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Automation Maturity Model: Robotic, Intelligent, and HyperAutomation

Automation Maturity Model:  Robotic, Intelligent, and HyperAutomation

In a world obsessed with doing more with less, automation is a high priority topic. In particular, Process Automation has piqued the interest of nearly every industry. While the concept of leveraging automation software to realize efficiencies in daily operational tasks is not new, the past few years have yielded an unprecedented amount of investment in the technology. The investment in automation technologies can directly impact the bottom line by cutting human capital costs and reducing unintended human errors. Companies are able to obtain a competitive edge by minimizing low-value added manual tasks and standardizing repeatable processes. As automation across the organization matures, companies soon realize  reduced labor costs, streamlined processes, and higher productivity.

The journey to enterprise wide automation for most companies begins with a combination of existing manual processes and native automation applications, such as Excel which enables automated data analysis but is not compatible with other systems and requires human interaction to be value adding. From there, companies follow a progression from basic automation to intelligent automation until finally achieving hyperautomation. 

Basic Automation: Robotic Process Automation (RPA)

Basic Automation is the simplest form of automation technology solutions that addresses simple manual and repetitive tasks. Robotic Process Automation (RPA) configures software, sometimes referred to as “RPA bots”, to automate high volume, rules based tasks by mimicking human behavior. To maximize the impact of RPA, the development of bots should be paired with Business Process Management which focuses on simplifying end-to-end business processes. For organizations early in their process automation journey, Apexon delivers this powerful combination by documenting and evaluating the current state, conducting complexity and feasibility analysis to determine priority for RPA, and ultimately developing bots for the highest impacted processes. 

Intelligent Automation

Intelligent Automation, sometimes referred to as Cognitive Automation, is a more advanced form of automation technology incorporating artificial intelligence to pull and correlate data from multiple sources. One common practice includes enabling RPA and optical character recognition (OCR) to automatically extract semi-structured and unstructured data from various sources into a central database or application. For companies that regularly process a large volume of repetitive documentation, such as entering daily invoices into a central system, Intelligent Automation has the potential to significantly impact headcount costs by removing the need for manual data entry. 


Hyperautomation is the most mature level of automation technology that combines basic and intelligent automation with machine learning, ML. In this type of automation, the bot utilizes ML to become smarter and learn from prior results in order to handle more abstract cases. RPA and Cognitive solutions primarily focus on objective decision points, but require additional development in order to adapt to exceptions or new process workflows. By introducing ML, the inputs used by the bots continue to be refined with more data thus making the bot smarter and able to handle a wider variety of situations. It is because of this continuous improvement that hyperautomation can generate the largest return on investment for an organization when executed successfully.  

The return on investment with RPA is rarely realized without intentional scalability. This is typically because the cost of setting up a bot is not equivalent to the overhead cost of a single process. Therefore, companies must be prescriptive in their approach to rolling out automation in order to capitalize on the benefits of scaling bots across multiple processes. Companies must therefore develop an automation strategy that is scalable across the enterprise in a sustainable manner. To do this, apexon recommends establishing a Center of Excellence for Automation (COEA). 

Center of Excellence for Automation

Generally, companies follow a natural progression when establishing their COEA. The major difference between each tier has to do with where the COEA sits within the organization. A Localized COEA is led by an individual business unit with a roadmap that is limited to their internal processes. This COEA typically does not have dedicated development resources, but leans instead on external support for bot development. In these types of engagements, Apexon provides development expertise and process analysts to inform the roadmap.

Either a Clustered or Consolidated COEA can be the next step for an organization’s automation journey. In the case of a Clustered COEA, there are multiple Localized COEAs sitting within different business units. Apexon functions in a similar fashion by providing development and process support, but will also serve as a champion of best practices across the COEAs and provide insights into an optimized end-to-end process view. On the other hand, a Consolidated COEA is a dedicated team serving as a shared resource to the organization as a whole. A successful Consolidated COEA requires the discipline of a Project Management Office and the innovation of a Community of Practice. At this level of maturity, Apexon can also supply development and process resources if required by the organization. However, the main role Apexon plays in this type of COEA is establishing best practices and standards for implementation, change management, and organizational prioritization of the automation backlog. 

As with any new competency, selecting a platform and establishing a sustainable practice can be daunting without the right partner to ensure frictionless implementation. Apexon recognizes the importance of process automation and is excited to partner with you on this journey. Contact us here to get started.

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