Hyperautomation definition

According to Gartner, Hyperautomation is a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible. Hyperautomation involves the orchestrated use of multiple technologies, tools or platforms, including: artificial intelligence (AI), machine learning, event-driven software architecture, robotic process automation (RPA), business process management (BPM) and intelligent business process management suites (iBPMS), integration platform as a service (iPaaS), low-code/no-code tools, packaged software, and other types of decision, process and task automation tools.

100 years of science

Frederick Winslow Taylor's Scientific Management, introduced in the early 1900s, laid the foundation for process optimization. William Edwards Deming's Total Quality Management (TQM), which gained prominence from the 1950s to the 1980s, focused on continuous improvement, statistical quality control, and customer satisfaction. Michael Martin Hammer's Business Process Reengineering (BPR), introduced in the early 1990s, advocated for radical redesign and fundamental rethinking of business processes to achieve significant improvements in performance. The 1990s saw the development of early workflow management systems, which allowed for the automation of specific tasks within business processes. The early 2000s marked the evolution of workflow systems into full-fledged Business Process Management Suites (BPMS). The establishment of standards like ISO 9001, BPMN, DMN, and BPEL further facilitated the creation of robust and standardized automation solutions, driving organizations toward greater efficiency and agility.

Hyperautomation has a similar approach to it's predecessor Business Process Management. But where BPM faded due to long implementation cycles, increase licences fees, costly implementations coupled with a demand for specialised skills which needs to be established and maintained in a CoE. Further disruption from mobile first, cloud adoption, CRM and ERP implementations paused or sometimes halted efforts to improve businesses using BPMS and SOA style architectures.  Furthermore technologies like RPA, Process Mining, Generative AI combined with Large Language models did not exist but now they do. Combining the new technologies with the more mature automation technologies gave birth to the Hyperautomation concept. 

Hyperautomation

Traditional technologies included

• ECM - Enterprise Content Management - Makes sure documents are stored, protected, published and managed through its life cycle from creation to destruction in a compliant way.
• DP - Document Processing - Used to capture, extract, validate, and classify documents and finally index documents.
• BPMS - Business Process Management Suites are comprehensive platforms designed to streamline and optimize organizational processes. They provide a range of tools and functionalities to model, automate, and manage business workflows, case creation and collaboration, improving efficiency, productivity, and decision-making.
• BRMS - Business Rules Management System is a software platform designed to manage and enforce business rules within an organization.

New technologies included

• RPA - Robotic Process Automation is a technology that uses software robots to automate repetitive, rule-based tasks, often performed by humans. These software robots can interact with applications, systems, and data to mimic human actions, freeing up employees to focus on more complex and strategic tasks.
• PM - Process Mining is a technique used to analyze and improve business processes by extracting information from event logs. It involves using data mining algorithms to discover, visualize, and understand the actual behavior of processes as they are executed in real-world environments.
• ML - Machine Learning is a subset of AI that focuses on algorithms that allow computers to learn from data and improve their performance over time. ML models are trained on large datasets to identify patterns and make predictions or decisions.
• NLP - Natural Language Processing is a field of artificial intelligence (AI) that focuses on the interaction between computers and human (natural) languages typically using a Large Language Model.

But adoption will be a challenge

Many organisations already have large investments in the technologies mentioned above. Typically each technology get implemented by a different team with some more mature and widely used than others. Additionally there are significant overlaps from the set of tools available and the team with the most success end up solving problems in a way that suits the specific technology they work with. For example an RPA tool has OCR capability originally found in DP tools and can therefore re-implement capabilities that have been fully matured in the DP team. The organisation might end up using the sub-optimal approach to solving the same business problem because technology teams end up competing with each other for same use cases to ensure their own existence. Without proper consideration of the way forward organisations will find themselves with an inefficient operating model and fragmented approach with potentially using the wrong tool to solve the problem in the best way. Another example might be that a business rule engine will easily allow the business to control the rules and policies supporting a process but an AI team can try and use very expensive machine learning models to derive the same business rules from the data but with less accuracy and higher cost. 

Suggested next steps

• With any major shift in the technology landscape it would wise to stop and assess where you are in terms of Hyperautomation maturity. 
• Make sure you understand the technologies you have already available in your environment.
• Rationalise and consolidate where possible as many software vendors have acquired additional technology components which are now integrated into a single platform. 
• Establish new FULL STACK skilled teams that can understand all of the technologies in Hyperauotmation and can advise the business correctly on which technique or technology solves the problem in the most efficient and cost effective manner.
• Use Process Mining to build your business case for change to ensure you understand the exact ROI based on real system information and not human assumptions. Measure twice cut once.
• Take advantage of the Generative AI features of the new solution to speed up implementation times and reduce complexity.
• Have fun learning.