“Work smarter” – Business process automation for optimizing workflows and increasing operational efficiency (Part 2)

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This article is the last in a series on business process automation (BPA) and builds on the basic knowledge of BPA provided in the last article. The aim is to explain the concept of hyperautomation to readers and to provide them with a better understanding of its areas of application and benefits.

The digital transformation is constantly challenging companies to further develop their business processes through technology (Gersch et al., 2020). One promising concept that is becoming increasingly important in this context is hyperautomation. Gartner first coined this term in 2019 (Haleem et al., 2021) and defines it as a business-oriented and structured approach that enables companies to quickly identify, evaluate, and automate numerous business and IT processes. This involves using a mix of different automation technologies like artificial intelligence (AI), event-driven software architectures, robotic process automation (RPA), business process management (BPM), intelligent BPM platforms (iBPMS), integration platforms as a service (iPaaS), low-code/no-code solutions, and specialized solutions for automating decisions, processes, and tasks (Gartner, 2025). The goal is to automate all processes that can be automated (Jiménez-Ramírez, 2021), which distinguishes it from the first three stages of automation (task, workflow, and user automation) explained in the last article and contributes to “process autonomization.” Recent data also underscores the economic benefits of hyperautomation. A study by Bain & Company involving 893 executives shows that companies that invest more than 20% of their IT budget in automation reduce their process costs by an average of 22%, while companies that spend less than 5% of their budget achieve only 8% (Heric et al., 2024).

What are the key aspects of hyperautomation?

To further specify hyperautomation, the characteristics defined by Mathew et al. (2023) can be used:

Figure 1: Aspects of Hyperautomation

Technology integration. As already described, one feature of hyperautomation is the combination of different technologies, such as RPA and AI or process mining. This integration allows complex business processes, such as a loan approval process from customer verification to payment, to be automated.

Scalability. Hyperautomation offers high scalability. Companies can implement automation not only on an ad hoc basis, but across departments and locations.For example, an Intelligent Business Process Management Suite (iBPMS) can be used for this purpose, as it increases the agility and intelligence of business processes by using low-code/no-code functions to adapt solutions and AI and analytics services to optimize running processes in real time. A cloud-based iBPMS architecture even enables workflows to be rolled out from one location to another with a single click, without the need to set up new infrastructure (TIBCO, 2025).

Adaptability. Hyperautomation is flexible and dynamically adaptable. In addition to iBPMS, low-code platforms can be used for this purpose, enabling non-technical employees to develop applications independently, accelerating development processes and reducing costs (Sanchis et al., 2019). This allows companies to continuously adapt their automation strategy to changing requirements and objectives, for example in the event of regulatory changes.

Process transparency. Real-time transparency is another key feature. Companies gain ongoing insights into the status of automated processes, which facilitates monitoring, control, and continuous improvement. Hyperautomation with process mining achieves this by showing how the process is executed in practice, enabling direct comparisons between target and actual processes (Saylam, 2013).

Intelligence & learning ability. The use of AI and machine learning ensures that hyperautomation continuously learns. As a result, automation strategies can not only run stably, but also be continuously optimized, a clear advantage over purely rule-based systems.

Exemplary use cases in the financial sector. In the financial sector, which is characterized by complex processes and regulations, hyperautomation is used in the following areas, for example (Safar 2025):

Figure 2: Hyperautomation use cases in the financial sector
  • Document scanning. AI-supported workflows capture structured and unstructured content (e.g., checkboxes, handwritten notes, images) and consolidate it automatically in a single process. This has enabled Deutsche Bank, for example, to increase process efficiency in corporate finance and reduce human error (Dewald, 2024).
  • Automated reporting. End-to-end automation for every step of regulatory report creation through hyperautomation ensures maximum accuracy and provides a complete audit trail, enabling banks to meet complex regulatory reporting requirements.
  • Preventing fraud and errors through real-time data analysis. JP Morgan Chase uses AI-powered systems to reduce financial losses from fraud or human error by analyzing transaction data and detecting anomalies (Dewald, 2024).
  • Compliance / Onboarding (KYC/AML). Intelligent bots verify identities, assess risks, and populate systems autonomously, reducing process throughput times. By automating its document verification process, UBS became the first Swiss bank to register and authenticate new customers fully automatically (Regula, 2025).
  • Customer satisfaction & 24/7 service. Chat and voice bots answer routine questions around the clock and escalate complex cases to human employees. These are already in use today, for example at Migros Bank (Huber, 2025).

Conclusion and outlook

Hyperautomation offers enormous potential for optimizing business processes and increasing competitiveness. Executives who leverage this approach strategically can achieve significant efficiency gains and make their organizations more agile. In view of dynamic technological developments, particularly in the field of generative AI, such as LLM-supported “autonomous workplace assistants,” which, according to Forrester, will support 10% of all internal processes and relieve human teams as digital colleagues (Le Clair, 2023), hyperautomation remains a promising field that will continue to gain importance and deserves ongoing attention and strategic investment.

Sources

Din Hida
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