Many companies bridge the gap between applications by relying heavily on manual tasks such as inputting order management data into an enterprise resource planning system. This is not only labour-intensive, it also costs businesses significant amounts of money. Robotic process automation (RPA) endeavours to resolve that.
By using software code to automate these duties, RPA effectively provides companies a solution that is cost-effective and more efficient. Moreover, RPA does not require expensive and time consuming enterprise application integration efforts. It essentially interacts with various applications the way humans do – through the screen user interface. As such, it offers opportunities for companies to boost efficiency and avoid lengthy and costly systems integration projects.
Sanjay Srivastava is senior vice president and Chief Digital Officer for Genpact.
With so much promise, it seems as though every organisation is thinking about implementing RPA. For example, the Cabinet Office recently announced its plans for a £4m project to try and raise awareness of what RPA technology can do for central government. The hype around the technology is at an all-time high, with industry experts declaring RPA as the start of the next industrial age.
However, it can be easy for organisations to be swept up in the momentum. And as a result, one crucial point is often forgotten – many RPA implementations have not yet achieved the promise they sought to deliver.
So, how can you ensure your organisation gets the most out of RPA? Here are my top three tips on how you can make RPA successful in your organisation:
Be digital by design
Quite simply, the best solutions in the world will not deliver the intended results, if they are applied to the wrong problem. It is the projects built on deep and thoughtful design that almost always come out on top.
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All too often, organisations are so focused on getting bots up and running that they dive straight into software configuration and do not properly focus on the preliminary stage of design. For example, most conversations around RPA implementation centre on what the software teams want to use rather than necessarily thinking about which processes they should automate and why.
Analysing the operating model, reviewing the end-to-end processes and picking the right subset to automate with RPA, and designing the automated processes to interlink back into the existing control points on each end are just as important – if not more – as configuring the actual implementation to the unique step-by-step process requirements.
Think holistically
RPA often ends up being but one component in a broader enterprise digital transformation. Companies need other technologies, such as machine learning and natural language processing, to create actionable insights which can drive business outcomes. A typical automation effort requires a set of complimentary component technologies that together address the entire objective. Planning this early and fully is a clear indicator for success.
Without a holistic, strategic technology roadmap in place, teams can struggle through the RPA implementation only to realise, once they get to the end, it is just the start of the next stage. For example, the RPA implementation could lead to applying machine learning to the new set of digitised data. And once that is done, the next stage could then become applying conversational AI agents that can use next best action insights from the machine-learned patterns, to automatically answer incoming queries. As such, what started as a sprint to the finish line with RPA quickly turns into a multistage relay race that the business wasn’t prepared for.
Stay in control
RPA implementations cannot simply set bots up and leave them to run in the background, autonomous of human interaction. They need constant management and maintenance to ensure software updates, security patches, and process changes for example, are executed properly.
Moreover, ever evolving data formats needs to be managed. Machine learning learns from data but what happens if that data is biased and who checks for that bias? Organisations need to ensure that machine learning is not only optimised but it is optimised in the right data set. Chatbots provide an interesting example as well. What if chatbots pick up on the inappropriate language used by frustrated customers and start to use it in interactions with other customers? This would have a detrimental impact on brand reputation and customer experience.
With all these moving parts, governance with a command and control hub is invaluable for an organisation to successfully deploy RPA at scale and automate mission-critical processes. Proper command and control also addresses errors in design. For instance, when the original design does not integrate enterprise password policies, and passwords subsequently change, the bots can no longer log into the application and the automation comes to a halt. In these cases, governance dashboards can bring visibility to the issue so that it can be quickly addressed.
The secrets to success
There can be no denying that RPA can deliver great results if implemented well. But RPA must be regarded as just one part of the digital journey. On the road to digital transformation, bots may be the first stop but there are many other stops on the way.
Therefore, organisations that combine RPA with a broader set of digital tools, and realise the importance of design in implementation will benefit from stronger business outcomes. The opportunity is there for the taking. CIOs need to learn from the implementations that worked and follow the necessary steps to ensure their business sets itself up for success.
