technology — or the sheer complexity of the existing IT stack. It is not uncommon for larger organizations to have thousands, if not tens of thousands of nodes (silos, apps etc), he said. This legacy infrastructure of hardware and software has typically developed organically over decades, making unravelling it near impossible.
2. Underestimating complexity. Management tends to massively underestimate the complexity of the work that humans undertake, Pelz-Sharpe said. What appears to be a simple job updating patient records or managing a financial transaction, when looked at from the top, is typically far more nuanced with a myriad of seemingly minor but nonetheless critical components.
He said picking the right projects for automation remains critical to making the technology work for organizations.
“Although RPA isn’t getting the headlines now, its day in the sun has gone. It remains one of the most effective and widely used toolsets,” he said. “The reason being, assuming it’s used correctly, RPA is only used to automate repetitive tasks. In other words, tasks that are done the same way, every single time.”
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Adaptive Technology Becomes Key
Progress is another element to keep in mind when considering new technologies. Things are evolving excessively fast, and what's hot today may become obsolete tomorrow.
Tony Lee, CTO at Hyperscience, said hyperautomation and tools like RPA and process mining, for instance, may have historically been good investments, but they are no longer enough in the age of AI.
Hyperautomated tech sits on top of existing data and workflow processes and, once trained by humans, can complete the same task repeatedly. However, it is extremely limited, as it can only focus on and fulfill the singular task it is trained for, he said.
“RPA approaches to automation also do not allow the business user to step back and look at the process holistically, to redesign it in the context of what AI can do today. By focusing on the replacement of individual steps, it’s more difficult to see the forest for the trees," said Lee.
He added that as organizations continue to prioritize operational efficiency to survive amid today’s macroeconomic climate and continued talent shortages, hyperautomation just won't cut it.
Organizations who are no longer satisfied with automation are looking to level up their tech stack from hyperautomated products to AI-based enterprise architecture solutions, which promise productivity gains unseen by hyperautomation.
“The market is only beginning to unlock AI’s benefits, and as training data improves and the technology evolves, organizations will further invest in technology that can continually learn and adapt to changing variables,” he said. “This will result in vendors focused solely on legacy technology such as RPA, [moving] out of the market [and] opening the door for ‘born AI’ companies to emerge more frequently.”
Related Article: How Generative AI Will Level Up Business Process Management
Humans Still Needed
Companies seeking enterprise workflow automation should understand that the process to get there can be challenging — and will continue to require humans.
Josh Russ, co-founder and COO of 8flow.ai, said there are three main phases to keep in mind to undergo such a transformation:
- Discovery: Identifying processes and workflows
- Analysis: Analyzing these workflows and identifying the steps that can be automated
- Execution: Building and implementing the automations
Current workflow automation strategies will work in some scenarios but don't scale. The reason is that in an enterprise, there could be millions of workflows with different nuances across many systems and embedded team knowledge of how they get done. Solving for that scale is incredibly difficult and still manual today.
In phase 1, you need a tool that can gather all the data about individual workflows. Russ said many tools screen record or monitor the computer to gather this data. The larger the company or the more complex the workflow, the greater the amount of data and events needed to move to the next phase.
Lee notes that because the tools available today focus on tracking data for humans to analyze, instead of creating metadata for models to utilize, when you reach phase 2, mapping out and analyzing the workflows identified in the first step must be done manually. There are tools that can identify repetitive steps, he said, but it will take a business analyst and subject matter experts to map and document the desired workflow per use-case. “Scaling this desired workflow to include every nuanced decision a human could make in a workflow is near impossible."
In phase 3, you need a product to build and serve the automations to users. There are many tools for this, Lee said, but the manual effort required to get from phase 2 to phase 3 is significant. Once built, the automations are often brittle or quickly become irrelevant because processes change and evolve.
“With hyperautomation, there is a great deal of excitement that AI and ML can be applied to the three phases of automation to make the end-to-end process less manual,” he said. “But the reality is, even products that have solutions for phases 1, 2 and 3 will still require human intervention to move between phases. All nuances are generated as the employee continues to work and workflows.”