The pressure to evolve from selective automation to true end-to-end automation continues to grow, driven by business leaders as well as bottom-up business pressures. This article explores the technologies and approaches that support rapid automation at scale and offers best practices to help organizations scale automation sustainably and accelerate the path to digital transformation. 

The term "hyperautomation" was originally coined by Gartner to describe an approach that aligns all organizational systems and processes to enable organizations to achieve automation at scale.  

Hyperautomation is a holistic and disciplined approach to the identification, prioritization, evaluation, and deployment of relevant automation technologies. 

This accelerated path to digitization can bring together a wide array of technologies including process mining (which turns event data into insights and actions), unstructured and structured data ingestion, digital twins (virtual recreations of a physical object or process), iBPMS (intelligent business process management solutions), LCAPs (low-code automation platforms), RPA (robotic process automation), and iPaaS (cloud services that integrate on-premise and cloud-based processes, services, applications and data). 

Hyperautomation is an approach whose time has come. As businesses wake up to the speed with which they need to automate processes, utilize structured data, and digitize sources of unstructured data, hyperautomation is increasingly part of the conversation. Over 80% of organizations consistently self-reported increased or continued investment in hyperautomation initiatives in 2021, and 56% reported having an average of four or more concurrent hyperautomation initiatives underway (Gartner, Top Strategic Technology Trends for 2022: Hyperautomation, 2021). 

And the trend continues to accelerate: 80% of executives expect to increase spending on digital business initiatives in 2022, 65% are increasing the pace of their digital business, and 72% will shorten timelines for implementing digital business initiatives. 

Faster adoption of hyperautomation initiatives will also be driven by the rapidly increasing availability and affordability of automation technology. By mid-2021, the combined venture capital and private equity investment in the new hyperautomation categories of RPA, process mining, process discovery, NLP, and virtual assistants had reached $500 million, and the size of the market opportunity worldwide had reached $440 billion (Gartner, Emerging Technologies: Research Roundup on the Hyperautomation Opportunities Ahead, 2021). The accessibility of these technologies, coupled with the clear business need, is likely to facilitate greater interest in hyperautomation. 

Companies are being asked to re-examine or even disrupt the business model in order to stay competitive and maximize revenue and profitability, and the forces accelerating automation come from both the top and the bottom.  

From the top down, company leadership is placing new pressure on business lines and operations to enhance performance. In 2021, 50% of CEOs and 69% of boards of directors were demanding accelerated growth and operational excellence, objectives that are best served by hyperautomation initiatives.  

From the bottom up, employees are desperate to find better ways of collecting and analyzing the mounting volumes of structured and unstructured data so that they can make timely, informed business decisions. Today, 65% of business decisions are more complex than they were two years ago.  

Hyperautomation is crucial to the task of capturing and consolidating content generated by chatbot exchanges, voice calls, emails, IoT sensor data, and other digital sources, as well as implementing electronic data interchange (EDI) initiatives that digitize and structure data from traditionally paper-based sources such as purchase orders, invoices, and contracts. 

There's no question that hyperautomation has a measurable impact on operational efficiency; it’s been projected that by 2024, organizations will reduce operational costs 30% by scaling automation and redesigning operational processes. 

But the true value of hyperautomation goes beyond protecting margins. Hyperautomation provides the ability to:

• Rapidly collect, consolidate, and analyze the full range of structured and unstructured business data
• Outpace the competition by bringing a product, service, or experience to market sooner
• Optimize ROI from investment in automation by putting business technologists in the driver's seat
• Create products with long-term retention and quick adoption because of the customer-centric focus, improving technology usage and value

Hyperautomation isn't about pushing automation initiatives through existing processes faster. It's about finding new ways of addressing the challenges of automating business processes at scale, including improving and accelerating decision-making, enhancing coordination, and adequately staffing a higher volume of initiatives with the right talent. To lay the groundwork for success, incorporate these best practices into the approach.  

Instead of starting with the technology, start by defining the use case and then determining what type of technology is the best fit. For example, a business that needs to spin up more engaging, intuitive UXs for customers or employees will want to explore low-code technologies, whereas a company focused on decision automation will want to look at decision management suites (DMS).   

Once you've identified the technology type, you'll want to apply a series of decision factors to vendor selection, including:  

• Time to market. How soon do you need this capability in place? Will you sacrifice cost and quality for speed?
• Total cost of ownership. Can you justify the cost based on ROI?
• Skill requirements. Does your organization have people with the skills needed to design, develop, deploy, and maintain the technology?
• Vendor maturity. Does a best-of-breed solution or a solution bundle on a single platform fit your organization better?

This gives all technology leaders a common point of reference that enables them to understand the role their efforts play in fulfilling the broader organizational mandate to hyperautomate. This type of map can also be helpful in identifying technological redundancies and incompatibilities that can arise with multiple concurrent hyperautomation initiatives. 

Finally, team members need to be recognized by the organization for their work and its impact on broader business objectives.