Hyperautomation is the next level of automation that aims to drive digital transformation by integrating various technologies to automate complex business processes. It goes beyond traditional automation by utilizing artificial intelligence (AI), machine learning (ML), and other cutting-edge technologies to not only automate repetitive tasks but also to make decisions, analyze data, and improve processes in real time.
This approach allows organizations to automate not just individual tasks, but entire processes from end to end, resulting in increased productivity, reduced operational costs, and improved customer experiences. By leveraging hyperautomation, businesses can achieve a higher level of operational agility and responsiveness to market changes, ultimately gaining a competitive edge in today's fast-paced digital landscape.
What is hyperautomation?
Hyperautomation is the concept of automating complex or manual business processes using AI, ML, robotic process automation, natural language processing (NLP), and other technologies. The goal behind hyperautomation is to create a more efficient and agile organization by automating a wide range of tasks, from simple and repetitive to complex and decision based. This approach aims to enhance productivity, reduce errors, and enable organizations to adapt quickly to changing business requirements. Hyperautomation is often seen as a key element in digital transformation strategies for businesses seeking to stay competitive in the modern era.
Key components of hyperautomation
Robotic Process Automation (RPA)
RPA involves using software robots or "bots" to automate rule-based, repetitive tasks traditionally performed by humans within business operations. RPA is often a foundational element of hyperautomation.
These bots are programmed to mimic human actions and can perform tasks such as data entry, processing transactions, and communicating with other systems. RPA focuses on automating structured data processes, which are well-defined and have clear input and output parameters.
While RPA can significantly increase efficiency and reduce human error in these rule-based processes, it has limitations with unstructured data. Unstructured data, such as text, images, and videos, does not have a pre-defined format or organization, making it difficult for RPA bots to accurately interpret and process. As a result, RPA is not well suited for tasks that require complex decision making or the analysis of unstructured information. This is where AI comes in into play.
Artificial Intelligence (AI)
AI plays a crucial role in hyperautomation by utilizing advanced algorithms to analyze unstructured data and recognize patterns. Unstructured data, such as text, images, and videos, can be processed and understood by AI to derive valuable insights to support decision making. This ability allows AI to extract relevant information from various sources and make it usable for automated processes in hyperautomation.
Furthermore, AI plays an essential role in creating a digital twin of the organization (DTO) by simulating and modelling the organization's processes, assets, and workflows. AI can continuously monitor and analyze the digital twin to identify areas for improvement and optimization, ultimately driving more efficient and effective operations.
Large Language Modeling (LLM)
In recent years, significant advancements in the field of LLP have taken NLP to new levels especially in the size of training sets and the levels of nuance that AI can understand. In fact, among the primary innovations that have resulted in the current AI wave of solutions, LLM stands as one of the most substantial contributors. Combined with democratization AI, NLP and LLMs are more accessible than ever before.
Natural Language Processing (NLP)
NLP allows machines to understand and interact with human language, enabling the automation of tasks involving communication, document processing, and more.
NLP is a subfield of AI that focuses on understanding and interpreting human language through computers. It plays a crucial role in enabling machines to comprehend, interpret, and respond to human language in a meaningful way. NLP uses algorithms, computational linguistics, and ML to analyze and understand the structure and meaning of text and speech.
The importance of NLP is evident in its applications across various industries. In customer service, NLP is used to analyze customer feedback, understand user queries, and provide automated responses. In healthcare, NLP is essential for extracting and analyzing information from medical records, clinical notes, and research articles, which can aid in disease detection, diagnosis, and treatment. Additionally, NLP is widely used in information retrieval to make search engines more effective in understanding and delivering relevant search results to users.
Process orchestration
Hyperautomation involves coordinating and orchestrating multiple automated processes across different systems and departments. Process orchestration helps tie manual and automated business processes together for an end-to-end view to ensure that all processes run smoothly and are achieving the desired results.
Data integration
Hyperautomation relies on seamless integration with data sources. This includes extracting, transforming, and loading (ETL) data from various systems to ensure that the automated processes have access to accurate and up-to-date information.
Advanced analytics
Hyperautomation leverages analytics to gain insights into process performance, identify bottlenecks, and continuously improve automated processes.
Why is hyperautomation important?
