Navigating Infinite Change

Digital Asset #4 - Hyper-Automation

To achieve the level of creativity, agility and resilience required in an innovation-ready enterprise, organizations must remember a golden rule: To automate everything that they do more than once. Humans should only intervene to solve problems that cannot be expressed in mathematical formula – those requiring creativity, empathy, compassion and/or a physical presence.

Slated to become a $40bn market by 2030, hyper-automation is a business-driven approach to identify, vet, and automate as many business and IT processes as possible. It requires the orchestrated use of multiple technology tools and platforms, including Robotic Process Automation (RPA), low code development and process mining tools.

Over the last decade, automation within IT and across organizations has been focused on islands of task automation, delivering limited efficiency gains. Dreams of industry-wide transformations and evolutions from value-chain to value-network have largely failed to materialize. Recent advances in AI, however, means these dreams are now more feasible. New, transformational applications offer significant efficiency gains to create decisive competitive advantages for organizations.


While the low hanging fruits of increased efficiency are attractive for many teams, the real value in automation might be found elsewhere. We have already established how important organizational adaptability is in today’s volatile, fast-changing world. By automating most of a company’s IT and business processes, these processes become much easier to adapt – in some cases instantly – in response to marketplace change. When designed and implemented correctly, automation also makes it easier to adopt new and emerging technologies. Freeing up employees from routine tasks enables them to focus on change, innovation, experimentation and inventing new, differentiated, high-value services.

In this context, it’s wise to consider not only the required speed of change, but also the acceleration of change. Business processes that are perceived as fast today will become normal tomorrow, and be considered slow the day after. For this reason, automation is focused on increasing speed, where hyper-automation is focused at increasing acceleration.

Target Technologies

Discovering, mapping, governance, compliance, monitoring and auditing are all key constructs of hyper-automation. In recent years, the technology stack underpinning hyper-automation has become more refined and purpose-built.

  • In automatic process discovery, a bot observes human activity and determines the most feasible paths for automating a given process. In some cases, the bot also writes code to automate the task.
  • AI fabric provides a library of components to translate unstructured data into structured payloads for automation. Tools include Optical/Intelligent Character Recognition (OCR/ICR), Natural Language Understanding (NLU) and dimensional reduction techniques to support users to streamline activities.
  • Software robots are traditional RPA tools that offer low code and no code methods for automating repetitive and rule-based tasks.
  • Monitoring and self-healing systems are sophisticated bot management tools to detect incidents that impede automation and to prompt users to remediate those issues via human intervention.
  • AI/ML models identify processing problems and suggest new operating models to drive radical new efficiencies in processing work items.

In future, the field of hyper-automation will advance even further via NoOps approaches and autonomous, self-maintaining, and self-healing systems.

Testing & Validation

Continuous test automation and test automation optimization must be a priority for all organizations using hyper-automation. The sheer volume of RPA deployments, changes and enhancements, and increased complexity and interdependency of today’s application landscapes mean tests must be automated as often as possible.

While test automation previously focused on automating the execution of tests, it has since matured into end-to-end quality automation. This encompasses all aspects of testing, from the production of test scenarios and data through to the automated execution of functional and performance testing, as well as automated quality reporting and monitoring. Test automation is integrated in the CI/CD process to achieve so-called ‘continuous’ test automation practices.


As a result of these changes, test tools have evolved from complex scripting tools to simple-to-use, codeless testing technologies for non-technical testers and business analysts. Today, we’re seeing the introduction of intelligent test automation that will enable additional breakthroughs in this area – including self-healing and self-adaptive test automation, and the advanced creation of test scenarios, test data and software-defined test environments.

Despite these developments, however, many organizations continue to fall short of required levels of test automation. In addition to developing test automation technologies, we've therefore discovered three other essential aspects to consider when calculating ROI from quality and test automation:

  • Establishing a quality automation strategy, including a decision mechanism for what to automate and which tools to use
  • Implementing a test automation platform with a support squad for individual feature teams
  • Standardizing test data and environment provisioning