What is hyperautomation?
Hyperautomation is a strategy to increase the automation of business and IT processes by harnessing multiple technologies, including artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), to discover, automate, and orchestrate complex processes.
Hyperautomation is a strategy to increase process automation by combining advanced technologies to find, automate, and orchestrate complex workflows.
Hyperautomation is an all-encompassing approach to applying AI-powered automation with advanced technologies, tools, and systems to streamline and optimize processes across people, operations, and data. Targeting enterprise-wide versus single task or process automations, hyperautomation represents a holistic strategy to turn isolated processes into optimized, intelligent, automated systems.
Benefits of hyperautomation.
Hyperautomation helps organizations realize the full value of automation, AI, and complementary technologies by applying them at every possible opportunity.
Accelerating complex work
Hyperautomation provides a high-speed route to the digital transformation of business operations by automating more—and more complex—work.
Harnessing AI and automation
AI-powered automation is the engine of hyperautomation, able to connect to business applications, leverage structured and unstructured data, analyze data and make decisions, as well as discover processes and new automation opportunities.
More efficiency and cost savings
By identifying existing processes and uncovering opportunities to eliminate bottlenecks and close process gaps, hyperautomation can lead to the most efficient and cost-effective way to achieve a desired business outcome, resulting in improved efficiency and reduced costs.
Higher customer satisfaction
Getting at the hard-to-automate nooks and crannies of customer service tasks and processes allows businesses to improve overall service and rewrite the status quo with faster response times and reduced wait times.
Smarter data collection
By leveraging Intelligent Automation tools, businesses are able to collect more detailed and more accurate data on everything from customer preferences to process differences between regions, leading to a complete ‘digital twin’ of the organization that supports better decision-making capabilities.
Improved accuracy and performance
Automating every process that can be automated eliminates human error at scale and reduces wasted time and resources. Extending that value to every possible process is the step-change of hyperautomation.
Faster innovation cycles
Automation technologies enable faster development of new products and services that keep up with market trends while providing better customer experiences through increased service speed, accuracy, and personalization of digital interactions both online and offline.
More scalability and flexibility
With automated processes in place, businesses have the freedom to rapidly scale operations without having to hijack existing resources. Automation also offers flexibility to quickly make changes and adapt to new business requirements or customer needs.
Greater security and compliance
Hyperautomation expands the application of technologies such as data encryption, access controls, and audit trails to more processes, helping businesses increase security and meet stringent compliance requirements while delivering end-to-end protection for mission-critical applications to safeguard sensitive data and ensure data privacy.
Sustainable competitive advantage
Hyperautomation helps businesses establish a foundation for continuous innovation, improved customer service, and greater productivity. This can result in stronger long-term profitability and market share for the business.
Increased employee engagement and productivity
By extending automation to every department and function and connecting back and front office operations, hyperautomation reduces the burden on employees across the business, giving back valuable time to apply to more complex tasks, creative work, and customer interactions.
Orchestrate automations and break down silos
By seamlessly integrating diverse technologies, processes, and data sources across the organization, a hyperautomation strategy improves collaboration and the flow of information between different departments for better decision-making and a more agile and connected business ecosystem.
Key technologies of hyperautomation.
Combining technologies to drive the rapid expansion and extension of automation is the hallmark of hyperautomation. According to Gartner, AI and automation enterprise systems are core enabling technologies of hyperautomation, powering automation of previously undocumented processes that rely on unstructured data inputs. In concert with core platforms, including business process management software, organizations are exploring the possibilities of mixing and matching from a wide array of AI technologies and next-gen tools, including machine learning (ML), process intelligence, data analytics, intelligent document processing (IDP), robotics, IoT, generative AI, low-code platforms, and still more.
Process discovery
Process discovery, supported by advanced technologies like artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), is a key technology component of a hyperautomation strategy. Process discovery provides detailed, 360-degree views of operational activities by capturing all processes and interactions between people, systems, and data to spot bottlenecks, risks, and areas for improvement. This thorough mapping and analysis of business processes help organizations identify and prioritize where to effectively implement automation, driving enterprise-wide process automation. Additionally, AI-powered process discovery tools speed up the development and refinement of automation, accelerating the realization of the benefits of investments in automation.
