Robotic process automation (RPA) and cognitive automation are two key technologies in the field of business process automation. Despite certain similarities, their capacities are essentially different. This blog article will introduce cognitive automation and RPA, highlight their distinctions, and show how they may be used in tandem to transform company processes.
What is Robotic Process Automation
Traditionally, people have handled regular, rule-based processes. RPA automates those processes. It entails programming software “bots” to do human-like tasks such as data input, transaction processing, or basic customer assistance.
The benefits of RPA are manifold. It can eliminate the need for human labor for regular chores which can contribute to cost savings. It also brings higher efficiency and accuracy by minimizing human errors and working around the clock.
What is Cognitive Automation
Cognitive automation, on the other hand, takes things a step further, because it applies machine learning (ML) and artificial intelligence (AI) to simulate how humans learn and make decisions. Compared with RPA, cognitive automation can handle more complex tasks, manage exceptions, and make decisions based on its understanding of the context.
The benefits of cognitive automation are far-reaching. It provides better analytics and decision-making capabilities, improves accuracy, and can handle unstructured data and complex tasks that RPA cannot.
Relationship between RPA and Cognitive Automation
While RPA and cognitive automation both aim to automate business processes, their approaches differ in important ways. Organizations can use each technology strategically for optimum gain by being aware of these differences.
Differences in Capabilities
RPA excels in handling regular, repeated activities and structured data with established rules and processes. It can accurately automate tasks like data entry, form completion, and transaction processing.
Cognitive automation goes a step further with AI, machine learning (ML), and natural language processing. This makes it possible to comprehend language, pictures, and other challenging inputs even on unstructured data sources. For knowledge-based jobs, exception management, and risk assessment, cognitive solutions are created to look and act like human cognitive capacities.
A Complementary Relationship
Rather than seeing RPA and cognitive automation as competing technologies, enterprises achieve the most value by employing them as complementary parts of an integrated whole.
In practice, organizations commonly use RPA for simple, high-volume processes and leverage cognitive automation to:
Extract and structure data from diverse sources for RPA consumption;
Manage exceptions, ambiguities, and other tasks beyond typical RPA capabilities;
Continuously learn from data to enhance decision-making across workflows;
A Powerful Pairing of Cognitive Automation and RPA
By strategically combining RPA’s strength in rules-based automation with cognitive technologies adept at unstructured data analysis, organizations can automate end-to-end processes handling both structured and semi-structured information.
Leveraging these capabilities together ensures enterprises extract full value from automation investments, with RPA augmenting what cognitive solutions provide and vice versa. Often, a combination of both technologies is the best approach.
Handling Diverse Data Types
A combined approach is ideal when both structured and unstructured data are involved. A cognitive solution can interpret documentation, conversations, images, and more to extract and structure information. This normalized data then feeds RPA bots for further processing.
Addressing Exceptions
Cognitive Automation is good at non-routine tasks like exceptions, new situations, and risk assessment. It can identify outlier cases and make judgment calls that enhance what predefined RPA rules and scripts handle regularly.
Enhancing Customer Interaction
Using cognitive and robotic technology in tandem benefits agents and virtual assistants. It can improve their abilities to comprehend questions, analyze consumer profiles, and offer customized next actions through conversations and digital touch points.
Supporting Complex Processes
Highly involved undertakings requiring approvals, risk analysis, or collaboration across departmental systems benefit from cognitive abilities augmenting RPA’s transactional capacities. The pairing addresses tasks too intricate for solo solutions.
Improving Customer Interactions
Combining cognitive and robotic technologies supports agents and virtual assistants. It boosts their capabilities to understand queries, assess customer profiles, and recommend personalized next steps via conversations and digital touchpoints.
Cognitive Automation and RPA Use Cases
Banking
Banks use RPA to automate high-volume processes like account opening. Bots digitally fill application forms and seamlessly share data between core systems for compliance. Cognitive solutions, meanwhile, examine text, phone records, and transaction data to find patterns of suspect behavior.
By comprehending the large quantities and variety of consumer communications and financial information, they can spot possible fraud and adhere to international anti-money laundering standards. Tools for risk assessment that continually learn from portfolio performance and economic data are likewise powered by cognitive abilities.
Healthcare
The use of RPA in healthcare reduces billing errors by extracting procedure codes and rates from charts to maximize reimbursement from insurance companies. Clinical staff thus spend more time with patients.
Furthermore, cognitive imaging analysis speeds up diagnosis by automatically identifying anomalies on histology slides, X-rays, and medical scans. Drawing clinicians’ attention to abnormalities for evaluation rapidly helps diagnosis and treatment plans. Such cognitive automation boosts throughput while retaining diagnostic quality, addressing physician shortages.
Retail
RPA standardizes inventory updates across warehouses and stores by synchronizing SKU additions/removals across disparate systems in real time. This streamlines replenishment.
Cognitive analytics then examines point-of-sale, website traffic, and customer profiles to generate personalized product/service recommendations displayed on-screen and in-app. This drives higher cart values by matching individual purchase histories and stated preferences to tailored upsell opportunities.
Manufacturing
RPA centrally manages equipment schedules, minimizing downtime between production runs. Bots reschedule as needed based on order volumes, customizations, or quality test outcomes.
Complementing this, cognitive solution images machinery operations for predictive maintenance. Modern anomaly detection techniques identify performance variances as problems arise, preventing potential failures. Additionally, machine vision categorizes product flaws automatically to save reworks and increase output quality.
Wrap-up
Although cognitive automation and RPA are both effective techniques for business process automation, they each have their own advantages. While cognitive automation is better suited to activities requiring human-like intellect and decision-making, RPA is best for high-volume, repetitive operations.
Businesses can modify their automation strategy by recognizing the similarities and differences between these technologies. The proper solution may result in considerable cost savings, efficiency, and competitive advantage, regardless of whether it uses basic RPA or a more sophisticated cognitive RPA.