Automation has been used for decades in a wide range of industries to boost efficiency and productivity, reduce waste and ensure quality and safety. And now, we’re ready for the next step. Emerging technologies such as Artificial Intelligence (AI), Natural Language Processing (NLP) and big data analytics are now being combined with automation, to deal with more complex problems and bring further improvements to business processes. This convergence of automation and intelligence is known as hyper automation.
About the author
Sanjay Nambiar Vice President and Head of Wipro HOLMES™ Artificial Intelligence (AI) and Automation Ecosystem, Wipro Limited.
Also known as cognitive or smart automation, hyper automation is at the forefront of the 4th Industrial Revolution and is gradually making its way into every aspect of business, delivering unprecedented results. There are a number of factors driving the adoption of hyper automation among enterprises, including the ability to improve operational and service performance. This technology also gives businesses the ability to improve customer experience and employee engagement, and enables them to unlock new costs savings and develop new revenue streams through analytical insights.
Adopting hyper automation
When adopting hyper automation, it’s essential that businesses recognize the importance of use case selection. This is one of the points highlighted in our recent State of Automation Report 2019, which is designed to provide a way of navigating the complex world of hyper automation. The report points out that use cases for hyper automation should be driven by business rather than by available technology.
When selecting use cases businesses should also consider the security privileges required to execute hyper automation, as well as the alignment of various functions within the enterprise. For example, particular types of mailbox administration automation require admin privileges for the automation program to run effectively. The automation team as well as the service owner for email service needs to be in agreement and aligned before such use cases can be finalized as potential candidates for automation.
Organisations must invest time and energy in creating an effective hyper automation strategy, first ensuring that each use case will actually benefit from it. While RPA (Robotic Process Automation) is now widely used, there are still many tasks that cannot be automated using rules-based systems. Applying AI and cognitive technologies to automation opens up a whole new range of possibilities where more complex information is involved.
implementing hyper automation
When implementing hyper automation, having clearly defined objectives is a must. There are numerous factors that must be considered when designing a new system including security, scalability and maintainability, as well as how easy it will be to train staff to use it. Without a solid use case strategy in place, the project is likely to fail. Not only would this cause time and investment to be wasted, it could also erode trust in hyper automation, causing severe setbacks in the organisation’s digital transformation journey.
The importance of a skilled workforce is also highlighted in the report. For hyper automation to be a success, having the right team in place is vital. As the technology is adopted on a wider scale, demand for talent will only continue to grow. That’s why it’s important for businesses to focus on recruiting the right talent and to invest in continually up-skilling them. Strategic partnerships between enterprises and universities will enable businesses to recruit the best talent straight from university, rather than attempting to recruit them further down the line at prohibitive costs. The ability to work with data will be a must-have skill in the era of hyper automation and recruiting experienced data scientists should be a priority.
Our report also highlights the importance of the acquiring the right data for each use case and ensuring its quality. Without the right amount of quality data, hyper automation projects are bound to fail. While moving towards a data-driven culture, organisations must also make efforts to avoid data bias which could have an impact on intelligence-based automation.
Adding AI to automation
Adding AI to the automation mix, brings complications in terms of legal, ethical and compliance responsibilities. In order to ensure trust, and prepare for any further regulations in the future, organisations should take steps to ensure that AI is explainable. Using collaborative tools which provide automation and cognitive services that can be tailored to specific roles within an enterprise are a great way for businesses to ensure they are staying one step ahead of these complications. The right services can help to provide a framework that is transparent, explainable and which keeps humans in the loop.
It’s important to look for tools that support to de-bias AI models and learning models as this will help alleviate future risks. As we move into the next decade, hyper automation will quickly become the norm for enterprises around the globe. This technology has the potential to trigger large-scale transformation in the very near future. New technology trends, such as edge-based AI, federated learning and synthetic data, will accelerate the pace of hyper automation adoption, not only bringing cost reductions, but also user experience improvements and a positive impact on revenue.
While various forms of automation have been around for decades, adding intelligence to the equation is the next logical step. In the coming months and years, adopting hyper automation will rapidly become a necessity. That’s why navigating the complexities of this technology is an urgent business need for every organisation today. And the faster you begin, the faster your organisation will step into the future.