Understanding Cognitive Process Automation

Cognitive Process Automation represents a powerful fusion of artificial intelligence (AI) and traditional process automation technologies, designed to significantly enhance business outcomes. This approach encompasses a range of methods that improve data capture, automate decision-making, and enable automation at scale. It often involves packaging AI and automation capabilities to establish best practices, encourage reuse, or deliver solutions through AI service platforms.

Often referred to as intelligent automation (IA), Cognitive Process Automation applies AI techniques to established automation tools like robotic process automation (RPA). This allows for the automation of complex tasks that previously demanded human judgment and intervention. Implementing IA helps organizations streamline business workflows, boost overall productivity, increase operational efficiency, and achieve the financial advantages associated with digital transformation initiatives.

By leveraging cognitive process automation, businesses can extract meaningful insights from unstructured data, automate crucial data validation and compliance verification steps, transcribe or translate human language, convert text from images or scanned documents into searchable, machine-readable formats, and generate valuable insights and recommendations for human operators. These capabilities are beyond the scope of older process automation methods.

Cognitive automation systems are designed to mimic human cognitive functions – learning from new information, identifying patterns and correlations within that data, and using these findings to make informed, data-driven decisions across numerous applications and use cases.

How Does Cognitive Process Automation Work?

Cognitive process automation functions by integrating RPA technology with advanced AI disciplines such as machine learning (ML), natural language processing (NLP), optical character recognition (OCR), and computer vision. This synergy automates intricate tasks that typically require human cognitive abilities like critical thinking, analysis, judgment, and creativity. Systems combining intelligent technologies with RPA can process and analyze massive volumes of complex data from diverse sources. Based on this analysis, they can take direct action or generate meaningful insights, improving human understanding and decision-making in complex scenarios.

Many cognitive services are customized for specific organizational needs rather than being built entirely from scratch. This allows companies to deploy the precise capabilities required to benefit their operations. Such customization usually demands specialists with the expertise to ensure the services align with company requirements, are robust and secure, and meet specific needs like data privacy or regulatory compliance standards.

Larger organizations frequently establish dedicated, in-house automation centers of excellence (CoEs) to manage sophisticated cognitive services. These CoEs spearhead the company’s automation strategy, ensuring alignment between automation initiatives and overall business goals. They possess knowledge of best practices for scaling cognitive automation systems, enhancing user experiences, and mitigating business risks associated with implementation, such as unforeseen costs or increased technical debt.

What Are the Benefits of Cognitive Process Automation?

A primary advantage of cognitive process automation is its ability to automate complex tasks that previously relied on human input or intervention, tasks that standard RPA could not handle alone. These advanced systems can consume, process, and analyze vast quantities of unstructured data originating from multiple sources—including documents, emails, images, customer interactions, voice recordings, and social media feeds—across a wide array of applications. This level of automation eliminates common errors and inconsistencies stemming from human factors like insufficient knowledge, fatigue, illness, or poor judgment, leading to superior results for specific applications or business workflows. Furthermore, cognitive systems can learn from their operational history to continuously refine their performance and improve the underlying processes they manage.

Finally, intelligent automation can significantly reduce labor costs. By automating certain complex or time-intensive tasks, organizations can reallocate and optimize their human resources. Employees previously engaged in these tasks can then shift their focus to higher-value activities, ultimately increasing the return on investment (ROI).

What Are Examples of Cognitive Automation?

Several terms are commonly used interchangeably or in close relation to cognitive process automation, including:

• Intelligent process automation (IPA)
• Intelligent business process automation
• Digital process automation (DPA)
• Hyperautomation
• Cognitive services

Here are some specific technologies often incorporated into cognitive automation systems and tools to handle advanced tasks:

• Machine learning
• Deep learning
• Natural language processing (NLP)
• Optical character recognition (OCR)
• Predictive analytics
• Intelligent document processing (IDP)
• Voice recognition
• Emotion recognition
• Sentiment analysis (opinion mining)
• Data mining

What Are the Uses of Cognitive Process Automation?

Cognitive process automation enhances numerous processes by extending RPA capabilities with AI. This integration allows cognitive systems to analyze data effectively and automate more sophisticated tasks, particularly those dependent on human knowledge and reasoning.

