October 28, 2021

Robotic Process Automation Evolves into Intelligent Automation

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The term robotic process automation (RPA) describes a form of software that performs specific administrative tasks. It is based on the concept of “software robots” (also referred to as “bots”) that can imitate most human-computer business interactions, performing a variety of administrative tasks at high speed. RPA automates mundane, repetitive computer-based responsibilities in the workplace. Moving multiple files to another location and processing multiple copy-and-paste tasks are two good examples of using bots for tedious, repetitive tasks.

RPA bots — which are, at their core, software programs designed to perform these repetitive actions — are generally simple, inexpensive, and easy to implement. They have often been compared to machine learning and artificial intelligence, with RPA being described as “lacking any built-in intelligence.” Robotic process automation systems should not need any customized software, nor require a deep systems integration. They install easily and work efficiently. 

Intelligent automation              

When robotic process automation is combined with machine learning (ML) or artificial intelligence (AI) it becomes intelligent automation (IA). Intelligent automation uses natural language processing, machine learning, and RPA, to provide prescriptive analytics, process unstructured data, and automate processes that involve making decisions or using judgment.

Combining RPA with what is called “cognitive technologies” can produce some very impressive results. Cognitive technologies are subdivisions of artificial intelligence, and include computer vision, speech recognition, natural language processing, and machine learning. One goal of artificial intelligence is to imitate human thought. By combining robotic process automation with cognitive technologies, a limited range of decisions can be made. Historically, these decisions would have required the judgment of a human being.

Using intelligent automation

Automation has been around for some time, starting with machines replacing people on assembly lines, and gradually evolving to include artificial intelligence, cognitive technologies, and robotic process automation. Intelligent automation systems can be used in nearly every industry. Intelligent automation bots can process huge amounts of data and analyze it seeking inconsistencies, checking for correctness, and learning in the process. It should be noted intelligent automation is not yet easy to implement, and the decision to embrace it should not be made casually. Industries currently using intelligent automation include:

In commerce

Intelligent automation has become an efficient way to boost sales and increase customer engagement and reduce costs. Intelligent automation offers a variety of tools that provide useful benefits. Some examples of intelligent automation in commerce are:

Guidance through online shopping– AI supports customer guidance by digesting large volumes of data about customer journeys.

Transaction security– Smart algorithms detect signs of fraudulent operations, suppressing malicious behavior.

Data-driven planning of supply and delivery– Algorithms digest large amounts of data and predict future supply needs.

Hands-free checkout– A recent innovation, contactless payments allow customers to walk through the store, shopping, while smart algorithms calculate their charges, and then withdraw money from their account. This has been implemented at stores like Amazon Go.

In insurance

Customer claims and agent appraisals, payment calculations, and regulatory compliance are key features of the insurance industry. Intelligent automation supports these processes with virtual bots’ performing repetitive tasks more accurately than humans.

Managing data and calculating rates– Insurance service providers are using intelligent automation to manage data and calculate rates. Intelligent automation systems assist insurance companies in addressing tasks, such as eliminating the need for manually entering bulk data and improving time-effectiveness.

Augmenting insurance reps– Using IA to assist in responding to potential customers’ queries, in real time. 

Claim processing– Insurers are using intelligent automation to process claims quickly and more accurately.

In the factory

Robots and cobots (also known as collaborative robots) can accomplish almost any manufacturing process within a factory. Cobots are designed to work “with” human beings in safe ways. Many modern factories need minimal human participation because robots can make limited decisions. Examples of IA within the industry are:

Automated workflow– Digital bots allow manufacturers to process huge amounts of data, streamlining orders, procurement, and scheduling.

Predictive analytics– Proactively prevents problems, preventing outages and downtime. With predictive analytics, problems with equipment can be addressed before they break down.

Machine vision– This technology supports quality inspections that are more reliable and detailed than those performed by humans.

In transportation

Intelligent automation has had a dramatic impact on the transportation industry. IA solutions can present the most efficient routes for delivery drivers. Whether it’s an uber driver, a UPS driver, or a big rig driver, IA examines data in real time to provide the best routes available. Some examples within the industry are:

Route optimization– Intelligent automation solutions help drivers to find the fastest, most efficient routes, and will alter them in real-time to avoid bad traffic situations. Route optimization tools are often found in fleet management software.

Autonomous deliveries– Although not yet a reality, AI algorithms and RPA for self-driving vehicles will be used for transporting supplies, and possibly people. It is predictable that intelligent automation/AI controlled drones will also be used to deliver goods to customers. 

Also Read: Workflow Management vs BPM vs RPA

In healthcare

Healthcare providers have embraced intelligent automation solutions by combining artificial intelligence and RPA. Medical institutions can streamline repetitive tasks which are connected with data retrieval and decision-making systems. These decisions are based on facts and figures, and do rely solely on a doctor’s experience. Caregivers are assisted with the following tasks:

Patient dischargeBots– Used to create accurate guidelines patients can use to plan follow-up visits, pick up prescriptions, and plan other care tasks.

Electronic Medical Record Audits– Intelligent automation and RPA bridges the gap between diagnostic imaging unit and patient care environments based on the account number/order number.

Remote monitoring– Wearable medical devices allow caregivers to be remotely aware of their patients’ condition, location and current medical treatment.

Customer account management– Handles bulky data quickly and efficiently. Improves productivity.

In real estate

The real estate industry maintains huge databases of properties that are for sale and for rent, each of which requires proper handling and the best possible match. RPA and AI have revolutionized the real estate industry by automating time consuming, repetitive tasks. These tasks:

Buyer interest– Engages buyers before getting representatives involved, using faster, more consistent responses. 

Pricing– Bots are used to predict the value of properties by comparing similar properties and estimating the optimal selling price.

Loan defaults– Using data analytics and machine learning to create risk models, bots can forecast loan defaults. 

Tenant management– IA tools help create, check, and approve or reject rental applications.

In banking and finance

Finance and banking institutions use intelligent automation systems to maintain regulatory compliance, increase profits, and provide a streamlined customer experience. IA has been applied to the core processes of their business:

  • Cash management
  • Discounting and financing
  • Commercial lending
  • Letters of credit
  • Avoiding money laundering

Intelligent automation in the near future

Intelligent automation has caught the interest of developers, analysts, and CEOs. The combination of AI and robotic processing automation are becoming a new kind of workforce, in turn promoting digital transformation and expanding business opportunities. Intelligent automation is fast becoming an important part of succeeding in the world of business. 

Cognitive automation is supported by intelligent automation, and can be considered the next evolutionary step. It makes use of tools, predictive analytics, and large amounts of data to mimic human behavior. Cognitive automation attempts to imitate human behavior, and will even mimic emotional reactions.