The heated global competition across various industries puts constant pressure on business profitability and stops companies from being complacent in a comfortable position without looking at optimization and automation levers for too long. Even if they miss it internally, the business cycle and the economic headwinds forces them to do so. With the increased deployment of more robust and operational friendly software at a global scale, there are many optimization opportunities that pops up in the visibility radar of the CEO/COO from the business side and in turn spiral down to the CIO for the technological accommodation. So, all CIOs are in a hurry to approve the RPA projects or under the pressure to create Centre of Excellence (COE) and kick-off Proof-Of-Concept (PoC) projects to adopt RPA.
Before RPA became norm, businesses looked at outsourcing as a strategy to bring a big-bang change to their operational costs. As this option got sufficiently exhausted (not really, but with the slow pace of change and competitive intricacies in "as-a-service" models, it may seem passé) and ruthlessly adopted by their competitors as well, the industry is turning to other big-bang optimization approaches. As always, the theme is automation but this time, under the RPA Camouflage. This leaves us with some questions, , is RPA the end of the automation journey before cognitive / AI capabilities kick-off? What will happen to BPM type automation projects post RPA implementation? As RPA is a surface level fix and effectively creating a successful business case for the next 3 years, what will happen after? When the eco-system further changes with the "as-a-service" models and begins with digital transformation projects, the complexity of the challenge tend to go beyond the manageable limits and there are chances that companies will get stuck again in the same slow pace as with BPM projects due to high investment and delayed delivery times.
As RPA solution providers quite effectively hit the budget pockets of the business side through major effort contributions in the discovery phase, there is a widened gap while collaborating with the IT side. Many RPA projects are delayed because of access to systems, IT infrastructure / architecture reviews, security surprises, procurement, CoE resources, post-deployment support confusions and legal discussions. In some cases, the enterprise software vendor, the RPA service vendor, and the IT support vendor are all different. All of them work out strategies to further add value to their engagement by introducing their versions of legacy service frameworks. This further complicates the RPA decision making authorities to think about a holistic solution. With this myopic view, they are also under pressure to bring a magical change and be in pace with the race of RPA implementation. This leaves us a question of how can CIOs sail through this game by implementing RPA with a long-term service orchestration strategy and not get influenced by the business pressure?
In a complex situation like above, companies need to think beyond the functional quick-win RPA projects and work-out an enterprise level agile automation strategy that effectively addresses the business outcome. They also need to create the right eco-system with a proper mix of partners & capabilities to support them and enable them to have choices and flexibility in the long run. Moreover, the RPA projects should be in tandem with other cognitive / AI enabled solutions such as natural language processing (NLP) and machine learning.
Hence, the companies need to
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