Suitable candidates for cognitive process automation include:

• Virtual Assistants: AI-powered chatbots utilizing technologies like NLP can understand and process human speech and text to handle complex queries and offer personalized responses or recommendations.
• Product Categorization: Automatically classifying product data from various sources into a unified, structured dataset is vital for improving product discoverability in e-commerce, enabling shoppers to find products faster and potentially increasing sales.
• Information Technology Service Management (ITSM): Cognitive automation can streamline numerous ITSM tasks, including incident response and management, service desk automation, security analysis, system monitoring, and vulnerability management.
• Accounts Payable: Extracting information from diversely formatted invoices, standardizing it, and loading it into accounting systems minimizes manual data entry and verification, accelerating the entire accounts payable cycle.
• Customer Service: Utilizing speech recognition and natural language understanding, systems can automatically retrieve relevant customer or support data during an ongoing service call. This empowers call center agents to have more informed and meaningful conversations, enhancing customer experiences and potentially identifying cross-sell or upsell opportunities.
• Employee Onboarding: Many routine onboarding tasks traditionally handled by HR, such as creating login credentials and enrolling new hires into programs, can be automated. This frees up HR personnel to concentrate on more strategic aspects of onboarding.
• Customer Onboarding and CRM: AI-enabled systems automatically capture customer information, aiding sales and customer-facing teams in improving engagement. These systems might also feature communication tools, contact management, and analytics to support sales, marketing, and customer support functions.
• Recommendation Engines: AI algorithms can generate personalized recommendations for customers by inferring their intentions from available data, thereby streamlining the customer journey. Combining RPA bots with conversational AI chatbots or virtual assistants can further enhance this process.
• Regulatory Compliance: Automation focuses on tracking regulatory updates, identifying and flagging activities posing noncompliance risks, and automatically generating compliance reports. This saves valuable time for compliance teams, helps address compliance gaps proactively, and ensures adherence to applicable standards, laws, and regulations.

What Are the Challenges of Cognitive Process Automation?

Perhaps the most significant hurdle in adopting cognitive process automation is the need for enterprise-specific customization and integration. While less problematic for simpler tasks, more sophisticated automations demand extensive planning, tailored development, and continuous iteration to achieve optimal outcomes. Smaller companies might lack the necessary resources, expertise, or funding to undertake these efforts, potentially limiting their ability to deploy and benefit from cognitive automation.

Other potential challenges include:

• A potentially slow path to achieving a positive return on the technological investment.
• Difficulty in finding experts experienced in automated business systems.
• The critical need to rigorously vet AI algorithms to minimize risks of bias and AI hallucinations (generating incorrect or nonsensical outputs).
• Potential security vulnerabilities arising from the system accessing a broader range of IT infrastructure and workflows.
• Possible privacy or compliance breaches if sensitive data, such as personally identifiable information (PII), is improperly handled within cognitive automation workflows.

What Are the Differences Between RPA and Cognitive Process Automation?

Standard RPA, without cognitive enhancements, employs software robots (bots) to mimic repetitive human actions on a computer. These actions include tasks like data entry, website scraping, basic data analysis, and providing automated help desk support based on predefined rules. Unlike cognitive process automation, traditional RPA focuses solely on automating predictable, rule-based manual tasks and is not considered an inherently intelligent technology.

While RPA bots require explicit programming with specific rules, cognitive automation systems learn dynamically. They move beyond simple rule-based operations, learning what to do and how to do it from the data they process. These intelligent capabilities enable them to adapt and adjust their actions, leading to progressively better outcomes for specific applications or use cases. This capacity for learning and adaptation also allows these systems to understand meaning and context within ingested data, perform analyses to generate useful, actionable information, and even make autonomous decisions.

Chart comparing key differences between RPA and cognitive automation systems across capabilities like data handling, learning, and task complexity.

RPA and cognitive automation serve distinct purposes and offer different benefits. Here’s a concise summary of the key distinctions:

• Task Complexity: RPA automates repetitive, rule-based actions. Cognitive process automation can automate a wider variety of processes, including complex ones that typically necessitate human input or judgment.
• Data Handling: Traditional RPA primarily works with structured data, limiting its applicability. Cognitive automation processes both structured and unstructured data, enabling support for a broader range of real-world applications.
• Implementation & ROI: RPA is generally simpler to set up and manage, often delivering quick tactical wins and a faster ROI. Cognitive process automation typically involves more time for infrastructure setup, workflow design, and ongoing management but often results in a significant strategic, long-term advantage.
• Learning & Adaptation: RPA bots follow explicit programming for specific tasks. Cognitive automation systems learn the intent of a use case without rigid programming, continuously adapting and improving their output and decision-making capabilities over time.