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Is hyper-automation more than hype?
Until recently, organizations grappled with simple, repetitive, and rules-based tasks and processes. The game well and truly changed with the arrival of Robotic Process Automation (RPA), as businesses could reallocate mundane and repetitive tasks to automated machines – thus freeing up the workforce. This made RPA the fastest-growing technology of the global enterprise software market in 2018.
The next wave of developments concerns hyper-automation, which Gartner notes as automation that deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans.
Gartner goes on to predict that the scope of automation over the next few years will evolve, from distinct tasks and transactions based on static and rigid rules, to automating knowledge work. What will these now increased capabilities involve? How will hyper-automation evolve to facilitate this, or is it just hype?
A major trend in hyper-automation currently, concerns the continuous integration of automation across an organization’s operations. This can include continuous development cycles and establishing a CI CD pipeline – a series of steps that must be performed in order to deliver a new version of software.
If you think about state change in hyper-automation, the reason that businesses are becoming more comfortable with this type of complexity is because they have a CI/CD pipeline that orchestrates this test environment.
One thing I have learned is that you constantly need to be testing these automated processes. Ultimately, they’re infinitely complex and things change all the time so unless you do that, you have no assurance that they are always going to work. For example, implementing a framework that randomly and automatically tests a wide range of capabilities every 24 hours, is a good step.
I see this more as an evolution – if your business can become more confident and capable with unassisted RPA then it becomes easier to construct a more complex series of automated processes.
After all, hyper-automation brings together multiple solutions such as deep learning, advanced analytics, machine vision, natural language processing and RPA to automate complex processes, rather than individual tasks. This allows business managers to tackle bigger challenges and focus on a long-term optimization strategy.
One of the reasons our business has benefited from hyper-automation is that it enables us to fully integrate our platform and network via APIs.
We are able to use API endpoints to perform various tasks across our network that typically, in the past, would have been performed by network engineers. For example, our APIs enable customers to receive quotes for point-to-point and multi-point connectivity services or APIs help automate the connection between our network and another network.
While APIs can provide businesses with a common level of abstraction, interoperability can be a challenge. Certainly, when it comes to network automation, API standards will be critical moving forward, particularly for avoiding things like vendor lock-in.
That’s where the adoption of the standardized Sonata APIs can provide real value with network automation and management, and in the long term enable automation between different networks.
The public cloud is behind in this regard because cloud providers and vendors have been slower to introduce API standardization, which is leading to a closed ecosystem.
To accommodate increasingly dispersed hybrid workforces, enterprises shifted workloads to the public cloud which further cemented the appeal of greater business flexibility and agility.
As a result, the management of global networks is fast becoming a key use case for hyper-automation with its ability to impact and improve the provisioning and overall management of networks. There are clear benefits in sophisticated forms of RPA for this. For example, implementing an internal application that uses RPA to manage a catalog of inventory and pricing, which are accessible in near real-time to users. It is also capable of managing the procurement and quotation of provisioning connectivity to new locations
Aside from network management and the need to deliver self-provision network services, hyper-automation has many far-reaching use-cases. The pandemic exacerbated legacy manual processes across multiple industries from finance to customer service, all of which require change. However, it is vital for business leaders to assure employees that machines will not replace humans, rather help bring their value to the fore.
As noted, Gartner predicted that hyper-automation would be one of the top strategic technology trends from 2020 onwards, but that doesn’t necessarily mean that businesses must buy into the hype. The first thing to do to understand whether end-to-end automation can deliver substantial business value, is to create a roadmap that clearly aligns business goals with the automation tools needed to reach them.
It’s also worth noting Gartner suggests considering three key objectives — revenue, costs and risks. According to these parameters, businesses might want to think about which technologies can drive revenue by enhancing customer engagement, increasing output, and automating repetitive tasks. They should then redesign processes to reduce the cost of poor quality and streamline production. Finally, they might need to consider the compliance risks of inefficient processes — for example, feeding parts to a machine manually might be not only inefficient but also risky, and it might therefore violate safety regulations.
A careful consideration of these factors should give businesses a clearer idea of whether or not hyper-automation can significantly boost productivity and give them the competitive edge they might be lacking without it.
Paul Gampe is CTO of Console Connect by PCCW Global
Paul Gampe is CTO of Console Connect by PCCW Global.
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