Hyperautomation, a term coined by Gartner, is complete automation at speed, integrating various technologies to optimize business processes end to end. These technologies include robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), intelligent document processing (IDP), business process management, and low-code tools.
Hyperautomation combines these automation technologies to:
Automate any business process end-to-end.
Enable your modern workforce with the right capabilities.
Add intelligence and insight to automation with AI and ML.
Provide insight into automation ROI for continued scale.
“The combination of these trends around automation, artificial intelligence, and low-code development is the heart of hyperautomation.” - Neil Ward-Dutton, IDC.
Unlike deploying RPA bots alone, hyperautomation is a holistic approach that is significantly more potent. It allows faster application development (like low-code and no-code) and coordination of specific tasks, such as computing, data storage, data processing, and networking.
Similar to the distinction between generative AI and large language models (LLMs), hyperautomation and task automation are related concepts that differ in scope and complexity. Here’s a breakdown of how hyperautomation and task automation compare.
Task automation: Focuses on automating individual, discrete organizational tasks or processes. These tasks can be repetitive, rule-based, and relatively simple. For example, automating data entry in a spreadsheet, scheduling routine backups, or sending automated email responses.
Hyperautomation: Encompasses a broader range of activities and processes. It involves the integration of multiple automation technologies, such as RPA, AI, ML, and advanced analytics, to automate complex workflows, end-to-end processes, and entire business functions. Hyperautomation aims to automate individual tasks and business processes across departments and systems.
Task automation: Task automation typically deals with straightforward, repetitive tasks that can be easily predefined and executed based on specific rules or triggers. These tasks often have clear inputs, processes, and outputs, making them relatively simple to automate.
Hyperautomation: Deals with more complex processes and workflows that may involve multiple systems, data sources, decision points, and human interactions. It requires advanced technologies like AI and ML to handle unstructured data, make intelligent decisions, and adapt to changing conditions. Hyperautomation orchestrates and integrates automation tools and technologies to achieve seamless end-to-end automation.
Task automation: These solutions may operate in isolation, focusing on automating specific tasks within a single application or system.
Hyperautomation: These solutions emphasize integration and interoperability between automation tools and systems. They seek to create a unified automation ecosystem that spans the entire organization, integrating disparate systems, data sources, and processes to achieve holistic automation.
In summary, task automation addresses individual tasks or processes. Hyperautomation involves end-to-end automation of complex workflows and business processes with advanced technologies and integration capabilities. Hyperautomation is a more strategic and holistic approach to automation, driving greater efficiency, agility, and innovation across the enterprise.
Learn more about how hyperautomation fits into the automation landscape:
Streamlined application development and task automation solutions are essential for competing in the hyperautomation economy. Let’s review seven hyperautomation success factors that enable organizations to stand out from competitors by delivering unparalleled value to customers and stakeholders.
Hyperautomation leverages AI and other advanced technologies to automate repetitive tasks across various business functions. By automating these tasks, companies significantly increase efficiency and reduce operational costs. This is especially important for large companies with complex operations where small efficiency gains can translate into substantial cost savings.
Large companies typically deal with vast amounts of data and numerous processes that require scalability. Hyperautomation provides the scalability needed to handle these large data and processes efficiently. It allows companies to automate processes at scale, adapt to changing business demands, and handle increased workloads without needing significant manual intervention.
In today's dynamic digital economy, companies need to be agile and responsive to market changes and fast-shifting customer expectations. Hyperautomation enables companies to automate processes quickly and adapt them as needed to meet business requirements. This speed and agility helps companies stay competitive and build operational resilience in the AI economy.
Hyperautomation leverages AI and machine learning algorithms to analyze data and provide insights to inform decision-making. By automating data analysis and reporting processes, companies can make data-driven decisions more quickly and accurately. This gives them a competitive edge in the AI economy, where data-driven insights are crucial for success.
Automation can also be applied to customer-facing processes like customer service and support. By automating these processes, companies can provide faster response times, personalized experiences, and round-the-clock support, improving customer satisfaction and loyalty.
