Any process in an organization that can be automated should be automated. This is the idea behind hyperautomation, which has quickly become big business since the term was first coined in 2019. Gartner predicts the market for hyperautomation enablement tools and tech will reach nearly $860 billion by 2025, up 44% from $600 billion in 2022. Automation has evolved to reach an inflection point where people and technology, and especially artificial intelligence (AI), can solve increasingly complex and variable problems while maintaining a high degree of accuracy.
Hyperautomation fastracks growth by identifying, testing, and automating every business process possible. Separate Gartner research predicts that by combining hyperautomation with optimized processes, organizations will lower their operational costs by 30% over the next two years. In a world facing sustained supply chain uncertainty, inflationary pressures, and rising energy costs, hyperautomation offers valuable risk reduction and improves business resilience.
This article explains what hyperautomation is, why the hype behind it is justified, and how you can get started on your hyperautomation journey.
There’s no denying that hyperautomation sounds like another tech industry buzzword. However, there’s real value behind the sizzle. The reality is that proliferation of accessible AI technology is enabling intense acceleration in the arena of enterprise automation. Organizations are discovering the possibilities of combining these technologies to automate more complex processes and problems.
Hyperautomation describes the broader and deeper role automation is ready to play in the enterprise, the kind of automation that tangible digital transformation is made of. Harnessing intelligent automation at true enterprise scale is intended to create a positive snowball effect where people and automation tools (RPA, AI, ML, UDP) are unified toward the goal of organization-wide automation—including the automation itself.
At a practical level, hyperautomation means adopting intelligent automation as a 360-degree enterprise practice with the goal of automating everything that can be automated both broadly (across functions and at all levels) and deeply (end to end). That means hyperautomation isn’t about a specific software or application. By definition, “hyperautomation involves the orchestrated use of multiple technologies, tools or platforms.”
Where robotic or business process automation is about isolating individual processes for bot-assisted execution, hyperautomation is about tackling the entirety of complex organizational processes. Processes that could span the entire business, include immense variation, require decision-making, compliance and oversight, and need to happen faster than ever before to delight customers and contain costs.
Organizations that have successfully established an automation practice are in the best position to achieve hyperautomation. Deloitte research found that companies already working on implementation and seeking to scale automation efforts were much more likely to also reimagine processes across functions. Conversely, organizations still piloting automation programs remained stuck automating islands of status-quo processes.
What’s holding them back? In large part the barrier is that they have not begun taking advantage of the breadth of technologies and methods that can expand the scope of their automation vision and practice.
Automating nebulous processes across functions and business units is an opportunity to put AI to work—and embark on organizational soul searching. Up until hyperautomation, there was more clarity. Simple process in one department? Easy. Throw RPA at it. Have some variation in data or process steps? You’ve got intelligent automation platforms for that. For the wild west beyond, the message can seem very DIY: Stitch together your own patchwork of AI, ML, RPA, and other automation-enabling technologies.
From this technology point of view, while broad strokes about choosing the right suite of AI and automation tools are accurate because hyperautomation is a strategy not a technology, the lack of direction can be overwhelming.
Automating more complex and variable processes requires making sense of more complex and variable information. That’s why hyperautomation in fact needs to begin with data. Both complex business processes and the AI and ML models that support automating them rely on high-quality information. Data represents the foundation of all hyperautomation efforts. Intelligent Automation Network articulates the point well: “The importance of having access to high quality data before you begin your hyperautomation journey cannot be understated. In fact, it should be your guiding force from day one.”
Despite the indispensable nature of high-quality data, it is often assumed that data is ready to go and the barrier to hyperatomation is system integrations. For example, Mulesoft CEO Meir Amiel says, “To achieve hyperautomation success, organizations need to be able to bring together integration, API management, and automation technologies to connect all of their data and applications.”
While it's true that meshing systems and data is an essential piece of the hyperautomation puzzle, without first securing high-quality data no organization will achieve hyperautomation success. Sourcing data for hyperautomation requires finding and manipulating information across the vast stores of business data that are unstructured, including emails, chat conversations, customer service calls, Internet of Things (IoT) sensor data, and more.
There is a vast and growing ocean of data out there. IDC predicts that 80% of global data will be unstructured by 2025. And with terms like “Big data” and “Dark data” it’s no wonder people shy away from looking under the automation bed to shine the light on the data that supports it. But there really isn’t anything to be afraid of.
Data is categorized by how structured it is in raw form, from highly structured to fully unstructured. More structure means the data is more easily exported/extracted, organized, and stored, and used. Structured data is essential for automating business processes. For example, automating customer onboarding requires data from unstructured sources such as applications or contracts. Without structuring that data, there is no path to achieving end-to-end automation.
Before organizations can achieve hyperautomation, they need to solve for unstructured data processing. No-code AI for unstructured data processing aligns with self-service tools, such as RPA and intelligent automation platforms, that make up the hyperautomation toolkit. Application and integration agnostic, super.AI is leading the pack on speed and quality by applying an AI assembly line model that combines human labelling with AI processing, enabling guaranteed data output accuracy.
At super.AI, our mission is to make artificial intelligence accessible to everyone and automate repetitive work so that people can be more human. We take this approach in everything we do, and strive to create useful resources that empower people to learn about and leverage AI. For additional information on hyperautomation and AI in general, check out the following resources: