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Organizations are ruled by business processes. Some of these business processes are obvious and explicit, such as the processes by which customers buy products or companies procure supplies. Others are more implicit, such as those processes involved in employee management or IT service administration.
Many of these business processes are encoded into various computer systems and enterprise software packages that can help to prod these activities along. Many more are simply encoded in the logic of people talking to other people or, heaven forbid, they are paper-based processes, which still rule many business operations.
But AI process automation may simplify some workflows by improving the existing automated processes and enabling a wider class of processes to be encoded into systems so they can be effectively managed, improved and made more efficient.
Enabling compliance and improving regulatory workflows
Enterprises are implementing AI process automation tools to improve compliance with ever-changing regulatory and compliance requirements. In the past, organizations had to identify all of their data stores that dealt with information covered by regulations, such as patient data in healthcare systems, financial data for banking and financial regulations, customer data, and personally identifiable information.
Machine learning is now being implemented to automatically tag and identify data that matches those patterns and apply the necessary compliance-oriented information to the data or operations workflow so that they are properly managed.
One of the benefits of autonomous intelligent systems is their ability to keep a constant watchful eye on systems and data. Rather than having to hardcode workflows or integrate with APIs that might not offer full access to data, AI-enabled process flows can simply observe the data and workflows as they actually happen between systems and act on those that match identified patterns.
For example, every time a customer's private information is exchanged, it can be flagged for review, automatically logged and encrypted, or otherwise acted upon as necessary. These AI-enabled systems keep a watchful eye over networks and employees for disallowed activities and assist with performing background checks and time-consuming fraud investigations.
Keeping data clean, verified and complete
Enterprises are also employing AI process automation systems to keep data clean, handle data integration tasks, review mismatched data and augment missing information. Traditionally, organizations have used robotic process automation (RPA) tools to handle the problem of swivel chair integration; that is, humans manually keying in or extracting information from multiple systems to accomplish a task.
Many customer, employee, patient, constituent, or supplier-facing personnel have to deal with entering or extracting information from multiple systems to accomplish their tasks. While some efforts have been made to connect these systems using APIs or integration middleware, these systems are often implemented or created by third parties and, therefore, simply won't talk to each other. RPA tools emerged as a way to quickly and cost-effectively automate the tasks of multiple system data entry or extraction.
However, just like factory assembly robots, many of these software bots are not imbued with intelligence; they are just automating tasks defined by humans and executing those tasks repeatedly without variability. AI and machine learning are giving these bots much-needed intelligence. Rather than simply entering or extracting data as instructed, computer vision and natural language processing can convert images and documents into machine-readable text and data that these bots can operate on.
These AI process automation systems can process high volumes of data from a wide range of structured and unstructured sources in different formats and languages. The machine learning systems can then keep tabs on information, data and systems and notify users when anomalous activities happen.
In this way, these machine-learning based systems can identify mismatched data that comes from different sources, fill gaps in data by tapping into additional systems and verify that the data meets the requirements for the particular task at hand. These AI-enabled bots augment human agents and provide an extra hand to make sure that data mistakes or missing information doesn't propagate through the organization.
Improving customer service operations
Besides simply smoothing the integration and interaction processes between multiple online systems, intelligent, AI-enabled business process systems are helping organizations better serve their customers, partners, suppliers and employees. Existing business processes can be very rigid and may not respond in an agile or even satisfactory manner to the needs of an organization's various stakeholders.
Employees complain about procurement systems that are overly complicated when performing tasks like setting up new vendors or efficiently handling procurement requests. Customers complain about support reps being unable to deviate from business processes that can make things like refunds or specific requests very difficult to handle. Business partners also complain that their vendor systems are too inflexible to handle continuously changing business requirements.
AI process automation systems are helping to address all these issues by moving beyond rigidly defined workflows and learning generally acceptable behavior patterns in processes, eliminating the need for humans to approve exceptions.
These machine learning-enabled helpers automatically handle process mitigation and the changes that meet overall business requirements without having to shift from computer-controlled systems to human-involved operations. Soon, customers and employees won't ask to speak to your manager and will instead ask the AI system to approve modifications to help keep things humming along.
Moving from rigid automation to intelligent autonomous process
Rather than simply automating business processes, AI systems are poised to greatly advance the state of enterprise processes by enhancing them with real intelligence. AI is allowing organizations to move beyond automation to build truly intelligent systems that can respond autonomously to continuous change. This evolving field of AI-enabled process management is called autonomous business process (ABP), and it is an in which enterprises are starting to invest more of their time and attention.
Instead of human-defined bots that simply repeat existing workflows and business processes over and over, AI-enabled ABP systems are capable of autonomously discovering existing process flows, handling process exceptions and mitigations, and extracting or inputting data as necessary based on learned patterns.
While the vendor space for ABP solutions is still nascent, this is currently the edge of innovation to which enterprises are looking to move their business processes.