If you’re evaluating enterprise business process automation technologies, it’s important to understand what complete process automation technology can do compared to today’s many automation point solutions. Process automation technologies automate repetitive and manual tasks inside a business process.
But here’s the key point: Enterprises use process automation platforms to optimize complex workflows, such as customer onboarding processes in banking, from end to end, as opposed to simply automating a discrete task inside a workflow. A workflow like the previously mentioned banking example cuts across business functions, multiple software solutions, and on-premises and cloud systems. An automation point solution on its own can’t optimize and speed up end-to-end processes.
Today’s set of business process automation technologies includes robotic process automation (RPA), intelligent document processing (IDP), workflow orchestration, artificial intelligence (AI), system integrations, and business rules. A process automation platform brings together these technologies to help companies optimize full workflows, delivering new speed to organizations. Process automation also plays an important role in continuous improvement efforts.
Each one of these technologies plays a role in freeing up human workers from time-consuming, routine tasks and making workflows more efficient. As some organizations have learned the hard way, a standalone robotic process automation tool only gets you so far. RPA can handle discrete tasks, for example, kicking off a step in a procurement process. But it can’t orchestrate a full procurement process end to end.
For example, RPA can’t handle content extraction or email classification. So if you need to extract large data sets from legacy documents to improve a workflow, you need IDP. Therefore, RPA vs. process automation is not an either/or decision: robotic process automation is just one tool in a holistic process automation strategy.
What are some examples of process automation? Process automation can optimize common workflows that span every industry, such as employee onboarding and customer service request routing. But it also plays a big role in streamlining the most complex, highly regulated processes in industries such as financial services and insurance, for example, by improving the automated underwriting process.
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What do you need to know about why and how to choose process automation technology? Let’s examine key considerations for selecting process automation tools and shaping an intelligent process automation strategy.
If you’re evaluating process automation tools, you may already be familiar with the pain that results when an organization deploys multiple simple automation tools in search of quick wins, then hits a wall when trying to scale the automation effort to meet critical business goals such as faster time to market or improved customer experience.
These disparate automation tools, or “islands of automation,” leave both IT and business leaders frustrated. IT has to deal with problems including:
Business teams, meanwhile, may find that automation point projects create initial wins for specific business systems but don’t enable the overall level of speed or agility across the organization that they want to deliver.
A process automation platform helps organizations avoid those IT problems and make way for faster, more agile processes. One way it does this is by cutting through data silos, which leads to the next process automation consideration . . .
To cut through data silos, consider a process automation platform with data fabric capabilities. A data fabric connects data sets across your organization’s disparate software systems, whether they’re on-premises or in the cloud. It does this by creating a virtualized data layer that sits on top of those systems. With data fabric, users interact with the fabric view; the data stays in its source systems.
Why is that a big deal? Data fabric cuts time for integration design by 30%, deployment by 30%, and maintenance by 70%, according to Gartner’s Top 10 Data and Analytics Trends for 2021.
This unified view of data cuts across thorny data silos and democratizes data access so people can make faster and better data-driven decisions. You can also combine new and legacy data sets in innovative ways, a trend known as composable design. In other words, your data architecture won’t stop your workflow automation progress. Finally, data fabric centralizes data management for improved security and compliance, which is crucial as you scale automation.
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What are the must-have capabilities of process automation technologies? To succeed with end-to-end process automation, focus on four essential areas:
Which emerging automation trends deserve your attention now? Get the Gartner® Hyperautomation 2022 Trends Report.