Intelligent document processing (IDP)
Intelligent document processing (IDP) supports hyperautomation by leveraging AI and ML technologies to automate processing complex documents. IDP extracts, classifies, validates, and integrates data from unstructured documents such as invoices, contracts, and claims forms, enabling an expanded scope of automation. IDP both recognizes and extracts text from documents as well as understands context and meaning, bringing the right information at the right time to feed faster, more accurate, and autonomous process automation and enabling businesses to handle large data volumes, contributing significantly towards an organization’s hyperautomation strategy.
No-code/Low-code automation
No-code automation supports hyperautomation by enabling non-technical users—citizen developers—to create and deploy automations, providing a means of expanding process automation across the enterprise to everyday workflows within functions and teams. With tools like generative AI and automation assistants that operate on natural language prompts, users can design, test, and refine automations without technical programming skills.
Intelligent Automation
Intelligent Automation provides core capabilities toward hyperautomation. It combines advanced AI technologies such as machine learning, computer vision, and generative AI with robotic process automation (RPA) to identify, automate, and orchestrate repetitive tasks, as well as collect and synthesize data for informed decision-making. Intelligent Automation also enables the seamless interoperability and integration of enterprise systems which is central to delivering on the promise of hyperautomation.
Robotic process automation (RPA)
Robotic process automation (RPA) plays a central role in enabling hyperautomation. RPA automates repetitive tasks and business processes by mimicking human interactions with digital systems and seamlessly integrating with AI tools. Robust and adaptable, enterprise-level RPA can perform thousands of automations concurrently, fulfilling security, systems integration, and compliance requirements while remaining user-friendly and easy to manage. When combined with AI-powered capabilities, RPA can go beyond handling structured data to process unstructured data as well as be flexible to adapt to new information to provide consistent performance for business-critical operations.
Artificial intelligence (AI)
AI technologies like machine learning (ML), natural language processing (NLP), intelligent optical character recognition (OCR), and AI computer vision play important roles in supporting hyperautomation. ML enables systems to learn, adapt, and improve over time, increasing the effectiveness of automated processes. NLP powers system understanding and processing of human language, expanding automation into areas like customer service and content analysis. Intelligent OCR underlies automated processing and understanding of text or numbers from documents, enabling accuracy and efficiency in automating document-centric processes such as invoice processing. AI computer vision extends automation capabilities by enabling systems to comprehend and interact with visual data. Collectively, these AI technologies represent core capabilities enabling the expansion of automation across diverse business processes.
Integrations, APIs, and iPaaS
Integrations, APIs, and iPaaS platforms are foundational enablers of hyperautomation, allowing high-scale, complex enterprise workflows to be automated. These technologies provide the means to transform disparate systems into interconnected, streamlined workflows, powering real-time action and data exchange across systems. Pre-built connectors and API packages offer no-code automation building blocks, simplifying the process of automating at scale. Similarly, cloud iPaaS platforms support the execution of automations from enterprise workflow apps, enabling developers to reuse automation assets across the enterprise. They also enable seamless interactions with API-enabled back-end systems, delivering real-time data for faster, more efficient process execution.
Generative AI
Generative AI accelerates hyperautomation strategy by automating and augmenting multiple aspects of the automation lifecycle and business process execution. As a stand-alone tool, generative AI can be applied to tasks that involve data collection, cleaning, preparation, and analysis. It can also provide ideas and content to supplement creative work. And its ability to generate synthetic data for training and testing can boost the effectiveness of hyperautomation models as well as reduce error risks. When well-integrated into automation assistant tools, generative AI enables users to request automations, generate personalized content, and summarize documents directly within work applications. For automation development, generative AI built into AI-powered automation systems can assist developers in creating complex automation workflows, quickly transform process documentation into ready-to-deploy automations, and monitor and adapt automations to application changes to ensure ongoing resilience.
AI Agents
AI agents, based on large language models like GPT-4, interact with systems and tools to execute cognitive tasks, engaging with users through prompts and utilizing long-running memory to retain context and adapt in real time. They can seamlessly integrate with enterprise architecture, leveraging AI models and algorithms, applications, and systems to perform cognitive tasks and operate collaboratively via multi-agent orchestration, eliminating automation gaps across enterprise systems. Within secure automation platforms that provide stringent governance and guardrails, AI agents represent the future of AI deployment, powering the acceleration of hyperautomation.
Supporting hyperautomation with an AI + automation enterprise system.
Choose an AI-powered automation solution to support the many facets of the hyperautomation journey, including your workforce, integration with existing systems, security, and scale.
