Contemporary businesses must transform decision dynamics by adopting automation-enabled workflows and prioritizing AI-mechanized hyperautomation at the top of digital transformation. So why is this recently expounded phenomenon surprising industries?
Existing scholarly works predominantly present the theoretical foundations of Robotic Process Automation (RPA) or its industry-specific implications within specific domains, notably finance, manufacturing, or healthcare. To elucidate the aforementioned conundrum, this article aims to analyze the current state-of-art of RPA and examine the converging impact of Artificial Intelligence (AI) and Machine Learning (ML) technologies. Inherently, it presents an empirical study to spot potential gaps in the ‘hyperautomation’ context as a key enabler in decision-making.
Introduction: Hyperautomation Making its Way into the Spotlight
Hyperautomation emerges as a multi-faceted strategy integrating leading technologies such as Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and predictive analytics to create a hyperautomated environment to derive optimal results. Simply put, it is a superior iteration of intelligent automation. In the modern business context, hyperautomation is a technological extrapolation to amplify the enterprise digital journey by accelerating crucial innovation initiatives, AI adoption, and driving digital decision-making. It requires organizations to take a comprehensive, outside-in approach to their business cases. It can address process debt effectively when business technologists have clear automation goals and use tools judiciously as needed.
Gartner predicts that the global expenditure on software technologies enabling hyperautomation will reach USD 1.04 trillion by 2026. According to Precedence Research, the hyperautomation market size will hit USD 197.58 billion by 2032.
Hyperautomation can be scientifically defined as the tactical utilization of integrated automation tools to optimize functions to their maximum potential, thereby achieving heightened productivity, enhanced operational efficiency, and substantial cost savings.
RPA Bots Becoming Super Bots: Driving Intelligent Decision Making
RPA bots that originally operated on rule-based programs through learning patterns and emulating human behavior for performing repetitive and menial tasks have become super bots, with Conversational AI and Neural Network algorithms coming into force. These self-learning agents configure cognitive reasoning and allow RPA bots to adeptly automate complex tasks with minimal (attended bots) or zero (unattended bots) human intervention. However, the risk caution lies here when transforming conventional RPA to its advanced derivative, driving cognitive automation. In many cases, business technologists fail to scale on their RPA initiatives either due to a lack of execution strategy, a poorly defined business case, or the wrong selection of processes to automate. A Forrester study states that 52 percent of user groups have claimed that they struggle with scaling their RPA program.
RPA has been in existence for over two decades, delivering deterministic outcomes using structured data in areas such as Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM). Primitively, RPA feasibility hinged on low cognitive demands and minimal exception handling. Recent case studies, however, reveal instances where AI-powered RPA bots demonstrate the ability to make subjective judgments, use interpretation skills, and handle multiple case exceptions.
Integration of Generative AI and Large Language Models (LLM) with RPA enhances virtual agents’ cognitive abilities, allowing human-like interactions and personalized feedback by learning customer preferences. The IT Service Management landscape has been strengthened with 24*7 availability, addressing common issues such as network troubleshooting, software update installation, and password resets.
Organizations are increasingly adopting the #Bring-Your-Own-Bots trend, integrating Conversational AI tools with APIs in their RPA ecosystem, thus eliminating the need for human resources in decision-making during customer engagement. This shift is expected to become the norm by 2024.
AI and ML Training Algorithms at Atomic-Level for Deep ‘Learning’ & ‘Thinking’
Between junctions of every workflow, decision-making is happening at a granular level, where software robots profile strings of structured and unstructured data in high volume to orchestrate automation across business processes.
Central to deep learning is the ML-based Neural Network algorithms, which have dramatically revolutionized the decision-making process at discrete data points on a quantum scale. It penetrates the big data—data input that is voluminous, scattered, and incomplete. It iteratively runs learning and predictions within probability parameters and ultimately derives an output.
Optical Character Recognition (OCR) technology is a valuable companion for real-life RPA applications within the healthcare industry. For example, by leveraging Natural Language Processing (NLP) and text analytics, OCR can proficiently scan and transform handwritten or printed documents, such as prescription labels, patient forms, doctor’s notes, and lab results, into digital format. This simplifies the storage and management of healthcare information, resulting in organized databases. The stored data is easily accessible, allowing for valuable insights to be extracted from a patient’s medical history.
