Hyperautomation is next big thing after robotic process automation which was a powerful technology tool that companies have had their focus on. Now, it has moved from task-based Automation to process-based Automation. The magnitude of Automation is set to go even higher as Hyperautomation increases in the coming year.
And with good cause, as the selection of Automation is quickly increasing, the technologies and tools possible are simultaneously developing and improving, arranging the stage for a completely new era of Automation. Finally, it arrives at its potential to optimize business processes and improve employees’ lives even more significantly.
First, a definition of hyper-automation is in place. So hold on to your hats while we hurl through some technical terminology.
Hyperautomation combines multiple machine learning, packaged software, and automation tools to deliver work. It applies advanced technologies, including AI and machine education, to automate processes and augment humans increasingly.
Hyperautomation is an extension of Automation in both width and depth. It is like going from thinking of Automation as ‘simply’ RPA and task automation to thinking of Automation as highly sophisticated, AI-based process automation to the level that organizations are building ‘digital twins.’
To put it simply, hyper-automation is the mix of automation technologies and artificial intelligence that, when combined, augment humans’ capabilities, allowing them to complete processes faster, more efficiently, and with fewer errors.
Hyperautomation differs from Automation
Automation can easily optimize task processes; hyper-automation has an additional layer of robotic ‘intelligence’ that executes the strategies even brighter. You could assume that where Automation controls a robot’s arms to perform tasks quicker and with fewer errors, hyper-automation also uses the robot’s brain to perform those tasks more innovatively. This additional layer of intelligence can incorporate AI technologies in different forms. For example:
- Machine learning (ML) allows bots to identify patterns in data
- Optical character recognition (OCR), which enables bots to convert images to readable text
- Natural language processing (NLP), which allows bots to understand human speech
When coupled with automation software, these technologies dramatically increase automation opportunities.
The following inevitable questions are how the hyper-automation trend will affect your job or business and what you can achieve if you gear your business with hyper-automation technology today. These are issues that we require to answer to explain the potential return on funding in hyper-automation.
Here, it’s necessary to, first of all, organize that the point of Automation is to expand human capabilities, not to replace them. Hyperautomation should, in that sense, not be viewed as a warning to the individual employee. Businesses who have previously funded in Automation will probably know the advantages of optimizing tasks and methods through robots.
Benefits of Hyperautomation
- Lowers the cost of Automation
- Improves alignment between IT and business
- Reduces the need for shadow IT, which heightens safety and governance
- Improves the adoption of AI and machine discovery into business manners
- Enhances the ability to measure the impact of digital transformation efforts
- Helps prioritize future automation efforts
As enterprises master hyper-automation, there are many ways this discipline could be used to improve business operations.
A company could apply RPA and machine learning to produce reports and pull data from social platforms to manage customer sentiment in social media and customer retention. Then, a process could emerge to make that information readily available to the marketing team, creating real-time, targeted customer campaigns.
Suppose an enterprise launches a product quickly and digital process automation metrics show strong customer demand for it. In that case, the outcome could be immediately scaled to support the company grow its revenue. Conversely, if advanced analysis reveals that the product fails to obtain traction among customers, it could reduce losses by withdrawing it immediately.
Challenges of Hyperautomation
Hyperautomation is a new idea, and enterprises are early in making it work in practice. Some of the most significant difficulties include the following:
- Choosing a CoE strategy for the organization. Some organizations may do great with a more centralized approach, while others will see more reliable results with a federated or shared approach to effecting large-scale initiatives.
- Tools. There is no silver bullet in hyper-automation software. Although leading automation vendors are expanding their hyper-automation capabilities, enterprises will struggle to ensure interoperability and integration across these tools.
- Security and Governance. Hyperautomation initiatives can benefit from in-depth monitoring and analysis of business processes that span multiple departments, services, and even country boundaries. However, it can combine a host of new security and retirement issues. In addition, enterprises require to acquire the appropriate guardrails for vetting the safety vulnerabilities of automatic apps.
- Half-grown Metrics. The instruments for evaluating the cost and possible value of Automation are still in their infancy.
- Manual Augmentation Required. Only about 45-55% of the code for Automation could be automatically produced using existing tools. Thus, a lot of old-fashioned effort is still required and needs to be budgeted when building robust Automation at order.
- Getting Human Buy-in. Most automation merchants are driving the narrative that hyper-automation will augment rather than substitute humans, but the reality is that Automation reduces jobs once done by humans. Thus, workers must be convinced that the robots will not take their careers for these efforts to take off. Also, the various monitoring tools used in hyper-automation projects might prompt a backlash from knowledge workers worried about the potential misuse of that data.
Vendors developing their automation collections include the accompanying:
- Microsoft has been gradually growing its hyper-automation capabilities with its Power Automate line of RPA tools and Process Advisor for process mining.
- Kryon was one of the first intelligent automation tool vendors to consolidate process discovery directly into its tools.
- Blue Prism has been expanding its internal process mining capabilities and has announced a partnership with Celonis.
- Celonis, the leading process mining vendor, recently bought Integromat to increase its automation capabilities.
- UiPath acquired Process Gold and StepShot to grow up its process mining capabilities.
- ABBYY has been a principal OCR vendor and has gradually increased its portfolio of tools to promote various intelligent process automation capabilities. It lately introduced different process mining capacities to develop its hyper-automation tooling.
- Automation Anywhere should be increasing its process mining and task mining inclinations for automatically creating bots.