Intelligent Automation and Artificial Intelligence
Artificial intelligence is associated with autonomous workers capable of mimicking human cognitive functions, whereas intelligent automation is focused on the development of better workers, both human and digital.
In today’s world, organizations are enamored with the promise of emerging technologies like cognitive computing, artificial intelligence (AI), robots, and various forms of robotic automation. Still, many are unsure how and where this could benefit their businesses. As a result, this ambiguity has created an environment that has led to an uneven acceptance of the new technologies and correspondingly significant skill gaps.
The inability of a company to familiarize themselves with innovative technologies because they fail to identify immediate ROI could eventually come back to harm them in terms of business operations and employee recruiting efforts if they are associated with having inadvertently created a culture of hesitancy versus innovation. Therefore, let’s discuss a few areas where this revolutionary technology can benefit those willing to embrace an innovative mindset.
Deriving Value from Intelligent Automation
Intelligent automation’s (IA) ability to mimic the cognitive abilities of humans to “think” and “do,” is sometimes categorized as cognitive automation. Intelligent automation bridges the capabilities of artificial intelligence, machine learning, natural language processing, intelligent document processing, optical character recognition, and structured data interaction with robotic process automation to provide a potentially transformative technology solution.
IA is used to perform a higher subset of tasks that involve deductive reasoning, analysis, questioning, intent and sentiment recognition, and decision making before initiating any action. Depending on the process’s complexity, the IA framework can go through all of these functions or exclude somewhere necessary. Without robotic process automation (RPA), IA would require unique connections to extract data from various sources and require programming for APIs to translate the results of the AI engine into actions. Below is a high-level intelligent automation architecture diagram.
Intelligent Automation — Implementation Use Cases
Intelligent Automation for Inventory Management
The traditional method of inventory management is time-consuming and inefficient. With IA, companies no longer need their workers to fill out invoices manually. IA can automate several backend activities such as invoice generation, shipment tracking, delivery, fulfillment management, supply-chain management, and much more.
As a result, organizations can save time and money, avoid human errors and utilize resources for higher-value activities. Technologies such as NLP and IDP coordinate with IA to create a data-field mapping schema to convert bills and other order-related documents of varying formats to a standard document format to upload them to the cloud for audit purposes or send them to the respective customers.
Intelligent Automation for Banking Fraud Detection
Data from several thousand customer accounts can be extracted and fed into the AI engine of an IA framework. Then, the data is processed by cross-referencing spending patterns and transactions with historical data to check for irregularities, such as whether the customer is funding any illegal activities by comparing transaction endpoints to a blacklist.
Finally, the framework lists transactions that it categorizes as fraudulent or worthy of further inspection. After narrowing down suspicious transactions, IA can perform actions such as sending alerts to officials, freezing the transaction/account, sending notifications to customers, or communicating with internal auditors.
Intelligent Automation for Automated Customer Support
With the aid of NLP, meaning, intent, and the sentiment of a customer’s inquiry can be implied and understood. Intelligent automation uses NLP and functions as an integral cog in Intelligent Virtual Assistants (IVAs) which respond to customer queries with human-like dialogue.
IA and NLP help answer customer queries for which the IVAs were not explicitly trained. Deep learning and machine learning algorithms in an IA framework assist with understanding colloquial language syntax and abbreviations. Also, if the situation demands, IA can route a customer to a support executive depending on the severity of the complaint.
Also Read,
Extract meaningful information from Big Data using NLP and Machine Learning
Shifting From RPA to Intelligent Automation Services
First, enterprises will need a company-wide strategy to succeed. This strategy entails identifying areas and use cases whereby AI could improve operations and create new learning processes. Look at IA as a new way of working, supported by organizational reform and ongoing targeted retraining.
Payoda has the knowledge and experience to help your company progress from basic robotic process automation (RPA) to a more powerful, intelligently automated environment. We help you automate repetitive tasks and focus on exclusive work to make the most of your workforce.
What’s the payoff? Predictive models identify your biggest challenges, and prescriptive models provide a roadmap to the future. In effect, creating processes that combine informed human intellect with perpetually self-optimizing AI!
To know more, we encourage you to consult our technology experts who can accelerate your growth, cut costs, mitigate risk, and provide you with a tailored services to streamline your important business processes.
Blog Authored by: Mohan Bharathi
