A hyper-automation practice is an enterprise approach to generating missing value that is embedded in its industry best practices, core competence, and the deluge of data that it aggregates from its sprawling operations across a significant period.
By integrating data across the enterprise and training AI models about its operations and decision-making trees, businesses can increase the efficiency of their day-to-day operations and more importantly improve interactions between employees, customers, and partners.
The hyper-automation project identifies processes that would benefit from automation, integrating operational data, choosing the automation tools, reusing proven automation where possible, and extending the existing automation capabilities with AI and machine learning.
The advancement between various forms of automation that have been used previously and today is being enhanced by anomaly detection, computer vision, and natural language processing, among others.
Gartner indicates that the hyper-automation journey of an enterprise is a framework, not a product. It is the maturation of the contribution that automation can bring to a business by increasing the scope and business impact of automation. While individual tasks can be automated, processes, cross-domain orchestration, and business model re-invention are what hyper-automation delivers.
Hyperautomation is not about the tools being used that can vary but about the value created by extending the capability and scope of automation.
According to Gartner, who coined the term hyper-automation, some of the typical generic ingredients required for a hyper-automation journey would include:
· Artificial intelligence
· Machine learning
· Event-driven software architecture
· Robotic process automation
· Business process management
· Intelligent business process management
· Integration platform as a service
· Low-code, no-code tools
· Decision, process, and task automation tools
Once the processes have been digitized and data integrated, training of the AI and ML models begins to yield results. Since hyper-automation is scalable, it can manage the increasing complexity of tasks while accelerating the speed and accuracy of operations. The enterprise begins its transformation journey from automation to hyper-automation.
For a business to go from manual to hyper automated requires the commitment of many executives, as well as a lot of data and technology. Here are the typical steps required in the hyper-automation journey.
1. Objectives, pain points, work in progress
Hyperautomation starts with an overview of ongoing business processes. Gather feedback from the front office and customer-facing teams on their pain points. Use process mining and process discovery tools. List the ongoing RPA initiatives and bots in place. Compare these with industry best practices.
2. Data Integration and platforms
Consider Data integration tools to enable seamless flow of data across various systems, ensuring that information is available where needed. Analytics tools help in deriving insights from the data, aiding in process optimisation and decision-making. In addition, Integration platforms can help to connect various software applications and systems, allowing data and processes to flow seamlessly between them. These platforms facilitate the interoperability of different technologies.
3. Tools and services
Make a list of tools, services and solutions that will enable and become part of the hyper-automation journey. These will be integrated with the data structures automating inside and across business applications and generating monitoring dashboards
4. Champions and leaders
Identify business and process heads who will partner with IT to hyper automate their areas of operations. They will help to identify the processes and workflows in the initial stage. Moving ahead, they will be responsible for demonstrating the returns the enterprise is gaining from the hyper-automation exercise.
5. Change management
Hyperautomation impacts the day-to-day functioning of the operating teams much more significantly than robotic process automation. The hyper-automation journey requires planning beforehand, on how to manage the before and after states of the business area, including future job roles and responsibilities. Team members are now hugely enabled with business insights and accelerated speed of operations and job roles need to be redefined to take advantage of these changes.
6. Metrics and performance measurement
Defining key performance indicators (KPIs) to measure the success and impact of the hyper-automation project is important. Metrics could include process efficiency gains, error reduction, cost savings, and customer satisfaction improvements. There needs to be a plan to continuously amend and get the best fit as the journey progresses.
To conclude, successful hyper-automation projects require a strategic approach that considers the unique needs of the organization, the specific processes to be automated, and the integration of technologies that align with the organization’s goals. A forward-looking vision is necessary to keep the hyperautomation journey progressing in the right direction as well as partnering with the right set of technology and process advisors.