Hyperautomation

Hyperautomation refers to the advanced automation of IT platforms within an organization to enhance the speed and accuracy of work processes.

How is hyperautomation used?

Unlike small-scale automation that completes repetitive tasks without manual interventions, hyperautomation is a large-scale machine learning initiative with the use of multiple automation tools that, together, should be able to exchange, decipher, and implement new intelligent automation.

The ultimate goal is to improve employee productivity by freeing knowledge workers from mundane, trivial, and time-intensive tasks.

The creation of a hyperautomation model largely requires machine learning and robotic process automation (RPA). However, depending on the characteristics of the process, it could also require the following tools:

  • Devices that can identify and compile tasks worth automating: These can also include automation development apparatuses that include RPAs and integrated platforms to determine and delegate workload automation together with business process management which facilitate the adaptation and reuse of automation. 
  • Artificial and machine learning: These common tools extend the machine’s essential qualities to include natural language processing (NLP), chatbots/virtual agents, optical character recognition, and machine vision.
  • Digital worker analytics: This focuses on evaluating the performance and technique of the machine in conjunction with leveraging the cost of developing and maintaining a specific tool against the value derived.

Implementing hyperautomation

The first step is to determine if the organization would benefit from this process. Identify all the fundamental processes of the company that can be automated and budget for completion. This would be the time to apply the digital worker analytics or digital twinning, which is a model of how the process would function within the organization that highlights areas for improvement.

Next, the technologies used in automating an organization should be compatible with the platform. Using tools that are similar in operability also ensures that the bots are functioning at the level of the task. The amount of data that a process might require fluctuates and differs depending on the intended use, so there needs to be a strategy in place.

Lastly, collect data. Automations are only as good as the data fed into their algorithm. Hyperautomation models should be preemptive in making correct predictions, and there must be a constant flow of data to all or most sections of the automation. This allows for clear communications and the team can predict outcomes based on the technologies of the model. Only after these steps can you implement the automation.

Benefits of hyperautomation

When perfected, hyperautomation reduces operation costs and improves service and efficiency within an organization. For one, automating manual and repetitive tasks means knowledge workers can dedicate more time to improving a company’s product, which in turn increases customer satisfaction.

There is also an increase in the prioritization of future automation, furnished by the application of big data and artificial intelligence in extracting information from raw data and making effective decisions.

In addition, the use of hyperautomation can streamline interactions between customer, business, and industry through use of emerging technologies like such as blockchain, machine learning, and artificial intelligence, ultimately changing the way business decisions are made.

Challenges of hyperautomation

However, because hyperautomation is not yet perfect, there are a few challenges to examine. The most fundamental being the tools and software used to develop the automation.

The absence of comprehensive data can cause lapses in the automation’s ability to function and scale. The data should be compiled based on quality, style, and operability, and the strategy used in implementing the automation should be thoroughly vetted.

In many instances, companies lack the resources to implement hyperautomation in its entirety, creating lapses that are managed by manual intervention. Also, in instances where hyperautomation is widespread and scaled throughout an organization, the security must be tight, and routines for determining vulnerable positions and guarding them accordingly must be developed.

Although, hyperautomation can lead to rapid evolution in the marketplace of products and possibly create a supersaturation of merchandise. If so, the market could easily devolve into fierce mergers and acquisitions amongst vendors to narrow the redundancy of product offerings. 

Finally, the general public is wary of hyperautomation. It poses a challenge to those whose jobs can be automated, and as a result, public buy-in is hesitant.  

Current uses of hyperautomation

So far, hyperautomation has been implemented in healthcare, supply chains, banking and finance, and retail. The majority of communication, online applications, billing and inventory, staff scheduling, and supply management are managed by automated processes.

Hyperautomtion is also being used in targeted marketing. For example, in banking and finance, its application is used to expedite the accounts-payable process and the submission and approval of expense reports.

Yemisi Awobode
Yemisi Awobode
Yemisi Awobode is a contributing writer for Webopedia, covering tech topics including computer science and machine learning.

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