Gartner has forecasted that by 2026, the market for hyperautomation software will reach nearly $1.04 trillion, and that by 2025, 70% of enterprises in all industries will have engaged digital business model transformation to further their analytics-driven business decisions.
This means that the way businesses operate with analytics, AI, and similar technologies is evolving, leading to the use of AI, ML and similar tools making smarter, more informed, data-driven business decisions. Hyperautomation is reshaping the future of how we work and keep a competitive edge in this digital era.
AI and ML play an important role in hyperautomation by enabling systems to learn from data and improve their performance over time, without having to be explicitly re-programmed to do so. Additionally, hyperautomation represents a "removal of redundancy" from the digital user’s daily tasks. In business, resolving customer requests, whether it’s a new loan, a dispute or prior authorization, is pivotal to creating a competitive advantage. Hyperautomation brings the opportunity to eliminate needless time spent on redundant tasks, raising the overall median of response time to customer requests. The time saved for the digital worker allows teams to focus on creating more innovative solutions, solving more complex problems and increasing the overall value a ‘hyperautomated’ organization brings to the market.
How AI and ML drive/enable hyperautomation
- Data analysis and pattern recognition: ML algorithms can analyze large volumes of data to identify patterns, trends, and insights. This is key for optimizing complex business processes.
- Decision making: ML algorithms can make data-driven decisions based on historical data and real-time information. This is particularly valuable in scenarios where decisions need to be made rapidly and consistently.
- Continuous improvement: ML algorithms continuously learn and adapt to changing data and can be retrained for new and evolving scenarios. This allows hyperautomation systems to evolve and improve over time, optimizing processes and adapting to new challenges.
- Predictive analysis: ML can be used for predictive analytics, forecasting future trends and outcomes. This is valuable in scenarios where anticipating future events can inform decision making and enhance automation processes.
- Anomaly detection: ML algorithms can identify anomalies or irregularities in data, helping to detect and address issues in near-real-time. This is important for maintaining the integrity and reliability of automated processes.
- Multimedia comprehension: AI and ML can not only understand text but images, audio and video are also now capable of being "understood" by hyperautomated tasks, thanks to computer vision.
To recap, AI and ML enhance hyperautomation by providing the ability to analyze, learn, and adapt based on data. This enables a more intelligent and dynamic approach to automation, allowing organizations to automate complex and sophisticated business processes more reliably and effectively.
Industry examples of the benefits of hyperautomation
Hyperautomation offers a range of core benefits for businesses, including improved efficiency, agility, innovation, compliance, and reduction in human error. By integrating advanced technologies like AI, ML and RPA, hyperautomation streamlines processes, automates repetitive tasks, and enables organizations to operate more efficiently and effectively. This ultimately enhances agility by allowing businesses to adapt quickly to market changes and customer demands. Moreover, hyperautomation promotes innovation by freeing up valuable human resources to focus on more creative and strategic initiatives, driving growth and differentiation.
Hyperautomation also ensures compliance with regulations and standards through consistent and accurate execution of processes, reducing the risk of errors and non-compliance. By minimizing human intervention, hyperautomation also decreases the likelihood of human error, ultimately improving the overall quality and accuracy of operations. In today's competitive landscape, hyperautomation is essential for businesses to stay ahead of the curve, maintain high standards, and democratize automation by empowering employees to utilize and benefit from these advanced technologies.
Hyperautomation and ML are transforming business across every industry. Alithya's solutions have demonstrated significant impact to our clients, bringing about positive change in industries such as finance, manufacturing, healthcare, and energy. These innovations are revolutionizing operational processes and enhancing efficiency across the board. Here are just a few examples of the benefits hyperautomation brings to various industries.
Financial services and banking - trade surveillance
In the fast-paced and heavily regulated world of financial services, hyperautomation - powered by ML - plays a critical role in trade surveillance. Alithya's AI-FI Trade Surveillance solution is a prime example of how hyperautomation enables systems to analyze large volumes of trading data significantly faster and more efficiently than humans ever could. This solution utilizes ML algorithms to identify irregular patterns, detect potential market abuse, and ensure compliance with regulatory standards - a time and data-intensive job that would otherwise need to be manually completed by compliance officers. With this solution, compliance teams can analyze large volumes of trading data, making the compliance process more intelligent, efficient, and accurate. The use of hyperautomation in this scenario allows compliance teams to focus on high-value tasks instead of spending that time analyzing and classifying trading alerts.
