As automation becomes increasingly critical for digital transformation efforts, new terminology like "hyperautomation" and "intelligent automation" is emerging. While these concepts sound similar at first glance, there are some key differences technologists and business leaders should understand.
Defining Intelligent Automation
Intelligent automation refers to automation technologies that go beyond basic robotic process automation (RPA) by incorporating artificial intelligence (AI), machine learning (ML) and natural language processing (NLP). This allows bots or agents to handle unstructured data inputs, adapt to changes, and make smart decisions rather than just repeating rote tasks.
Key components of intelligent automation include:
- Robotic process automation (RPA) for simple, rules-based tasks
- AI and ML for processing complex information
- Computer vision and OCR for document understanding
- NLP for contextual analysis of text and speech
Together, these technologies enable end-to-end automation of an entire process or workflow from start to finish. Intelligent automation systems can continuously improve over time with the right data and algorithms.
Artificial Intelligence and Machine Learning Capabilities
The AI and ML capabilities leveraged in intelligent automation unlock new levels of process efficiency and effectiveness. Some examples include:
- Computer Vision – Advanced image recognition and processing algorithms can extract detailed information from documents, forms, invoices etc.
- Natural Language Processing – NLP tools parse text data to understand context, classify documents, analyze sentiment.
- Predictive Analytics – Statistical models identify trends and patterns to forecast outcomes before they occur.
- Recommendation Systems – Sophisticated recommendation engines suggest next best actions and optimize decisions.
These AI/ML technologies move automation from purely rules-based to decision-making and continuous learning capabilities – creating intelligent agents rather than dumb bots.
Technical Components Enabling Intelligent Automation
Some key platforms and technologies for enabling intelligent process automation include:
- RPA Software – Leading RPA vendors like UiPath, Blue Prism, and Automation Anywhere provide bot development environments.
- AI Cloud Services – Platforms like AWS, Azure, and GCP offer pre-built AI for vision, language, recommendations.
- OCR and Document Processing – Tools like Google Document AI parse complex forms and paperwork.
- Workflow Orchestration – Integration software like IFTTT and Zapier automate workflows.
These capabilities combine to create intelligent agents that learning and optimize automated processes over time for greater efficiency and insight generation.
Defining Hyperautomation
Hyperautomation refers to a holistic business strategy of automating as many processes across an organization as possible. The term was coined by research firm Gartner, which emphasizes hyperautomation as a "business-driven, disciplined approach."
While intelligent automation focuses specifically on advanced technologies for complex process automation, hyperautomation encompasses a broader ecosystem of tools including:
- Intelligent automation
- Process discovery and mining
- Low-code application development
- Integration platform as a service (iPaaS)
- Digital twins of systems or processes
- Internet of Things (IoT) and sensors
Hyperautomation Integration Capabilities
Key integration platforms and services enabling enterprise-wide hyperautomation include:
- API Gateways – Manage APIs connecting disparate apps and data sources.
- Enterprise Service Buses – Message brokers like MuleSoft and WSO2 distribute data.
- iPaaS Tools – Integration Platforms as a Service (iPaaS) simplify connecting automation technologies.
- RPA Orchestration Hubs – Centralized management platforms control automation deployments.
These solutions provide the glue tying together all the platforms in a hyperautomation ecosystem – allowing smooth interoperation and scaled management.
Hyperautomation often utilizes an automation "hub" approach with different bots, agents, applications and capabilities integrated to automate end-to-end business activities rather than just individual tasks.
Comparing Intelligent Automation vs. Hyperautomation
While intelligent automation and hyperautomation are complementary in some ways, there are a few key differences between the two approaches:
Technology vs. Strategy
As noted above, the core differentiator is that intelligent automation refers specifically to advanced process automation technologies, while hyperautomation represents a broader strategy and business approach focused on maximizing automation across an organization.
Scope
Intelligent automation focuses on automating complex, end-to-end processes using AI and other cognitive technologies. Hyperautomation has a broader scope – the goal is automating as much of the business as possible using whatever tools are optimal. This may entail stitching together traditional RPA, intelligent automation, legacy systems and even human workers.
Implementation Approach
With intelligent automation, the starting point is typically identifying a complex process that can be improved through AI and developing the solution. With hyperautomation, companies take a stepped approach assessing the entire business, finding automation opportunities using multiple tools, and incrementally automating broadly.
In short, intelligent automation delivers the advanced technology capabilities needed for hyperautomation initiatives. And hyperautomation provides the business context and discipline for scaling intelligent automation across the enterprise.
The Growth of Business Process Automation
Automation is becoming an imperative for digital transformation and competitiveness across industries. Adoption of RPA and AI-driven automation continues to accelerate rapidly:
- The RPA software market is projected to reach $13.74 billion by 2028, expanding at a CAGR of 33.6%. (Fortune Business Insights)
- 63% of organizations have an automation strategy in place, up from 23% in 2021. (Mulesoft)
- AI adoption grew from 25% in 2018 to 54% of global enterprises in 2022. (Gartner)
And cost savings through increased efficiency continues to be a primary benefit driving automation growth:
- 73% of RPA adopters receive ROI within 6-12 months. Payback estimates range from 200% to over 1000%. (Deloitte)
- Average efficiency gains from automation average 20-30% across key processes. In some processes, automation drives 60-80% improvements. (McKinsey)
Automation Adoption by Industry
Automation is important across sectors, but some industries lead adoption:
- Banking and insurance have driven significant automation fueled by regulatory changes, transaction volume growth.
- Telecom and technology companies also lead in leveraging automation for improving customer experience.
