Hyperautomation: Transcending Traditional Bounds, From Bots to Beyond!

In today's fast-paced digital age, automation has been expanding its horizons. While chatbots such as ChatGPT have been making strides in enhancing customer interaction, the emergence of hyper-automation marks the advent of a more comprehensive and interconnected era. Hyper-automation, a concept that might seem nascent, is playing an instrumental role in reshaping how industries operate, taking automation capabilities far beyond what chatbots alone can achieve.

Decoding hyperautomation

While hyperautomation may be a recent term, its roots lie in pre-existing technologies such as artificial intelligence (AI), robotic process automation (RPA), Intelligent Business Process Management (iBPM), and low-code platforms. Hyper-automation doesn't necessitate the deployment of groundbreaking tools, but it does represent a transformative strategic approach to automation.

The hyperautomation journey initiated by pinpointing and addressing repetitive, labor-intensive tasks through automation to enhance efficiency. However, the true allure of hyperautomation lies in its capability for continual learning and progression. It marks the advancement beyond conventional automation, fostering a dynamic environment where decision-making processes are rendered more nimble, effective, and data-driven.

In comparing hyperautomation to its predecessor, RPA, the evolution is striking. RPA, though useful for automating predefined and repetitive tasks, is inherently limited in scope as it lacks adaptability and intelligence. Hyperautomation transcends these limitations by integrating RPA with AI, analytics, and other technologies, thus fostering an intelligent and adaptive ecosystem capable of optimizing processes, recommending actions, and breaking through inter-functional boundaries. This evolutionary leap enables end-to-end automation of complex workflows, allowing systems to make informed decisions, learn from data, and adapt as needed. Consequently, hyperautomation alleviates the need for constant human monitoring and frees up human resources to engage in more creative and strategic endeavors. It's a scalable and efficient solution that transforms monotonous, rule-based RPA activities into dynamic, intelligent strategies for holistic process improvement.


Hyperautomation adoption - a closer look

Although hyperautomation has been in the technology landscape for a mere half-decade, its momentum remains undeterred. Persisting economic uncertainties, talent gaps, and supply chain disruptions have done little to impede its growth. In fact, it continues to gain traction, as evidenced by 80% of Gartner clients indicating that they plan to either maintain or augment their spending on hyperautomation for the third consecutive year [1]. It is forecasted that by 2024, 65% of large organizations worldwide will have deployed some form of hyper automation [2].

Leading the charge in adoption are industries that are traditionally data-heavy and process-oriented, such as finance, manufacturing, logistics and healthcare. The impetus for their embracement of hyperautomation hinges on its prowess in expeditiously and efficiently processing complex data sets. This, in turn, catalyzes reductions in operational costs and bolstering of efficiency, while simultaneously fortifying adaptability to the ever-evolving market conditions and augmenting customer experiences. As such, hyperautomation emerges as an indispensable technology that businesses must adopt in their quest for sustainability and growth.

The adoption of hyper-automation indeed poses several challenges for businesses, including an implementation skill gap, difficulties in change management, concerns over data privacy, uncertainties in gauging success metrics and ROI, as well as high costs. [3], [4] Despite these hurdles, numerous organizations choose not to sidestep this technology since they understand that this pivotal transformation could be instrumental in enhancing resource efficiency and gaining a competitive edge in the market share battle. With a global market expected to reach $26.7 billion by 2028, the move towards this technology trend is undeniable [5].


From ChatGPT to industry giants: exploring hyperautomation applications

Since the beginning of 2023, the world has been buzzing with excitement over ChatGPT. In its essence, ChatGPT demonstrates how hyperautomation can revolutionize customer communication by automating language processing and enabling efficient, scalable, and personalized interactions. It exemplifies the power of AI and NLP in automating complex tasks traditionally performed by humans, enhancing productivity, and delivering improved customer experiences. Nevertheless, while ChatGPT exemplifies hyper-automation in customer service and support contexts, the scope of hyper-automation applications is vast.

Banking, Financial Services, and Insurance (BFSI) sector

The heavily-regulated BFSI sector has started leveraging hyper-automation to manage complex compliance, transparency, and auditability requirements. According to Gartner, approximately 80% of finance leaders are either already employing or planning to adopt RPA [6]. Hyper-automation helps in mitigating errors and risks by automating compliance processes like KYC and AML checks, while also keeping up with regulatory changes. Consider, for instance, the application of intelligent character recognition technology in KYC procedures. This allows for the electronic transfer of data from manually completed KYC forms into the respective fields of KYC portals, with additional data copies loaded into relevant systems.

