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3 intelligent process automation use cases and how they work

Enterprises are pursuing intelligent process automation to take their digital transformation and RPA applications to the next level. Here's a look at three use cases.

Digital transformation is a top priority for businesses. The goal is to go from being people- and paper-bound to being seamlessly electronic.

To accomplish this goal, organizations need to combine and automate various processes, data, workflows and systems to become more efficient. However, merely automating data and processes is no longer enough. Focusing on making existing processes more efficient is helpful, but it's hard to gain a competitive advantage by just doing what they're already doing.

Now, leading organizations are looking at intelligent process automation use cases to help address the more complicated challenges in the corporate environment and gain true value and insight from their data.

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Robotic process automation (RPA) is proving to be very popular. RPA technology introduces the concept of software bots that exist in a virtual workstation environment and use keyboard and mouse interaction with existing applications to take action and execute tasks. However, these process automation tools are meant to merely repeat functions and actions that require integration or interaction across a range of systems.

RPA bots evolved from desktop automation and process management roots and provide companies with sophisticated tooling and capabilities to work around the fact that there may be many disparate systems that do not talk to each other. Many organizations are successfully taking advantage of RPA bots to help with a variety of tasks, freeing up man-hours, reducing errors and speeding up various processes. Most of these software bots are trained by recording the human user's movements, and then they mimic these actions.

While this automation can be extremely useful and helps get data into correct formats and spreadsheets, it's not particularly intelligent. The bots can't make decisions, can't handle information that requires manipulation or edits, and can't understand and process UI changes, such as a field being moved on the screen. These simple automation tools get stuck when judgement is needed on what, how and when to use certain information in certain contexts, and human intervention is needed in order to move forward.

For organizations that want to move up the ladder of intelligence, they need to start using systems that use AI and machine learning to dynamically adapt to new information and data. This increased intelligence will start to shift these systems from mere robots that automate and repeat the exact same steps to more intelligent automation tools that can significantly affect organizations. Once this happens, enterprises will begin to see real benefits for a variety of different use cases.

Let's look at three intelligent process automation use cases.

Intelligent process automation takes on customer service

Improving and enhancing customer service is a constant goal for essentially all enterprises. Companies are often faced with significant challenges, responding to and resolving customer service inquiries and requests in a timely manner. Large organizations often have disconnected processes and customer data in multiple systems.

For call center agents, resolving a single customer service issue could mean accessing dozens of different systems and sources of data. To bridge the disconnect and provide better service, companies are turning to intelligent automation to tie together disparate systems on premises or in cloud, provide automatic handling of customer data requirements, ensure compliance and reduce errors.

Intelligent automation allows companies to significantly reduce operational service costs, consolidate dozens of systems into just a handful of coordinated processes and accelerate customer service response times. Rather than simply moving data from one system to another, intelligent automation systems are able to process inbound requests and actually understand what customers are asking using natural language processing (NLP) capabilities.

With NLP, these bots can read and understand emails, documents and chat conversations, as well as expedite connections to the right information with more speed and accuracy. Likewise, using computer vision technology enables customer service systems to be able to process images, video and documents, and then translate that understanding into information that can further help process customer support requests.

Speeding up insurance claims handling

Insurance companies operate in a complex environment, offering a diverse array of products, from home to car to business insurance. So, it's no surprise insurance call centers receive a large volume of calls and email requests touching on many different issues. Intelligent automation and artificial intelligence technologies are helping to streamline agent and broker processes, accelerate claims management, and deliver improved overall customer experiences. Intelligent automation also helps relieve some of the pressure for call center agents by providing augmented intelligence prior to, during and after the agent's interaction with the customer. 

By applying more advanced intelligent automation systems, insurance companies are able to see significant returns. Intelligent automation is able to take information from multiple sources, aggregate it, fix any missing or erroneous data, and place the resulting information in a data store. This process saves many man-hours, freeing up human agents to work on higher-value issues.

Insurance agencies are also using artificial intelligent technologies to help assess damage to vehicles after an accident, help with claims processing and get a whole view of the customer to provide hyper-personalized claim offers. This more complete view of the customer also helps with loan management, loan origination and loan syndication. Insurance technology firms such as Lemonade are using AI throughout the whole process to allow for quicker loan decisions and payments.

Improving regulatory compliance

Intelligent automation tools are increasingly helping organizations to improve compliance, governance, auditability and risk management. Intelligent automation tools can automatically complete compliance or regulatory policies and generate auditable trails. They can also enforce regulatory requirements around personal information sharing, filling out forms and information, and other needs. For companies that face large penalties for noncompliance, this adds a lot of value.

By moving beyond dumb, repetitive automation and making processes more intelligent, these tools are able to automatically find and fix missing or incorrect data, anticipate and mitigate process flow exceptions, or understand UI changes and make dynamic process changes. Industries ranging from financial services to healthcare and life sciences are finding that intelligent process automation use cases are helping them speed time to market for their offerings, streamlining management, improving auditing and monitoring for regulatory processes, providing assistance with marketing and enhancing organizational processes. 

Intelligent automation platforms are powerful additions to the enterprise technology landscape. Because they represent a combination of multiple different, but connected, technology approaches, it makes sense for organizations to take an incremental, step-wise approach to intelligent automation. Getting started with RPA bots is a great first step for organizations looking to clean their data, rethink processes and workflows, and automate tasks. Organizations can then adopt intelligent automation approaches for more advanced business processes. As organizations continue to understand the need to extract value from their data, intelligent automation services will only continue to become more important.

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