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To hyperautomate or not to hyperautomate?

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Long story short, enterprises will always be driven to automate as much as possible. Tasks that are repetitive to the extent that they are the same every single time should be automated. But many more tasks and processes are nuanced; they are not completed the same way every time. Indeed, they may differ in subtle ways daily. AI and ML hold great promise to handle some nuanced situations. Still, at Deep Analysis, where we position ourselves as AI experts, trust us when we say you should never overestimate the so-called intelligence of AI, nor should you underestimate the severe challenges oftraining and managing AI effectively. Hyperautomation sounds excellent in theory if you take the time to dive into its true definition. But at a high level, it’s arguably an unattainable goal.

Moreover, focusing on gaining efficiencies through automation (aka cost cutting) is a shortsighted approach to business improvement and one that often increases costs and complexity over time. Rather, I suggest you focus your automation efforts on making your business more effective over time. That may in turn mean more competent and more human employees rather than fewer. That said, becoming more effec- tive will very likely involve automation technologies, in some cases, to replace humans, in others, to augment their work and support them to make the right decisions.

In building a more effective, ethical, and efficient business through automation, particularly in a challenging economic climate, you must tread cautiously. It’s worth remembering the harsh outsourcing lessons of a decade and more ago: Once you outsource your expertise it is typically impossible to bring it back in-house. So too when you automate a complex work activity, you may have automated it, but whether it is ultimately (not just in the short term) more effective or efficient is a moot point. One way or another, once you have taken the path to automate, there is likely no going back.


Full Gartner Definition: Hyperautomation is a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible. Hyperautomation involves the orchestrated use of multiple technologies, tools, or platforms, including: Artificial intelligence (AI) and Machine learning.

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