PWC reports that large banks globally spend up to US$88 million annually just on collecting and storing data from corporate clients [7]. Though this represents a substantial investment, merely accumulating data doesn't confer a competitive edge. This is where AI-powered smart automation systems come to the fore. By monitoring transactions and proactively detecting fraudulent activities through advanced modeling techniques, AI-based machine learning models can foresee and mitigate potentially harmful transactions. Anti-Money Laundering (AML) technologies, which have become a mainstay in the financial sector, are excellent examples of how hyper-automation and AI contribute significantly to both predictive and preventive measures. [8]

Hyperautomation has significantly improved supply chain processes in task allocation, appointment scheduling, and capacity planning. For instance, machine learning-based hyperautomation improved productivity in a cold chain logistics company by autonomously assigning tasks to the most efficient staff based on its ability of monitoring and assessing real-time performance and location data. This resulted in smoother warehouse operations, reduced supervisor’s workload and overtime expenses.

In another scenario, hyperautomation helped address the issue of lengthy truck turn times and resulting detention charges in a cold storage warehouse. An intelligent solution based on machine learning offered insights into warehouse load, estimated picking effort, and order complexity. It analyzed the impact of scheduled appointments on other bookings throughout the day, intelligently predicting outbound truck turn times. By integrating with the warehouse management system, the solution automatically generated optimal schedules, factoring in potential carrier delays and allowing warehouse staff to focus on their tasks without concerns about truck dwell time.

These improvements were powered by an in-house low-code platform, which automates portions of processes and decision-making to enhance productivity. As a result, the supply chain has transformed into a smarter, interconnected system, with advanced analytics and AI driving decision-making. This illustrates the transition towards a more cognitive, AI-powered supply chain that augments human efforts intelligently. [9]

Healthcare sector

Revolutionizing the healthcare sector, Hyperautomation is making waves particularly in lab automation, health insurance processing, and enhancing patient experience.

In lab automation, hyperautomation employs robotics and advanced software to streamline laboratory processes. This not only accelerates various lab tasks but also reduces waste and manual labor, substantially lowering costs and increasing efficiency. Moreover, consumers benefit from the convenience of scheduling tests and accessing results online through user-friendly interfaces.

Health insurance processing is another area where hyperautomation is making a significant impact. By using AI and machine learning, it can quickly analyze vast datasets to detect fraudulent activities. This not only mitigates financial losses due to fraud but also expedites claim processing, resulting in increased client satisfaction.

A staggering 90% of healthcare providers believe that enhancing patient experience is the primary objective of their digital transformation endeavors [10]. At the forefront of this transformation is hyperautomation, which is revolutionizing the patient experience. By employing conversational AI and process automation, hyperautomation facilitates seamless engagement between patients and healthcare services. AI-powered chatbots not only assist in customer support but also empower patients through self-service scheduling and streamlined communication channels. Importantly, these chatbots gather data which, when leveraged by healthcare providers, enables timely and efficient intervention, thereby significantly improving patient care.

Last words

Hyperautomation, blending software robots with a range of tools and techniques, is showcasing the potential of AI-driven business operations. In the foreseeable future, we anticipate businesses assigning an increasing number of complex tasks to sophisticated AI systems. Yet, the successful deployment of hyperautomation requires cross-domain expertise and can involve substantial initial and ongoing costs.

Despite hyperautomation's capability to effectively perform certain tasks, it's not realistic or beneficial to aim for a total replacement of human resources. Therefore, it's crucial to establish clear, measurable objectives and to assess the feasibility, such as process stability and scalability, to ensure the delivery of anticipated business value. Employing hyperautomation is not a mere switch, but a journey that entails strategic planning and management to truly capitalize on its advantages.



  1. https://www.gartner.com/en/webinar/448856/1058287
  2. https://www.gartner.com/en/documents/4019586
  3. https://www.ibm.com/cloud/blog/hyperautomation-benefits-and-challenges
  4. https://akabot.com/additional-resources/blog/solving-3-challenges-when-businesses-move-towards-hyperautomation/
  5. https://www.einnews.com/pr_news/611608066/statistics-report-global-hyperautomation-market-size-and-share-will-hit-usd-26-5-billion-by-2028-zion
  6. https://www.gartner.com/en/finance/insights/robotics-in-finance
  7. https://www.strategyand.pwc.com/de/en/industries/financial-services/saving-a-bundle-on-banks-data-costs/saving-a-bundle-on-banks-data-costs.pdf
  8. https://www.rtinsights.com/10-hyperautomation-use-cases-delivering-enhanced-business-productivity/
  9. https://blog.gramener.com/hyperautomation-in-supply-chain/
  10. https://www2.deloitte.com/us/en/insights/industry/health-care/digital-transformation-in-healthcare.html



Add Comment

Subscribe to our newsletter