The process works in three stages in an endless loop:
- Discovering and evaluating the opportunities for automation
- Easing the process of automation and executing it
- Leveraging advanced technologies to scale implementation
In the first step, enterprises break down each task into bite-sized pieces that are easily tackled by automation. Process mining, task mining and process analytics help understand the workflow for different tasks—A, then B and C—so they can address each sub-step in turn.
Next comes the first rung of automation with processes like RPA. Such early iterations are important not just to see what they can and cannot achieve but to also develop an automation framework that lays the right foundation. By starting here, employees also understand how automation can augment the work that they do, liberating them to focus on other tasks. At this stage, employees can also use no-code/low-code platforms to create automation processes on their own, without having to lean on IT. Manufacturing workers, for example, can teach a collaborative robot how to perform a simple task simply by tracing its arms along a prescribed path. Similarly, drag-and-drop menus allow workers to create mini automation tasks, much like creating an email filter to automatically archive incoming messages.
Once enterprises have set automation in motion, they will be able to tell where additional technologies such as artificial intelligence (AI), optical character recognition (OCR), natural language processing (NLP) and computer vision can fill in the gaps and scale automation. Especially in asset-driven industries, the deployment of the Internet of Things (IoT) at scale is looking to leverage AI at the edge, close to the source of data, with an expected boom in edge AI, as reported in a recent study by Markets and Markets on the edge AI software market. Understanding the key performance indicators (KPIs) to be measured and configuring the right hardware and software combination to truly realize the potential of IoT is one fruitful task that hyperautomation can deliver.
One of the challenges for enterprises is understanding and breaking down their processes thoroughly enough for automation to do its job. Knowing which technologies to incorporate, and when, is also critical for success. For example, using optical character recognition to read signatures in healthcare proxies can remove that particular (if small) roadblock to automated processing of healthcare forms.