The RPA rush seems to be the tangible zeitgeist for businesses, but straightforward task automation is not a panacea—and very soon, it may not be enough.

While business owners look at the gains being made by organizations adopting RPA, it is important to remember what RPA functionally is—the automation of routine, predictable, and repetitive tasks. RPA substantially speeds up high-volume, programmatic tasks—and while these tasks are ubiquitous, and automating them can yield substantial gains— tasks exist on a spectrum, from the completely automatable to ones that require human intervention.

The diversity in task types means that beyond routine tasks that can be automated, there still lies significant room for improvement. Process execution is often chaotic, and friction and non-compliance on-ground is often a major source of significant revenue leakage.  By adopting automation in isolation without diagnosing inefficiencies and flawed processes, many companies are missing out on capitalizing on their true potential for business excellence.

On the face of it, this should be avoidable. The meteoric arrival of AI and ML technologies means that businesses have more tools to gain insights into their processes and plan their growth strategically than ever before. Process Analytics and ML algorithms enable you to dissect your business processes—mapping out how their execution looks on the ground—and gain actionable insights to drive compliance and growth. In addition to this, state-of-the-art data ingestion engines enable the extraction of high-volume data for mining and analysis at an unprecedented pace.

But often, organizations default into adopting new technologies in silos—plugging them into individual slots, when what they really need is a blueprint—a personalized roadmap that allows them to define their business objectives and adopt complementary technologies in a way that optimizes business processes comprehensively.

"By 2022, 65% of organizations that deployed robotic process automation will introduce artificial intelligence, including machine learning and natural language processing algorithms."

-Gartner, Moving Beyond RPA towards Hyperautomation

Gartner’s report, Moving Beyond RPA towards Hyperautomation makes it clear that in the next few years, one of the key factors that will distinguish top-performing businesses from the rest is the strategic and holistic integration of new technologies. Companies that transform their business processes through data-driven insights, and ensure that their automation implementation is intentional, will make gains that are much larger than the sum of individual, ad-hoc contributions of hastily-adopted AI and automation.

Competitive, future-ready businesses will inevitably do more— they will look beyond fixing the obvious; they will know more about themselves and their processes.

Cloud4C’s Hyperautomation suite ensures that the first thing you do is define your business objectives—whether they be reducing costs, increasing revenue, or driving productivity. The second step is process diagnostics—looking at the on-ground execution of existing processes, and the degree of compliance with your defined objectives.

This approach puts Process Intelligence at the center of your business transformation. Granular insights into processes allow for you to make incremental changes automating the right tasks in scalable ways; nudging inefficient processes towards being frictionless, and in others—restructuring them to meet objectives.

The integrated approach to business transformation enables Process Analytics and ML to guide strategic RPA implementation and business transformation—ensuring that the tasks and processes you do automate are ones that will help you cover the maximum distance towards the goals you have defined.

In a world rife with complicated problems and convoluted solutions, Cloud4C provides a curated suite of tools for business owners to shape their processes for the future.

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Team Cloud4C
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Team Cloud4C

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