Hyperautomation is not just about automating existing processes; it also enables companies to innovate and transform their business models. By freeing employees from repetitive tasks, companies can redeploy talent to more strategic initiatives, such as innovation, product development, and customer experience enhancement, driving growth and competitive advantage in the hyperautomation economy.
Many organizations take a piecemeal approach to investing in automation technologies. Driven by narrow scope and tactical strategies and hoping for quick ROI, many companies choose quick-fix solutions that only address their immediate needs. As a result, they unintentionally create disconnected islands of automation without a unified solution to orchestrate a more end-to-end strategy.
These islands lead to more complex automation challenges across employee and customer journeys, especially as large organizations try to scale their automation initiatives. Process automation is crucial for organizations to compete in the digital economy. But you need an end-to-end process automation strategy to come out on top. That's where hyperautomation comes into play.
Now, let's delve into five hyperautomation use cases and examples, illustrating how this comprehensive approach to automation addresses the complexities faced by large organizations and enables them to achieve greater efficiency, agility, and innovation in their operations.
These islands lead to more complex automation challenges across employee and customer journeys, especially as large organizations try to scale their automation initiatives. Process automation is crucial for organizations to compete in the digital economy. But you need an end-to-end process automation strategy to come out on top. That's where hyperautomation comes into play.
In financial services, bank executives recognize that innovation is the key to unlocking new frontiers in risk management. Hyperautomation technologies, from cutting-edge analytics to AI and machine learning algorithms, empower institutions to leapfrog traditional risk assessment methods. By harnessing AI, for example, hyperautomation gives banks real-time visibility into emerging risks, enabling them to make data-driven decisions to optimize risk management processes precisely.
“Credit assessment, KYC (Know Your Customer), anti-money laundering, fraud, and collateral management are complex processes within the lending lifecycle,” says Appian’s Industry Vice President, Guy Mettrick. “These processes are well-suited for automation, especially within retail, corporate, and institutional banks."
Customizable hyperautomation platforms offer the most value when optimizing lending operations' efficiency, effectiveness, and decision-making speed. These platforms provide greater visibility into risk assessment and an organization's risk appetite, which are crucial for financial institutions' success.
For example, The Options Clearing Corporation (OCC), the largest equity derivatives clearing house globally, faced regulatory demands from the US government to enhance control and visibility over its operations for risk mitigation. In response, OCC adopted a hyperautomation platform within its legal department, focusing on regulatory filings, corporate actions, and product development.
In the past, OCC relied on spreadsheets which hindered visibility and documentation accuracy. With their hyperautomation platform, OCC now provides auditors with clear and comprehensive documentation, tracks processes end-to-end, and effectively manages risks amidst dynamic regulatory landscapes.
These islands lead to more complex automation challenges across employee and customer journeys, especially as large organizations try to scale their automation initiatives. Process automation is crucial for organizations to compete in the digital economy. But you need an end-to-end process automation strategy to come out on top. That's where hyperautomation comes into play.
The insurance industry is quickly moving into a hyperautomation future where emerging technologies will reshape how policies are sold, claims are processed, and risks are managed. By 2030, according to McKinsey, AI-powered automation could boost productivity in insurance processes and reduce operational expenses by a remarkable 40%.
Underwriting, assessing risk, and determining policy premiums has traditionally relied on manual analysis and decision-making. To be successful at underwriting requires a careful balance between speed and accuracy. With the rise of hyperautomation, insurers can streamline the underwriting workflow, making it more efficient and accurate. The best hyperautomation solutions allow insurers to:
Leverage algorithms and data analysis to evaluate risks.
Make faster, more accurate underwriting decisions.
Overcome the challenges of data quality and regulatory compliance.
For example, CNA, a leading US-based, global commercial property and casualty insurance company, developed ComPass™ on the Appian Hyperautomation Platform to improve operational efficiency across 164 countries. CNA’s automation solution allows insurance agents and underwriters to write and manage local-admitted policy placements in real-time, identify potential issues, and access the status of claims and payments, all within the Appian Hyperautomation platform.
