2020 has changed many things in the business world from the way we work to the quick adoption of disruptive technologies, so we are able to face the new challenges ahead. It is then very likely that we will witness a rise in hyper-automation practices in the coming years.
Hyper automation is the intersection of AI and ML with autonomy driven by robotic and cognitive process automation. This approach would help drive digital transformation and innovation within organizations by providing analytical tools and capabilities that promote collaboration, enhance customer experience, and boost productivity.
Hence, we have talked to experts in the industry about the role and the future of hyper-automation.
What is hyper-automation?
Dipanshu Shekhar, Advisor Digital Strategy, starts by defining Hyper-automation as an approach in which organizations push boundaries for automation.
According to him, there are two specific objectives associated with hyper-automation:
- Rapidly identify opportunities for automation and realize a quick return on investment such as a faster payback period of 6-9 months, post-implementation of any automation opportunity
- Expand coverage for automation of business processes to cover not only rule-based activities but activities that require cognitive learning or decision making. This requires using principles of machine learning, packaged software, and even new emerging methodologies like low-code/no-code
Dipanshu also highlights some typical components of Hyper-Automation may include:
- Robotic Process Automation
- Business Process Management
- Artificial Intelligence/and or Machine Learning
- Low-Code and No-Code (Some think this approach will disrupt RPA while others think that it can synergize with RPA)
For Renish Jose, Intelligent Automation Senior Manager at Zensar, Hyper Automation is a paradigm shift in how enterprises work, not just an improvement in operational efficiency. It empowers businesses to support strategic-level business goals and automate processes from end to end.
Renish also notes the difference between automation and hyper-automation:
|Technologies required to perform||Performed by automation tools||Performed by multiple machine learning, packaged software, and automation tools|
|Sophistication of technology||RPA and task-oriented automation||Sophisticated AI-based process automation|
|Outcome||Efficient operations||Smart and efficient operations|
|Degree of coverage||Where relevant: “What processes can we automate?”||All-encompassing: “Everything that can be automated will be automated.”|
|Scope||Is conducted from one platform||Is an ecosystem of platforms, systems, and technologies|
Driving digital transformation and innovation
The typical time span for fully-fledged digital transformation generally takes around 3-4 years. However, with technological disruptions happening on a day-to-day basis, time to market and customer-centricity have become key components for undertaking any digital transformation program.
Indeed, Dipanshu tells me that Hyper-Automation, with its end goal of pushing boundaries of automation (RPA average ~ 30-40%) to almost 70-80% across most of the business processes, can help manage cost while a fully-fledged digital transformation program is going on, which requires significant investment.
According to an Everest report, published in 2018, the combination of Lean and RPA approaches improves average productivity improvement (over baseline) from 60-70% (Lean only approach) to 400-800%. Hence, going full hog on hyper-automation can take this baseline number even higher, which explains the need for leaders to adopt hyper-automation aggressively not only to address cost but also substantially improve efficiency.
Moreover, Renish adds that Hyper-automation results in the creation of a dual twin of the organization (DTO).
Therefore, Hyper-automation can:
- Reduce lead time by completing tasks in a short period of time.
- Help reduce extra workforce which can be placed to address other tasks that need human intervention
- Free top management who can focus on strategies formation
- Fetch great revenue by decreasing cost
- Eliminate risks
- Optimize process, it can industrialize and scale the business
Thus, according to Renish, the ability to improve the quality of employee engagement, customer experience and the need to improve operational and service performances are some of the more prominent drivers of Hyper Automation.
Automation initiatives mainly focus on cost reduction and increase in compliance, Renish tells me.
In addition to those, he continues, the top benefits of Hyper Automation for businesses include:
- Agility: the business doesn’t need to rely on a single technology for automation purposes. Reliance on a suite of tools along with cultural change, enables organizations to achieve scale and flexibility in operations.
- Enabling employees and improving their productivity: automation frees the time of employees so that they can focus on more value-added tasks.
- Improved collaboration: Hyper Automation enables businesses to integrate digital technologies across their processes and legacy systems. With the integration of technologies, stakeholders have better access to data and can communicate seamlessly throughout the organization.
Renish also highlights that hyper-automation leads to fewer costs and better customer experiences by automating audit management, report automation, and adjudication process.