Ease of use
Enable your workforce to engage with automation by choosing a unified hyperautomation solution with apps and tools they can easily use. Leverage the power of generative AI by selecting solutions with conversational automation assistants that enable business users to find and run automations in natural language. These automation co-pilots bring hyperautomation to employees by embedding automation within work applications and enabling users to call AI agents and orchestrate workflows across systems and teams.
AI-assisted development
Power your hyperautomation roadmap with accelerated automation development with AI to seamlessly identify processes and transform documentation into functional automations at scale. Generative AI is transforming the process of automating, enabling professional and citizen developers to use natural language prompts to create end-to-end automations. Look for AI-assisted automation development capabilities that accelerate the automation lifecycle and enable developers to safely infuse generative AI within workflows.
Integration-ready
Embed automation within existing technology and teams by selecting a platform with pre-built connectors for core systems and work apps. Insist on core integration capabilities to connect business applications via pre-built packages, enable workflow automation through iPaaS integrations, and support developers of all skill levels to drag and drop APIs into any process.
Cloud-native architecture
Scale automation smoothly and securely by selecting automation technology that has a cloud-native microservices architecture and flexible deployment model. Choosing a cloud-native platform ensures high availability and disaster recovery (HA/DR) are standard features.
Training and support ecosystem
Accelerate workforce transformation and innovation through a provider that offers effective training and a global support ecosystem. Look for robust support resources and guidance to establish a foundation for hyperautomation success by learning from similar deployments, strategies, and operating models to apply the best-fit approach to your organization.
Trust and security
Secure your business data and process automations by looking for enterprise-class reliability with high availability and defense-in-depth security standards and certifications, including SOC 1 Type 2, SOC 2 Type 2, ISO 27001, HITRUST, and ISO 22301. Comprehensive solutions will comply with industry-specific regulations, adhere to GDPR and data privacy principles, including the encryption of sensitive information, and provide essentials-only cloud storage.
Agentic automation
Future-proof hyperautomation efforts by choosing a solution that supports agentic automation. AI agents power end-to-end automation of cognitive workflows. Look for platforms that facilitate no-code development of AI agents and enable agents to integrate with existing enterprise architecture, AI models, applications, and systems. Ease of use features must include robust security and governance and ensure responsible AI deployment.
Use cases for hyperautomation within industries and teams.
At a practical level, hyperautomation means applying multiple technologies, tools, and platforms with the unified goal of automating every task and process that can be automated— both broadly across enterprise functions as well as holistically, orchestrating and unifying business operations end to end.
Banking and Financial Services
- Accelerate banking processes such as payment processing, account management, and loan initiation by automating complex operations to improve efficiency and reduce costs.
- Drive accurate risk assessment and credit scoring to help manage potential risks and safeguard financial health.
- Streamline regulatory reporting by automating data collection, validation, and transformation.
- Detect fraud by combining AI and machine learning technologies to monitor transactions, flag suspicious transactions, and enhance risk management.
Healthcare
- Improve patient outcomes by developing personalized care plans that harness vast data sets and automate communications, including appointment reminders and prescription refills.
- Ensure regulatory compliance by automating compliance processes, monitoring and audit as well as enforcing data handling requirements for patient records, billings, and other personal/sensitive healthcare data.
- Streamline administrative tasks such as billing and claims processing through hyperautomation to reduce errors, improve efficiency, and alleviate physician and healthcare worker burnout.
Insurance
- Accelerate claims processing with automation including initial claims intake, preliminary assessments, and data entry, to reduce processing time and increase accuracy.
- Improve policy pricing through hyperautomation to provide more precise and customer-centric prices using real-time data analysis.
- Strengthen compliance by employing hyperautomation to enforce regulatory requirements, automate oversight, and track changing legal standards and regulations.
- Connect data across systems with integrations between platforms and applications to power real-time data analysis drive informed decision-making and improve service delivery.
Manufacturing
- Reduce downtime through the application of hyperautomation to develop predictive maintenance models from high-volume data generated by manufacturing processes.
- Reinforce quality assurance by using computer vision systems powered by AI to inspect and assess product quality, detect imperfections, and apply corrective solutions in compliance with quality standards.
- Optimize supply chain with AI-powered models to predict demand and monitor and adjust inventory levels.
- Enhance product traceability by applying advanced hyperautomation technologies to embed tracking mechanisms and data collection for end-to-end tracing.
Evolution of hyperautomation.
