Is Hyperautomation Just Hype Or Could It Help Companies Innovate Their Way Out Of Recession?
By John Appleby, Avantra
It's been almost a year since Gartner declared hyperautomation (automating as many processes as possible) as one of its top trends for 2022. It was undoubtedly a reflection of the economic climate at the time. Pulling away from the strings of Covid was imperative. Investment in securing the supply chain and creating more efficiencies to manage costs were being prioritized and, as Gartner evangelized, automation was, and is, one of the most effective ways to achieve both goals.
Fast forward to today and the need to achieve these outcomes is even greater as inflation and interest rates hamper growth. However, speaking to senior leaders, defining what automation means to their business, and therefore, how they should invest, is a complex puzzle.
Many are perplexed by the array of technologies available and how they can define a business case with a material return on investment (ROI). Throw hyperautomation into the mix and the conundrum deepens, usually because there’s nervousness around the hype. The investments that companies make now must deliver.
Yet, hyperautomation is delivering for many businesses and there’s much to learn from their success. Perhaps the most important lesson is that they have all defined what hyperautomation means to their business, their vision, and their strategy.
Gartner defines hyperautomation as a ‘business driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible.’ This definition can be used as a framework for change. In effect, a hyperautomation strategy applies the best suited and most mature technology to processes that could and should be automated for efficiency gains. It might include robotics to do the heavy lifting on a supply chain, but it could just as easily include a low or no-code application that supports quality control.
Both automation concepts are applied very differently and will use and create different data sets to achieve their outcome. But both contribute to creating ‘observability,’ something which I believe is the key to hyperautomation.
Take a robot on the supply chain, lifting a widget and adding another to it to create a useful part. When this exercise is repeated, data is accrued. This data can be used to identify bottlenecks in the supply chain, such as periods of the day when the speed to create slows down, or times when intervention is required – such as when the quality of the raw material from a different supplier was inferior.
If you know these things are likely, then you can do something about them before they become a costly problem. What’s more, you can use ‘observability’ to ensure that urgent and important tasks are not impacted by ‘glitches’ in the system.
It’s a form of ‘AIOps,’ and can act as a foundation for automation, whereby a record of what is happening is used to identify incremental gains and a reduction in errors in one area of the business. It’s this application of data and insight that justifies the business case for hyperautomation.
Not only that, but it can spur automation in another function. I’ve seen IT teams apply automation in their function and use it to help justify the application of machine learning to finance teams for the reconciliation of purchase orders. Knowing precisely what’s on hand and what’s on order helps create a better customer experience (you can’t sell what you don’t have) and a more profitable one (you buy more stock when you need to).
This helps improve cash forecasting and, in turn, releases capital needed to invest in other automation projects. Overall, this starts to create a business that is ‘automation first’ in the development of new processes. By creating ‘error-free’ processes, people have more time to do other more strategic and higher-value activities. If you have the ambition to get into new markets, expand the product portfolio, or diversify, none of that will be possible if teams are bogged down in manual, and largely unloved, tasks.
But as utopian as this sounds, there must be checks and balances in place. Take the example of a laptop manufacturer. It might decide to automate processes that ensure production meets demand. But if there are fluctuations in demand, the system may get nervous and over make in anticipation of greater demand in the future. This example highlights the need for observability, alerts, checks, and balances in the workflow that supports the automation, otherwise the cost saving is drained away.
And that’s the bottom line with adopting an automation strategy: none of it is worthwhile if costs can’t be reduced and there’s not a clear ROI. It’s therefore very important to pick the right areas of the business to automate first.
Starting small and building up is a sensible approach. Not only because it helps to prove automation is worth it, but because it encourages a digital and automation-first culture. People will see the value and believe in it and actively seek out ways to make improvements through automation. The innovation curve will grow consequently and given the current economic climate, which must be a critical outcome every board should take seriously.
About The Author
John Appleby leads Avantra as the Chief Executive Officer. Prior to Avantra John served as the global head of DDM/HANA center of excellence at SAP and as the global head of SAP HANA solutions at Bluefin Solutions, subsequently acquired by Mindtree. John is a recognized thought leader in the SAP market and was part of SAP’s mentors’ group. John holds an MA in computer science from the University of Cambridge.