“Organisations gain insights from understanding historic information and finding trends and patterns that enable them to improve their decision-making. But it is essential to be able to act on the insights as the value of analytics can only be realised through execution,” says Wessels. “The execution of analytics creates more information. In turn, this information can be used to gain better insights into the business and drive further analytics and actions. Essentially, the result in the ideal feedback loop that can drastically improve your decisioning.”
Introducing hyper-automation
At its core, hyper-automation supports the rapid creation of digital products. Low-code and no-code development ensure businesses are no longer reliant on having specific programming skills or lengthy legacy release cycles. Instead, they are empowered to quickly test ideas and operationalise innovation faster than before.
“In this environment, digital engineers take assets shared by colleagues to construct and operationalise a product or process. Through this, the speed and agility to operationalise and automate feature prominently in the hyper-automation cycle,” says Wessels.
Of course, the value of the analytics being performed is influenced by the quality of the insights that a company’s team can create. If the team is empowered to go beyond the typical descriptive insight into the realm of predictive and prescriptive analytics, significant growth opportunities can be unlocked.
“This requires a framework designed to enable the analytical process. Fuelling this is having teams collaborate to reduce the amount of information that must be reworked. By sharing information, efficiencies can be unlocked,” adds Wessels.
Making hyper-automation real
Typically, there are three phases to hyper-automation. Wessels indicates that the first is built on reducing costs. Hyper-automation can potentially reduce the operational costs of businesses by up to 30%.
“Secondly, hyper-automation will bring with it increased productivity in the workforce. If employees are free from repetitive tasks, they can achieve greater overall output and increase productivity. With this comes the third phase – that of innovation. Hyper-automation creates and sustains competitive advantage by offering customers better products, services, and overall experience,” Wessels says.
Hyper-automation is therefore not just about adding AI to automation. It assists organisations and their teams simultaneously develop and compose solutions before testing while operationalising successful innovations.
Automating analytics
“There are several steps to consider when it comes to introducing intelligent automation in the business. Companies must start with a business case if they are to understand the value and effort involved in delivering automation projects. Companies should consider using a prioritisation matrix to select their most impactful initiatives,” says Wessels.
From an analytic enablement perspective, decision-makers must understand the processes required to drive analytical excellence in their organisation, as well as the team they have on hand to realise these ambitions. Collaboration is therefore key to enable analytic leaders to tap into contributors from a wider business.
The intentional creation of a learning loop is also important, as this gives the company the means to understand and measure the effort and return on investment for its analytical projects. Throughout all this, a culture of continuous improvement must be embraced by the business if it is to create sustainable business value through intelligent automation.
“Hyper-automation is not the latest technology fad. Instead, it will continue to build momentum as more companies look for ways to innovate and protect their margins in a competitive industry. Hyper-automation, deployed across multiple organisations and shared infrastructure, could radically transform how services are designed and delivered,” concludes Wessels.
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