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RPA 2.0-Top 5 Contributors for Accelerating the Transformation of Large Enterprises
Jani Rahja, Head of Intelligent Automation, Posti Group
We at Posti Finland have been working with RPA for over two years now. We have benchmarked our operations with many Nordic and European peers. Based on these discussions and learnings from the best, I have summarized them into Top 5 contributors for accelerating RPA and digital transformation in large enterprises.
1. Go for the C-level from the beginning
Four to five years ago, the first companies embarking with RPA most probably did a POC to see if it works. Those of us who started later do not need to POC anymore. The technology works. I have noticed many companies do not see RPA as strategic, but rather someone from Finance or Customer Service has been “allowed” to try it out and build it from there on to other use cases. This approach is absolutely fine, but it might be challenging to promote RPA as a transformational tool afterwards. Another observation, RPA’s KPI might be the number of use cases in production, but that has very little to do with the impact on the entire company. Those who have gained more significant benefits have approached RPA from a strategic point of view, positioning it as a means of transformation. Targets are set high, measured in millions, which in turn forces to go big. If RPA has strong support from the C-level, there’s a better probability of high penetration.
Those who have gained more significant benefits have approached RPA from a strategic point of view, positioning it as a means of transformation
2. Ensure clear ownership
RPA, like many other new technologies, require clear ownership to succeed. Someone who drives it forward takes the responsibility, sets the targets, gathers the resources and keeps oiling the machine. RPA needs to have a face, which is why many companies have someone to take on the evangelist role. There’s a lot of change management and internal sales in driving RPA. People have more trust in another person who can listen to their problems and then offer them solutions to their pain points.
3. Streamline alongside automation
Timewise, the largest share of any RPA case, is most commonly used in finding, evaluating and describing it. All this happens before it even reaches the developer. So it’s logical that companies that already have their processed streamlined would have it more manageable. If process descriptions exist and they’re up to date, it’s faster to go for RPA development. But the real question is: should we first streamline the process and then automate, or the other way around? Naturally, the first makes much more sense. But especially with end-to-end operations, it could take a lot of time to agree on the new, ideal process before the actual automation can begin. Sometimes it’s just easier to automate small first. The most successful companies seem to have joined lean and automation together to ensure both tools are used for process re-engineering.
4. Combine RPA with other technologies
RPA alone cannot lead the digital transformation. It’s the combination of different tools, fit for purpose, that can ensure a high degree of automation. One tool might recognize text from images, another might be able to understand the language and make decisions based on it, and finally, RPA could be the tool to save the data. All this works around data, so combining your data, AI and RPA people might not be a bad idea. Also, you will need some people to investigate new technologies, such as blockchain and IoT, to verify how they would fit into your automation portfolio.
5. Hard work instead of silver bullets
Who wouldn’t want to find that silver bullet which would convince everyone that RPA is the next best thing since the Internet? I hate to disappoint you, but I think it does not exist. Despite RPA being able to provide plenty of benefits, such as saved working hours, increased invoicing and costs avoided, it is as any other new technology: they take time, patience and a lot of hard work. Transformation is a marathon instead of the 100-meter race. Also, no one outside your organization can tell you where the best use cases can be found. You must do the digging, since only you know where the pain points lay.
What does the future hold?
My prediction in a nutshell: the future belongs to citizen developers. As with BI and analytics before, RPA as will be first centralized to gain control and then decentralized to democratize it. The challenge of any new technology is to join it with business understanding. The only way to do this is by giving the tools to those who know it best.