Artificial intelligence (AI) promises outsized gains in business efficiency. And since organizations live or die by their processes, integrating AI into the picture through AI process optimization can help boost ROI with end-to-end process automation and enhancements.
This blog will explore five ways AI can be used to improve and optimize process efficiency. But first, let’s explain what AI process optimization means.
Learn how you can successfully implement an end-to-end process automation strategy in the Process Automation Guide.
AI process optimization refers to using AI and machine learning technologies to improve business process management, support organizational strategies, and meet goals—from analyzing data to automating repetitive tasks to helping team members make better decisions. The goal here is to reduce errors, enhance productivity, and improve operational efficiency.
It’s important to remember that process optimization itself is bigger than just AI. One powerful combination involves using process mining tools to measure process performance, note areas for process improvements, and uncover areas for AI application. Process mining uses data from system logs to gain insight into workflow performance and potential bottlenecks in the real world. Often, this exercise highlights where AI can be applied. For example, process mining might show that employees enter a high-volume of data from incoming purchase orders manually. This would be a great example of an area where AI-driven document processing could help.
Using AI to quickly complete routine, high-volume tasks leads off the list of key AI use cases.
While other technologies must come into play to form a more complete automation strategy, AI absolutely helps with removing some of the manual grunt work employees usually have to perform. This reduces errors and allows them to accomplish more.
Take the previously mentioned example of document processing for example. Businesses receive a ton of paper and electronic documents. Employees often have to process this information via manual data entry into software systems like ticketing solutions or pull data from invoices into billing systems. Intelligent document processing (IDP) can use AI to process these documents and turn them into usable data without manual intervention.
Decision-makers need strong data to make objective decisions. However, AI can assist by providing predictive recommendations based on patterns within a given data set. For example, business analysts could use predictive models to generate simulations to predict how different price changes might affect overall profitability, without having to throw their pricing into the real world and hope for the best. This allows them to set optimal prices without alienating customers or siphoning profits. Another example: AI plays a role in money lending. AI helps loan officers analyze data points across several dimensions to ensure they only provide funds to those who will practice sound financial practices. This cuts down on the amount of time it takes to make decisions—and also helps make better choices in the process.
AI offers the ability to predict scenarios before they happen. Predictive analytics allows your team to build risk models and plan for worst-case scenarios. For instance, a transportation company might be able to use predictive modeling to understand what the effects of natural disasters or weather patterns are likely to be, then decide how best to staff and route deliveries to prevent supply chain disruptions.
This information ties directly to efficiency. When disasters happen, confusion ensues. If you’ve planned wisely, your teams will know what to do and how to execute effectively. In the transportation example, companies could quickly reroute drivers so they can still make their deliveries with minimal disruption.
AI has broad applications for customer service departments. From chatbots that can field basic inquiries to analysis of customer feedback for areas for improvement, AI plays a role in optimizing the customer experience. For example, AI could be used to analyze incoming customer requests, extract important information, then input this information into a ticketing system for a human to triage and handle.
But AI is only one tool in a larger kit. We already mentioned process mining, but other technologies—including automation technologies—can augment AI. For example, robotic process automation (RPA) can fill in forms and enter data that was extracted from a document via AI document processing.
AI is an invaluable tool, but to truly overhaul an end-to-end process, you need more arrows in your quiver. Let’s take a supply chain example. An AI model could analyze purchase data in real time to find anomalies, such as a run on a given product. The retail store purchaser could be alerted via business logic to the problem, then decide whether they need to act on the alert. If they choose to act, they could then enter the desired product amount into an application where an RPA bot connects to a supplier system and orders more. This could save an incredible amount of time.
AI process optimization will be critical for businesses to unblock their processes and teams, reduce errors, avoid risk, and ultimately boost both productivity and operational efficiency. This helps companies remain competitive—and outperform other organizations in their industry.
These five examples are just a small sample of how AI can help businesses. Curious how else AI and other automation technologies can help your organization? Get the eBook, 200 Use Case Ideas for RPA and IDP.