By automating routine tasks such as data entry, document processing, and recordkeeping, hyperautomation revolutionizes government case management. By deploying RPA bots alongside AI tools, public agencies reap the benefits of accelerated case processing, error reduction, and enhanced regulatory compliance. Moreover, integrating workflow orchestration tools like BPM ensures consistent process adherence, streamlines operations, and minimizes delays.
Furthermore, by automating compliance monitoring and reporting, hyperautomation revolutionizes government case management, enabling agencies to deliver better outcomes for citizens while maximizing operational efficiency and effectiveness.
Hyperautomation holds immense potential in the healthcare industry for tasks such as patient scheduling, medical record management, claims processing, and billing. By automating administrative tasks and data entry processes, healthcare providers can:
Streamline operations.
Minimize administrative costs.
Streamline access to patient data across the care continuum.
Optimize care coordination.
Automates the entire claims processing lifecycle.
Bolster compliance.
Reducing processing times and enhancing cash flow.
Hyperautomation offers manufacturers a powerful toolkit to increase operational efficiency, reduce costs, and achieve sustainable growth in an increasingly competitive global market. For example, predictive maintenance allows manufacturers to foresee equipment issues before they arise, saving valuable time and resources that would otherwise be lost to unexpected breakdowns.
Additionally, by leveraging advanced scheduling algorithms and real-time production data, manufacturers can optimize production schedules, reduce setup times, and improve overall equipment efficiency to optimize cost savings.
Get three more 3 Real-World Hyperautomation Examples to Learn From
Hyperautomation offers retailers a powerful toolkit to optimize operations, enhance customer experiences, and drive business growth. For example, hyper-automation empowers retailers to automate customer service processes, including inquiries, complaints, and returns management. By deploying AI-powered chatbots, self-service portals, and customer relationship management systems, leading retailers provide personalized and responsive customer service that drives customer loyalty.
Additionally, by leveraging real-time data analytics and predictive algorithms, hyperautomation allows retailers to:
Optimize inventory levels.
Reduce excess inventory costs.
Improve product availability.
Enhance customer satisfaction.
Drive sales.
Ready to join the hyperautomation revolution? Our most important tip for selecting the right hyperautomation tools and technologies is to prioritize a platform approach.
Organizations have historically invested in various automation technologies separately, resulting in disconnected solutions. This fragmented approach creates challenges across the board, particularly when scaling automation initiatives. To avoid this pitfall, prioritize hyperautomation platforms that integrate various technologies.
By unifying AI and other automation tools within a single platform, you can streamline operations and avoid creating disconnected islands of automation. This integrated approach ensures seamless technology collaboration and creates a robust foundation for effective data management.
To select a platform that’s right for your organization, consider the following criteria:
Evaluate how well the platform's automation tools seamlessly collaborate to execute end-to-end processes, including RPA, AI, and machine learning. The best platforms are designed with a cohesive orchestration layer that seamlessly blends human and digital workers, enhancing operational efficiency.
Prioritize a platform with a data fabric architecture that facilitates seamless data integration and utilization across the enterprise, regardless of the system involved. This virtual data layer streamlines data integration, eliminating the need for extensive database programming and maintenance.
High-performance platforms offer low-code development capabilities for quickly building comprehensive processes and applications without extensive coding. Additionally, ensure the platform includes an integrated orchestration layer to facilitate task allocation among humans, digital workers, and systems, allowing for adaptability and scalability as your organizational needs evolve.
Opt for a platform with robust integration capabilities, including pre-built connectors and easy API development, to seamlessly integrate with existing software solutions.
Choose a scalable platform that can grow with your organization and expand automated processes across departments. Cloud-based architectures offer flexibility and performance advantages, ensuring continued returns on investment over time.
Consider platforms with process mining capabilities to analyze real-time event logs and identify inefficiencies, bottlenecks, and governance issues in your business processes. This insight enables continuous process improvement and adaptation to evolving needs, enhancing overall operational efficiency and effectiveness.
Hyperautomation unlocks enormous benefits, from cost savings and improved operational efficiency to enhanced customer experiences and accelerated innovation.
However, fully realizing the potential of hyperautomation requires a platform approach that unlocks growth, resilience, and competitive advantage.