What is hyperautomation?

Hyperautomation is one of those IT buzzwords that gets everyone excited and confused in equal measure. Any businessperson could have a stab at what it means. Fast and broad automation across the enterprise would be a good guess. In this article we ask the question, is it hyperautomation–or hyperbole?

In most organizations, less than one-third of applications are fully automated, and over 60% of IT investments go into keeping the lights on. 

One of the more thorough research studies into IT innovation habits found that IT leaders in 90% of organizations feel they spend too much on keeping the lights on (i.e. system maintenance, upgrades, migrations, etc.) but have few ideas on how to deliver IT differently to reduce spend on critical infrastructure.

“If you don’t adopt digital process automation you die.”

This was the battle cry of a famous UK management consultant (our Peter Drucker as it were) Sir John Harvey Jones. His underpinning rationale was that no business process should be left untouched, as competitors would always seek to gain a lead against you in the market by innovating themselves. ‘Fail to innovate your business operations (he would say) …and your business is peddling backward.’

This presupposes that business organizations run teams of people focused on process discovery and process automation initiatives. That isn’t the case. Most organizations are lucky to have a single full-time business improvement officer. Most will ask senior officers to add this to their operational tasks, which is the road to mediocrity, but it is nevertheless the commercial reality.

Automated processes save knowledge workers hours of work on repetitive tasks, they reduce the time it takes to serve customers, thereby improving customer experiences, and often they can save money by totally removing processes (or process steps) by doing better things, instead of doing things better.

With digital process automation, your business can improve the customer experience of your business while optimizing operating costs.   The focus of automation once centered around a headlong drive to save money by removing the human from the loop. Digital transformation priorities have reset that balance directing more effort towards projects that bring direct value to customers and their experiences.

Department heads are driving technology and process innovation

Prior to the millennium, it was the IT department that largely was responsible for automation efforts and making decisions on behalf of all departments when it came to information technology priorities and product selections. 

Software-as-a-Service (SaaS) cloud computing technology can automate almost any repetitive task performed in business by humans tapping into computers. Many of these applications, which have superseded packaged software applications used to automate tasks or process workflows (the sort you used to install from a CD). This consumerization of applications used to complete tasks has empowered department leaders to solve their own problems with third-party off-the-shelf software solutions.

Find your digital workforce

Process mining is the foundational step in process improvement

Undocumented processes are the scourge of business process automation initiatives.

To automate your business processes, you first need to discover them, document existing workflows, and how they work. Through a process mining initiative, you will need to identify what those processes are, why they exist–and surface the stakeholders responsible for carrying them out.

Few organizations today are able to describe their business-critical processes. Those that can normally have to present a tome written by the quality control folk to satisfy regulatory compliance obligations.

It stands to reason that you can’t efficiently upgrade manual processes without first having knowledge of these whats and whys.

…so, what is hyperautomation?

Hyperautomation is the name given to a collection of process automation technologies that have surfaced in the last decade. It describes the ability of firms to outpace competitors by harnessing the most appropriate digital technologies to streamline processes, faster – applying enterprise platforms that reach the market faster, at lower risk and cost, and that adapt faster to change.

The term hyperautomation began to circulate in 2021 and, since then, awareness of the term has been gathering speed as Low Code and No Code hyperautomation software platforms like Appian, Mendix, ServiceNow, Encanvas, and BettyBlocks have risen in popularity and championed its cause.

Modern digital businesses are automating business processes at scale. They want to harness new digital technologies to improve their automation capabilities.

Business areas that benefit from hyperautomation include customer experience, supply chain management, servicing knowledge workers with productivity apps, employee performance improvements, automated billing cycles, CRM and ERP systems improvements, processing invoices, and much more besides.

Tech building blocks found in hyperautomation

Various automation technologies for automation exist today thanks to investments from large software industry vendors (think Microsoft, Google, SAP, Amazon, Salesforce, Meta Group, etc.) to cash in on the strategic technology trend of digital transformation. These are some of the more notables ones.

Process discovery and process mining tools

The starting point for any automation process is to identify automation opportunities and smarter ways to complete tasks.

You could perform this exercise with a clever analyst and a spreadsheet, but it makes sense to leverage process mining tools to speed up the activity and identify data resources associated with process workflows.

An additional step should be to assess the impact of change and come up with a prioritization plan for improvements so that you deliver a return on investment and cashable savings as quickly as possible. Failing to deliver benefits to stakeholders quickly can result in projects running out of steam.

Natural language processing technologies

A growing agenda exists in organizations to harness natural language analytics and natural language generation technologies in their business processes.

Artificial intelligence is transforming the ability of organizations to use chatbots to engage with customers and other stakeholders to serve them and answer questions. Automations come in three forms:

(1) Capture technologies to interpret speech and convert it into text; a data format that computers and humans can read it. For example, the bot technology company intnt.ai uses AI tooling to program and train bots to become more useful.