Use Case: Healthcare
Precedence Research data reports that the global RPA in healthcare market is expected to reach USD 14.18 billion by 2032.
Case Point: UK’s Leading Statutory Authority for Healthcare System
- Clinical Information Support: The UK’s leading non-departmental public body providing healthcare service introduced the GP Connect initiative. This program enables General Practitioners (GPs) and authorized clinical personnel to seamlessly share and access clinical information from GP practices, enhancing patient care through improved data accessibility.
- Patient Registration: By leveraging RPA solution the healthcare system authority has streamlined the entire registration procedure. Bots are employed to gather and input patient-submitted data into clinical systems, eliminating the need for manual entry by practice staff.
- RPA Supplier Support: The authority collaborates with trusted RPA solution suppliers enabling GP practices to automate various processes. This initiative aims to enhance efficiency, save time for clinicians and administrative staff, reduce service delivery costs, and elevate the quality of patient care.
General Healthcare Use Case & Benefits
- Medical Insurance: RPA-driven hyperautomation proves more adept at identifying healthcare fraud compared to human capabilities. Any innocent human error is eliminated and enables health insurance companies to fasten claims processing with minimal manual intervention.
- R&D in Drug Discovery: RPA solutions is a key technology tool in life science industry to transform drug development and research. For example, RPA was crucial in potentially improving time to market for Covid19 vaccines. By integrating RPA with various IT systems, Drug Discovery, Clinical Trials, Pharmacovigilance, and Validation can be efficiently facilitated without human error.
- Lab Reporting & EHR: The laboratory test results or clinical history of patients are digitally stored as Electronic Health Records (EHRs). RPA and AI-enabled EHR systems function as intelligent, evidence-based tools, assisting healthcare professionals in making more informed decisions and conclusions for better patient care.
Use Case: Banking and Finance
Research and Markets predicts that between 2023 and 2028, the financial services and insurance sectors will have the most adoption of hyperautomation, outpacing other sectors with 32% of the market.
Key findings from some of the prominent real-life RPA use cases in banking industry finance are referenced below.
- Accounting: A well-configured RPA program can help standardize data for general ledgers and automate complex journal entries and document account reconciliations.
- Accounts Payable: Here, RPA bots can be augmented with Optical Character Recognition (OCR) to automatically capture and transmit data while simultaneously providing an audit trail and simplifying compliance reporting.
- Fraud Detection: Financial institutions possess extensive customer information, which is both highly confidential and susceptible to cyber threats. Machine learning-based anomaly detection and RPA-enhanced fraud detection systems have proven effective. Instead of relying on manual processes, banks can use RPA tools to continuously monitor transactions, identify anomalies using a rule-based system, flag potential fraud, and alert human staff for further investigation.
- Payroll: RPA can harmonize data across multiple time-keeping systems, evaluate shift hours, and identify time-sheet errors.
Conclusion
Hyperautomation is currently charting an illustrious path, serving as a vanguard for companies across diverse industries and business domains in propelling digital transformation. Yet, akin to any pioneering innovation, its implementation poses inherent challenges and risks.
Hyperautomation is often centered on how to effectively navigate and mitigate the multifaceted challenges and complexities inherent in its implementation. Some core challenges involve:
- Data Privacy Breaches: Shielding sensitive data and systems from cyber threats and ensuring adherence to data protection regulations.
- AI Bias Dilemma: Confronting inherent biases in algorithms and ensuring impartiality in decision outcomes.
- Compromised Data: Managing extensive data from diverse sources and guaranteeing its precision, dependability, and pertinence.
- Workforce Augmentation: Balancing the integration of human judgment with automated decision-making processes.
Upon transcending these challenges and attaining a heightened level of maturity in hyperautomation, enterprises can turbocharge workflows efficiency. Equally they will find it more straightforward to determine the right Key Performance Indicators (KPIs) for implementing new metrics-based revenue models tailored to their business needs.
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