For Dipanshu, here are a few benefits of Hyper-automation:
- Integration: Businesses can integrate digital technologies across their processes and legacy systems. Stakeholders can have real-time access to data and the business can take advantage of a fully-fledged digital transformation program.
- Improved employee productivity: Employees can ensure more of their work gets done with fewer resources and they can improve their delivery of strategic work. Indeed, organizations can improve their average baseline productivity from 60-70% in a lean-only approach to more than 1000-1200% in a Hyper-Automation approach.
- Flexibility: Since the focus is not entirely on RPA but a multitude of approaches for automation, organizations can really achieve economies of scale and provide flexibility in operations.
- ROI improvement and Shorter payback period: ROI increases but payback time decreases. This can help customers realize ROI in a shorter time frame. Indeed, in a hyper-automation approach, the organization will gain more than 6 times growth in cost savings.
… And the challenges
We must understand that automation must always be taken for an optimized process and not for a process that is broken. However, Dipanshu notes, pushing for the acceleration of automation, process improvement sometimes goes in the background. This will not lead to a better outcome as automation of a broken process with errors will only ensure faster generation of errors, not correct errors per se
Hence, organizations must make sure that their hyper-automation program contains a very relevant component of process transformation.
One more drawback, Dipanshu continues, is the lack of acceptance of low-code and no-code as part of hyper-automation. However, an increased push for the adoption of low-code no-code as part of the Hyper-automation umbrella can achieve transformational benefits for the organization as the 2-3 years transformation program timespan can be reduced by 50-60%.
Moreover, according to Renish, here are the key challenges in using AI in automation:
- Training data may be unavailable or may include personal data: Risk of data privacy issues may occur when you share personal data with AI vendors, yet you can’t build everything yourself. Therefore, companies should invest in privacy-enhancing technologies such as data masking.
- Training data creation can be slow. Synthetic data sets can speed up training data generation in some cases.
- Edge cases: In any complex process that is automated with machine learning, there will be cases when humans need to step in. An easy-to-use human-in-the-loop solution is critical for the success of AI in automation
- AI systems may contain biases that are either in training data or embedded into algorithms via prejudiced assumptions.
Renish also underlines the challenges in process simplification:
- Lack of process understanding: Most processes are not well documented. Process mining tools can help organizations understand processes that rely on log files but still important process information such as the content of calls are hard to analyze, creating challenges.
- Specific customer demands: Custom solutions for specific customer needs increase customer satisfaction but reduce maintainability and introduce process complexity. Companies need to smartly trade-off between process simplification and customer satisfaction. A customer with extremely complex requirements may not be the right one to be served with a standardized process serving many other customers.
- Organizational challenges: Avoidance of potential errors and inertia are responsible for most cases of slow adoption of automation. Employees should be empowered to run experiments on sandboxes and digital twins to quickly see the impact and challenges of potential automation technologies.
The future of automation
Renish believes that Hyper Automation is just still in its nascent stage and its future looks promising.
Indeed, he points out that starting the Hyper Automation journey will admittedly require some effort in planning an automation business strategy, finding the complete set of tools to support automation integration, and getting employees on board among the list of other preparatory actions. Once you get the initial work out of the way, you can expect to reap the benefits as soon as you implement intelligent automation.
Hyper Automation will then doubtlessly experience a snowball effect. The more wins companies get with intelligent automation, the more they’ll implement it. Renish emphasizes that the potential of intelligent automation is too strong to chalk up growing adoption to the “just because everyone else is doing it” mentality. Soon everyone will be using it and it will be a standard approach in the digital transformation journey.
Past experiences and hesitations aside, it’s advantageous to recognize that RPA and machine learning technologies are advancing at astounding rates. Therefore, companies that decide to hold off on implementing these technologies will only be able to do so for a limited time before they start to feel the lag as other companies hyper automate and advance.
For Dipanshu, Hyper-Automation is the future of automation. Indeed, since this approach advocates for the use of a host of automation approaches to achieve an optimized and automated process with flexibility built on it and improved time to market, we can push forward with the realization of our goal of Customer centricity and create a solution which really addresses business needs in a time-bound fashion.
Special thanks to Dipanshu Shekhar and Renish Jose for their insights on the topic.