Build a digital operations toolbox to streamline business processes with hyperautomation – TechCrunch


Single dependency technology as a lifeline is a futile battle now. When simple automation no longer does the trick, end-to-end automation requires a combination of complementary technologies that can give business processes a facelift: the digital operations toolbox.

According to a McKinsey survey, companies that have likely succeeded in their digital transformation efforts have adopted sophisticated technologies such as artificial intelligence, the Internet of Things or machine learning. Businesses can hyperautomatize with the Digital Operations Toolbox, the hub of your digital operations.

The hyper-automation market is booming: analysts predict that by 2025 it will reach around $ 860 billion.

The toolkit is a synchronous blend of intelligent business process management (iBPM), robotic process automation (RPA), process mining, low code, artificial intelligence (AI), machine learning (ML) and a rules engine. Technologies can be optimally combined to achieve the organization’s Key Performance Indicator (KPI) through hyper-automation.

The hyper-automation market is booming: analysts predict that by 2025 it will reach approximately $ 860 billion. Let’s see why.

The purpose of a digital operations toolkit

The Toolbox, the technology treasure chest that it is, helps in three crucial aspects: process automation, orchestration and intelligence.

Process automation: A hyper-automation mindset introduces the world of “automating everything that can be”, be it a process or a task. If anything can be handled by bots or other tech, it should be.

Orchestration: Hyperautomation, in and of itself, adds a layer of orchestration to simple automation. Technologies like intelligent business process management orchestrate the entire process.

Intelligence: Machines can automate repetitive tasks, but they lack the decision-making capabilities of humans. And, to achieve perfect harmony where machines are designed to “think and act” or to acquire cognitive skills, we need AI. The combination of AI, ML, and natural language processing algorithms with analysis propels simple automation to become more cognitive. Instead of just following the if-then rules, technologies help gather insight from data. Decision-making capabilities allow robots to make decisions.

Simple automation versus hyperautomation

Here is a story of evolution from simple automation to hyper-automation with an example: an order-to-cash process.

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