- Healthcare, public sector expected to see continued expansion as automation helps strained resources.
Use Cases and Examples
Intelligent Automation Use Cases
Some common scenarios where intelligent automation delivers major impact include:
- Customer service processes leveraging NLP for sentiment analysis, intent recognition
- IT help desk tickets automated using AI and natural language capabilities
- Financial process automation with computer vision for document processing and contextual decision making
- Underwriting and risk analysis enabled by advanced predictive algorithms rather than rules
Hyperautomation Use Cases
Hyperautomation initiatives use orchestration hubs to connect disparate systems, data and technologies across departments:
- Integrating legacy CRM system with intelligent chatbot and RPA for streamlined customer support
- Unifying billing and invoicing systems across regions and business units into centralized financial ops platform with bots, digital worker desktops and unified data layer
- Connecting HR systems, payroll, badging systems and facilities IoT sensors for touchless, automated employee onboarding and offboarding
Expert Perspectives on Hyperautomation vs. Intelligent Automation
According to leading technology research firms, hyperautomation and intelligent automation are closely linked but distinct concepts:
- As per Gartner: "Intelligent automation provides the gateway to hyperautomation through its ability to understand unstructured information."
- For Deloitte: "Hyperautomation deals with the application of advanced technologies, including machine learning and artificial intelligence, to increasingly automate processes and augment humans across an organization."
- As IDC states: "Hyperautomation…takes process automation to the next level by layering advanced technologies over standalone automation."
The consensus is that hyperautomation represents the full vision for business-wide, scalable automation, enabled by the artificial intelligence, computer vision and other cognitive capabilities provided through intelligent automation.
Benefits and Limitations
Both approaches offer significant benefits while also having certain limitations:
Intelligent Automation Benefits and Challenges
Benefits
- Automate complex business processes end-to-end
- Handle unstructured data like documents, speech, video
- Continuous improvement over time via AI/ML
- Higher ROI from automating high-value processes
Research indicates intelligent automation delivers major efficiency and productivity gains across functions:
- 75% of early adopters saw increased productivity from process automation. 20% achieved over 50% improvements. (Deloitte)
- IDC estimates intelligent automation delivers over $3 million in benefits per 100 employees over 5 years.
- Forrester finds 73% higher capacity gains for decision-making tasks automated with AI versus rules-based RPA.
Limitations
- Requires large volumes of training data
- Narrow focus on specific processes vs. full automation strategy
- Harder to scale across enterprise due to custom solutions
While promising significant upside, intelligent automation also carries distinct challenges:
- Up to 30% of initial automation projects fail from technical implementation issues or lack of ongoing governance (Mckinsey)
- Over 50% of decision automation models degrade or become outdated within 12 months without monitoring, maintenance.
Hyperautomation Benefits and Challenges
Benefits
- Comprehensive automation approach across business
- Leverages multiple technologies based on use case
- Takes incremental, governed approach scaling automation
- Hub architecture for manageable enterprise automation
Per IDC, hyperautomation drives significant efficiency and experience gains:
- 75% of hyperautomation leaders see accelerated digital transformation vs. 23% of other firms.
- Hyperautomation improves employee experience for 60% of organizations.
- Standardizing tools and data via hyperautomation hubs delivers over 20% greater automation efficiency.
Limitations
- Complex coordination across multiple platforms and tools
- Potential gaps remaining across processes
- Significant change management requirements
The comprehensive scope of hyperautomation introduces challenges with change management and complex integrations between technologies. Common pitfalls include:
- 44% of firms struggle with process identification and candidate selection for automation. (Deloitte)
- Over 30% report significant technical challenges connecting new digital systems with legacy IT environments. (IDC)
Best Practices for Implementation
For successful implementation of intelligent automation or hyperautomation, some important best practices include:
- Take structured approach assessing processes based on automation potential and value
- Build executive support with automation center of excellence to align on strategy
- Prioritize solutions that enhance employee productivity rather than pure labor reduction
- Develop technical skills for building, deploying and maintaining automation tools
- Define policies for cybersecurity, compliance, bot management and monitoring
- Incorporate change management and employee adoption strategies
Additional recommendations from an analytics perspective:
- Implement process mining techniques to accelerate discovery of automation candidates.
- Leverage data science models for optimization – predicting bottlenecks, exceptions etc.
- Encourage IT/analytics partnerships to maximize AI value from company data.
- Maintain clean, unified data frameworks to fuel automation insights.
The Future of Automation
As an analytics leader closely following automation innovations, I see tremendous momentum across intelligent process automation, hyperautomation and the underlying AI capabilities fueling this transformation.
Today less than 5% of tasks are automated in most organizations, indicating massive headroom for expansion. IDC estimates intelligent automation and AI could automate as many as 80% of mundane workplace tasks as the technology matures.
I expect relentless waves of incremental innovation as robots become increasingly intelligent. Within 5-10 years, technologies like computer vision, voice recognition, machine learning and smart analytics will be integrated seamlessly into workflows. Humans will be augmented by seamless ambient automation and focus our energy on higher-value analysis, creativity and thinking.
Leading companies will continue embracing hyperautomation strategies to effectively leverage intelligent automation where it has the highest impact and interconnect critical business systems and data to enable next-gen efficiency.
The Bottom Line
While intelligent automation provides the sophisticated capabilities needed for end-to-end process automation, hyperautomation represents a holistic business game plan to maximize automation. Leading organizations will blend these approaches in a comprehensive automation strategy supported across IT, operations and business leadership. A well-governed mix of skills, platforms and technologies coordinated through a central automation hub can help drive the greatest enterprise value.