Hyperautomation strategies have progressed in step with the rapid advancement of automation tools and technologies. What began with simple task automation has evolved into advanced AI-driven process automation tools, such as AI agents, that are accelerating the journey to achieving the vision of hyperautomation.
Beginnings of business process automation
Hyperautomation has its roots in business process automation technologies like optical character recognition (OCR), and rudimentary data scraping. Platforms that orchestrated these technologies kickstarted the evolution of business process automation.
Automation picks up speed with RPA and AI
Robotic process automation (RPA) marked a significant milestone in this trajectory, making automation tools accessible and cost-effective to apply to everyday business processes. RPA quickly evolved to integrate AI and machine learning (ML), in particular through intelligent document processing (IDP). The combination of RPA with AI-powered tools added data and process flexibility, widening the applicability of automation within enterprise operations.
Intelligent Automation and hyperautomation
Intelligent Automation was soon born from the fusion of RPA with AI technologies. Through AI, complex decision-making processes could also be automated, broadening the scope and functionality of automating business processes. Hyperautomation harnesses this amplified power of automation toward the goal of ongoing digital transformation.
Future of hyperautomation
The future of hyperautomation lies in continuing to combine advanced technologies to further the expansion of automation across enterprise operations. With the arrival of agentic automation, hyperautomation strategies have the means to dramatically accelerate and expand business process automation, driving holistic digital transformation.
Building a company culture for hyperautomation success.
Efficiency and innovation demand that enterprises embark on a hyperautomation journey while navigating the ever-evolving landscape of AI and automation technologies. Recognizing that the speed and effectiveness of implementing hyperautomation strategies are inextricably tied to the people within the organization, nurturing a culture that embraces change and technological advancement stands out as a key success factor. By placing skilling and culture at the forefront of the hyperautomation vision, companies can cultivate strong support and workforce engagement to not only set the stage but also ensure ongoing momentum for the hyperautomation journey.
Changing the way work gets done
The speed of your hyperautomation journey will be driven by your people. Lay the groundwork by putting people and culture at the center of your vision to gain strong support and participation from your workforce. Identify functional leaders and champions before you start. Hire for successful transformation by prioritizing breadth and diversity of skills and experience, along with flexibility and adaptability. Upskill employees and engineers alike in emerging technologies, including generative AI.
Creating a culture for hyperautomation
Intelligent Automation is at the heart of hyperautomation, empowering the organization to transform where complexity, variability, and orchestration made automation impossible before. Transformation occurs on multiple fronts at different paces as automation tools are woven into the fabric of work and increasingly complex processes are automated.
Charting a path to hyperautomation
Creating a hyperautomation roadmap will be unique to your organization. However, the process broadly follows four phases: Building the foundation, establishing the roadmap, operationalizing transformation, and scaling.
- Foundation: Start the hyperautomation journey by establishing a core team and/or initiating a center of excellence (CoE) to spearhead automation efforts. At the same time, define hyperautomation vision and goals and identify internal champions within business units and at leadership levels to drive alignment and commitment.
- Roadmap: Assess organizational readiness, including evaluating existing skills and determining the current level of automation maturity. Create an applied hyperautomation strategy for high-priority use cases. Part of this strategy is selecting operating and governance models and aligning these with organizational objectives.
- Transformation: Uncovering automation opportunities, prioritizing initiatives based on strategic importance, and defining success metrics make up the first half of the transformation phase. The second half includes implementing automation solutions, operationalizing automated processes, and assessing and optimizing performance to drive tangible outcomes and efficiency gains.
- Scale: At the hyperautomation scaling phase, the focus moves to expanding automation initiatives across functions and teams. This involves defining the next strategic priorities and opportunities effectively and ensuring seamless integration of automated processes across business functions to maximize the impact and benefits of hyperautomation at the organizational level.
Continuous hyperautomation success
The reality is that the path to hyperautomation will not be linear—plan for the journey to encompass multiple dimensions simultaneously, from strategy, operating model, and governance to process discovery and selection, prioritization, measurement, and scaling. Successful hyperautomation allows businesses to nimbly adapt to change, uncover and create opportunities, and reinvent themselves. It’s the future that lies beyond hyperautomation, where the organization harnesses the value of AI and automation and the wide array of complimentary next-gen technologies to become a true digital enterprise.
Enabling hyperautomation with an AI + automation enterprise system.
Combine AI and automation with a unified enterprise system to support the many facets of the hyperautomation journey, including your workforce, integration with existing systems, security, and scale.