Manufacturing - predictive forecasting
Hyperautomation, when combined with ML, is a game-changer for predictive forecasting in manufacturing. Alithya’s AI-FI Demand Forecasting solution allows manufacturers to generate and utilize production plans based on leading indicators of expected demand. In the past, the manufacturing industry has relied heavily on lagging indicators, which only provide limited foresight into future demand. But by learning from trends and patterns in historical data, we can now better predict future demand. This is an invaluable insight for manufacturers to be able to accurately forecast demand and adjust production plans as needed. The ability to anticipate customer demand significantly enhances their capacity to meet it. This in turn, leads to savings on manufacturing, material acquisition, and storage costs, and overall improves business efficiency.
Healthcare – medical claim optimization
Alithya has transformed the healthcare insurance industry by making the claims management process more efficient through automation. Our solutions utilize advanced AI and hyperautomation to blend various systems and manual tasks, creating a smooth, automated workflow for processing medical claims from start to finish. Our technology automates the capture and extraction of data from both paper and digital medical claims, reducing human errors and cutting costs. This solution works with all standard medical claim forms and uses ML to extract data accurately. This technology also sets up automated checks as per business defined rules, reducing the need for human intervention, and increasing reliability by reducing the chance of human error. Recent successes have shown our platform being able to extract data with a 99% success rate and a 0.5% false positive ratio.
Healthcare – clinical correspondence
Alithya’s expansion into the healthcare field goes beyond claims and our achievements in the area of clinical correspondence, prior authorizations, grievances and appeals and even undeliverable mail have provided a recommendation engine to our customers. This engine is capable of reviewing complex clinical correspondence, integrating with customer systems of record, and automating decisions all the way to case resolutions. Being able to interpret a medical precertification and review hundreds of pages of medical records just like a nurse is a pure example of hyperautomation’s value. Alithya has taken 100% manual processes and produced solutions that deliver 80% “zero touch” automation to various lines of business in the healthcare payer field.
Energy/utilities - plant and document digitization
In the energy and utilities sector, Alithya’s solution leverages computer vision (CV) and optical character recognition (OCR) to transform the business’s capability to extract, digitize, and query large volumes of complex existing physical documents. These documents range from engineering drawings and schematics to technical documentation and even video or picture feeds. Alithya’s CV solution enables the automated identification and extraction of key data from the provided files, allowing documents to be efficiently digitized and queried. The automation of these processes results in significant time and cost savings for human operators who would otherwise have to manually extract and validate this data, this reduction of human effort required also reduces the potential for errors. This solution optimizes processes such as analog sensor data extraction, engineering drawing digitization or querying, and can even assist in the creation of a “digital twin” for a facility without the need for significant human effort.
You can see from these examples, the commonality is that hyperautomation removes or reduces the need for manual effort and human intervention in business processes regardless of the industry. This in turn reduces errors, making automated tasks more accurate and reliable. It also frees up teams to focus on the more important issues at hand.
By using hyperautomation and the expertise of partners like Alithya, you can unlock greater performance, agility, and remain competitive in today’s fast-paced digital world.
Creating a strategic AI adoption and implementation plan
As we know, AI/ML and hyperautomation are no longer tech terms of the future. Advanced technologies are here now and are transforming entire industries. At Alithya, we help our clients build a custom, strategic automation adoption and implementation plan to stay ahead of the competition and meet their business objectives, whatever they may be.
Alithya’s AI services for data and analytics can help you understand how to use AI-powered technology to improve business performance. We combine deep knowledge of your organization’s specific business problems with our technical expertise and industry-leading solutions to provide you with solutions and actionable business insights.
Can hyperautomation solve your business problem?
Maybe you’re just getting started with hyperautomation and are unsure if AI or ML can even solve your business challenge. Alithya’s AI-FI Suitability Assessment can provide you with a clear understanding of your data and identify how machine learning can be applied to achieve the greatest business result. Our team can assess your readiness for adopting ML and identify potential use cases and applications specific to your organization and business challenges.
Whether your goal is to uncover patterns, or improve processes, Alithya provides organizations with insights to understand risks, benefits, and make strategic decisions for implementing AI strategies that align with your business objectives and provide the greatest business value.
Contact us to learn more how we can help you automate to achieve greater business success.