AI and Automation – Is your business ready for hyperautomation?

As businesses gain through improved ROI, investment in hyperautomation is expected to increase to achieve operational excellence and resilience. By Balakrishna D R, Senior Vice President, Service Offering Head - ECS, AI and Automation, Infosys.

Automation has found a place in businesses since the beginning of industrialization. Enterprise automation tools have made efficient routine business processes such as accounts payables and order management. Even industry-specific processes like claims management in insurance or loan underwriting in banks today require little human intervention with the help of Robotic Process Automation (RPA). While automation software has traditionally focused on the ‘doing’ aspect of processes, modern-day automation includes the infusion of AI with RPA resulting in intelligent automation that also augments the human ability to ‘think’. Can we extend the value of automation even further?

Hyperautomation is an expansion of automation using sophisticated-AI based automation tools and software and an ecosystem of platforms and systems that extends automation to every business process in an organization that can possibly be automated. It promises to automate complex operational decision-making. To reap the full benefits of hyperautomation, organisations need to invest in sophisticated technologies and build access to the large amounts of data required to drive automation on a large scale. Are they ready to do that?

Roadblocks to achieving hyperautomation

The road to effective hyperautomation is filled with challenges. Lack of vision can lead to investments in solutions that either do not scale up or integrate well with other tools, resulting in automation occurring in siloes. The automation landscape offers multiple solutions, and enterprise architects are often left debating on the capabilities they need to invest in. Sometimes, the shelf-life of a solution and vendor stability are overlooked, impacting both support and enhancements required to keep pace with changing needs.

Lack of guidance or know-how to assimilate RPA with other tools is a common hindrance, particularly when employees do not have the necessary skills. Cultural resistance to automation due to fear of job loss is a difficult hurdle to overcome. The AI maturity of all the players in the entire automation chain needs to be the same to maximize benefits. Lastly, unstructured data and security concerns can derail the hyperautomation journey.

How can organizations ready themselves for hyperautomation?

Enterprises may invest in the most complex AI-based automation solutions, but unless they align to the long-term strategic roadmap to hyperautomation, they will fail to deliver the desired value. Technology innovation leaders should plan for end-to-end automation that is well-aligned to the overall business goals. The roadmap must include complementary technologies that are can be both scaled and well-integrated.

The first step to hyperautomation is to assess the AI maturity of an organization. Based on the maturity, a long-term strategy must be designed to ensure technology-buying decision-making

is streamlined to optimize the following key elements – revenue, costs, risks, and quality. The next step is to assess the different technology markets and create an investment plan to effectively deliver tactical and strategic business values.

Is the investment adding to the revenue? Will it enhance processes, increase customer engagement, or introduce new services – are some questions that enterprises must ask themselves. The choice of automation tools must contribute to optimizing costs by reducing errors or expediting and redesigning processes for efficiency.

Every automation must consider the risk of non-compliance with regulatory requirements. Adding AI to the automation mix brings legal, ethical, and compliance responsibilities that need to be taken care of. To ensure trust and prepare for future regulations, organizations should take steps to ensure that AI implementation is explainable. Automation success will depend on selecting the appropriate data for each use case and ensuring its quality. A strong use case strategy driven by business needs and not as much by technology can set the tone for success early in the hyperautomation journey.

Creating a powerful integration strategy is vital as it allows systems to be managed centrally and communicated throughout the organisation. Enterprises must assimilate and orchestrate the different platforms, tools, and software that it uses for hyperautomation. Digital Operations tools that align closely to the automation roadmap must be selected. All AI applications must be integrated with digital operations tools to augment business processes and deliver long-term business value.

None of the steps taken will lead to success unless an effective change management strategy is devised to counter employees’ fears of being made redundant with hyperautomation. Employees must be allowed to upskill and reskill to take on high-order thinking jobs, as the mundane is taken over by hyperautomation. Businesses need to focus on recruiting the right talent and invest in continually up-skilling them.


Post-pandemic, hyperautomation has picked up pace across industries to improve productivity and capacity, to meet fluctuating demands, and improve the quality of services and products delivered to customers and enhance customer experience. As businesses gain through improved ROI, investment in hyperautomation is expected to increase to achieve operational excellence and resilience.

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