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What is an example of intelligent automation?
Traditionally, business process management and enterprise automation have taken place at very different levels of business operations.
Using technologies such as process mining, business process management is designed to optimize entire processes that span multiple individual tasks. For instance, the procure-to-pay (P2P) process, includes purchase requisition, purchase order, good receipt, invoice, payment and many more. Business process management aims to streamline the entire process, across all of these tasks to drive overall efficiency.
With enterprise automation, on the other hand, businesses have largely been looking to automate these individual tasks, using technologies such as robotics process automation (RPA). Their goals usually include saving time, reducing errors, and freeing the workforce from repetitive tasks.
Now, with artificial intelligence and machine learning able to bridge the gap between the two, business process management and enterprise automation are coming together in the form of intelligent automation, to reduce operational costs, increase productivity, and drive real business value.
What is intelligent automation?
Intelligent automation, also known as intelligent process automation (IPA) or cognitive automation, is often thought of as simply applying AI technologies (such as machine learning and computer vision) to business process automation. This expands potential applications because it means an automation tool can run not just on structured data, but also on unstructured data, interpreted by artificial intelligence.
At Celonis we know real intelligent automation requires not just AI, but also a layer of process intelligence that can help AI speak the language of the business and understand how it operates end-to-end. Without this process intelligence, implementing intelligent automation technology is unlikely to deliver the intended results and can actually have adverse effects.
There are three key elements to intelligent automation:
Process intelligence: the insights from process mining are layered with standardized process knowledge and AI to create process intelligence – a real understanding of how a business operates and how processes work and relate to each other across every department and system. When process intelligence is used to inform AI tools for business, it provides those tools with vital context and situational awareness to help with smart decision making.
Artificial intelligence: the decision engine of intelligent automation, AI and machine learning use process intelligence to understand what’s happened in the past, predict what will happen in the future, and make decisions based on that information. AI can then trigger automation scenarios to activate those decisions.
Robotic process automation: Once AI has identified what action needs to be taken, it can trigger task automations through an RPA tool to complete that action quickly, and without manual intervention or the risk of human error. While RPA bots are often used to automate routine, manual tasks, AI input enables them to handle more complex tasks and use cases.
Examples of intelligent automation
There are endless use cases for an intelligent automation solution. Below are three examples of intelligent automation in action across a variety of industries.
1. Intelligent document processing in insurance
Insurance is a document-heavy industry. From underwriting and policy management to claims handling, every process requires data to be extracted from multiple documents. Manually processing these documents is labor intensive, but automating data entry is problematic because they come from a variety of sources, and are in different formats.
Intelligent automation streamlines document processing in the insurance industry. When supported by process intelligence, technologies such as machine learning and natural language processing (NLP) enable data to be automatically extracted from documents and applied appropriately.
The result is quicker customer onboarding, faster claims resolution, seamless policy updates, and better resource allocation. It enables more satisfied employees to focus on value-add tasks like risk assessment rather than data entry.
Find out how Austrian insurance group UNIQA harmonizes its claims handling process with Celonis, and aims to use Action Flows to proactively respond to urgent claims.
2. Customer satisfaction improvement in retail
Today’s retail customers expect seamless, efficient experiences across both physical and digital channels. Intelligent automation can help to deliver these experiences, improve customer service, and ultimately provide a competitive advantage for the company, in a variety of ways.
When used in the product development or manufacturing processes, artificial intelligence can get a higher quality, better value, more reliable, or more sustainable product to market faster. When used in customer service it can:
Help customers self-serve through conversational AI
Speedily resolve enquiries
Provide real-time visibility into order status
Deliver proactive information about potential issues
Luxury fashion house Max Mara recognized the value of an intelligent automation platform when the digital share of its business nearly tripled during the pandemic. By automating the processing of customer post-sales support enquiries the retailer was able to achieve a 90% improvement in customer service resolution times and a 46% reduction in average cost per resolution.
In another example, electronics retailer Conrad Electronics aimed to reduce late deliveries, along with the order cancellations that resulted from them. Through intelligent automation in the form of Celonis Action Flows, employees are now automatically notified about orders at risk. They are also given intelligent recommendations around what to do next to ensure a better customer experience – perhaps resolving an order block for a customer with a spotless credit history.
Intelligent automation means the retailer can prioritize urgent orders, meet logistical cut-off times, and deliver on customer promises. By increasing the order automation rate by 54%, Conrad Electronics was able to reduce blocked orders by 30% and lower the order rejection rate from 2.5% to 1.8%.
As Jörg Frenzel, Director of Operational Excellence at Conrad Electronics SE explains: “More than ever, our customers are getting their packages on-time. That’s an incredible success for us.”
3. Regulatory compliance in healthcare
Industries like healthcare and financial services are subject to stringent and continually evolving regulations. Intelligent automation is an effective way to improve the accuracy and consistency of regulatory compliance by reducing the risk of human error and identifying patterns or anomalies that may indicate a potential compliance issue.
In the healthcare industry, for example, organizations have to comply with laws like the Health Insurance Portability and Accountability Act (HIPPA). This protects sensitive patient health information (PHI) from being disclosed without the patient’s consent or knowledge. Intelligent automation can be used to monitor access to PHI, logging and tracking every action. It can identify unusual patterns and predict when a data breach is likely to occur, meaning automated, preventative action can be taken to remain compliant.
Find out how pharmaceutical service provider Vetter uses Celonis, in conjunction with the AI-enabled Trackwise® quality management system, to intelligently improve its change control and deviation management processes.
Getting started with intelligent automation
Jumping into AI process automation can be high risk if organizations haven’t first addressed their underlying processes. Automating sub-optimal processes can mean magnifying inefficiencies or having a negative impact elsewhere in the organization where processes intersect.
Using technologies such as process mining to understand how processes actually run, not just how they are supposed to run, is the first step. Then using the data from process mining to create the process intelligence necessary to feed intelligent automation tools is the next.
As long as these critical foundations are in place, artificial intelligence can bridge the gap between business process management and enterprise automation and help to drive real business value.
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