(2) Engagement technologies that make it possible to scale customer and stakeholder engagement and run operations 24/7 across all customer engagement touchpoints.

(3) Generational technologies that take video and text inputs to produce artificial intelligence-driven avatars that communicate information in a very human, interactive form.

Robotic process automation RPA

There are no limits to the tedious tasks of digital workers. This is the problem that Robotic Process Automation was born to solve.

Whether it’s gathering data from disparate IT systems that don’t talk to one another using a spreadsheet, performing repetitive behaviors like providing set responses to messages, or collecting and distributing reports–the human workforce is always the lowest common denominator of automation.

Removing the human in the loop can be a challenge when the cost of fully automating data transfers and installing logic to automate repetitive tasks potentially outweigh the return.

Robotic Process Automation is a blend of easily deployed automation building blocks–like screen scrapers, if/then logic trees, etc.–that empower data analysts to create micro-task rapidly.

Platform solutions

With IT resources in short supply, departmental use of RPA has grown in popularity over the past decade because there is so much demand for automation and it’s easier to install automation without involving deep IT and process changes. Nevertheless, it is an expensive and primitive technology.

A better, more secure, more robust, and ultimately more economic solution is a machine-to-machine interface that removes the need for Robotic Process Automation bots and humans completely. This has led to the design of integration platform solutions. This tech stack is about providing the glueware needed to combine data from disparate systems and forge the links in machine-to-machine processes without coding or scripting. Glueware delivers seamless integrations between disparate and decentralized software solutions and systems with related resources. Glueware enables the integrated operations of different systems, regardless of their developer/vendor, version or type. Examples include SnapLogic, GlueWare, and Encanvas Glueware.

Intelligent business process management with machine learning (ML) and AI

Much has been written about AI and machine learning. While machine learning is about using computers to run a sequence of ‘if/then/that/or’ type statements to act on data, artificial intelligence ai moves automation into the cognitive automation space, where machines can start making decisions on our behalf.

Introducing artificial intelligence and machine learning into your improvement agenda brings the ‘hyper’ into ‘automation.’

Centralized data and unstructured data management

All businesses rely on structured and unstructured data these days to operate. Data science turns businesses into digital businesses. There are three noteworthy elements to think about:

1. Data input – finding simpler/better ways to capture data without requiring humans to do it manually
2. Pull data – To harvest data from disparate internal data silos and external third-party computer systems. Often the technology used to do this is described as Extract, Transform, and Load (or ‘ETL’) technology.
3. Analyze data – Once the domain of the human and humble spreadsheet, now you can leave it to computers to identify patterns in data and surface events and behaviors, presented through data visualizations that turn data into insights, insights into actionable data to drive operational and strategic decisions.

Repetitive processes exist in data science too. Artificial intelligence and glueware can combine to remove the tedious tasks that exist in data input, pulling data, and analyzing it.

Optical character recognition

Optical Character Recognition (or ‘OCR’) is a technology that’s been around for two decades, but it’s still worthy of a mention. Before computers, documents carried the load of capturing, managing, communicating, and archival tasks. Even today, documents play a key role in bridging between different companies and processes.

OCR technologies take scanned images and convert text into optical characters that can be read by computers. Introducing intelligent document processing into your business makes data held in documents readable (and storable by computers). This saves on storage costs and means you can automate processes that previously required humans to manage and act on documents.

Hyperautomation platform solutions

If you are looking to install and event driven software architecture to scale automation initiatives, you will need to invest in a lot of different tools, or at least a significant comprehensive automation platform that can cover lots of these underpinning technological capabilities.

Hyperautomation requires a rich blend of automation tools, along with technical and organizational change competencies. Combining hyperautomation technologies produces the best results, which is why ‘data fabric and application platform suite providers’ like Appian, Mendix, ServiceNow and Encanvas (along with the mainstream Tech sector players I mentioned above) support so many of these core building blocks in their offerings.

It’s time to take the hype out of hyperautomation technologies

What is hyperautomation technology good for?

As I’ve described in this document, hyperautomation is not one kind of technology. The term represents multiple technologies that combine in clever ways to deliver intelligent automation solutions. But it isn’t hype. It genuinely is a subject any business leader should take seriously.

Your business processes should not be seen as something that exists, and that never needs to change. There will always be new processes to implement and old processes to re-examine. Neither should they be tinkered with without clear direction.

Your hyperautomation agenda should progressively examine the key performance indicators that drive its success and surface projects to improve the processes it relies on to create customer value and grow profits for shareholders.

When taken seriously as a long-term capability, the strategic deployment of intelligent automation and process automation technology can help your business to steal a march on its competitors and produce above and beyond customer experiences.

Finding a partner for hyperautomation

Every organization needs an event driven software architecture and to be a data-driven business in the 2020s to survive. Find the right partner and they will help you to install intelligent process automation and leapfrog quick and dirty solutions like RPA. You might even find they supply the automation tools and advanced technologies you need.