Treasury departments – and the wider finance function in organisations – often complain that they struggle to get funding for digital transformation. One way to overcome this issue is to turn investment in automation from a cost play to a growth strategy, according to a new report from Capgemini.
Growth in the machine: How financial services can move intelligent automation from cost play to growth strategy, highlights how the financial services industry could add up to $512bn to global revenues by 2020 through ‘intelligent automation’, the right combination of robotics process automation (RPA), artificial intelligence (AI), and business process optimization applied cohesively to achieve business objectives.
The report suggests conversations need to be moved away from pure cost savings and onto growth, focusing on the optimization of processes and associated cost savings. treasury could also point to better financial governance.
The Capgemini report found that, on average, over one-third (35 percent) of financial services firms have seen a 2-5 percent increase in topline growth from automation, with faster time-to-market and improved cross-selling efforts as the key factors that influence gains. Meanwhile, 64 percent of organizations from across different segments have seen improvement in customer satisfaction by more than 60 percent through intelligent automation, according to the report.
The study finds several factors that are preventing organizations from moving beyond proof-of-concept to actually deploying intelligent automation. Some of the key findings in the study include:
- 43 percent of organizations are struggling to establish a clear business case for automation.
- Many organizations are also struggling to persuade leadership to commit to a cohesive intelligent automation strategy (41 percent.)
- Almost half of businesses (48 percent) say they struggle to find the right resources to implement intelligent automation effectively.
- Also, 46 percent said that the lack of an adequate data management strategy hampers progress as AI-based automation algorithms require the right data at sufficient volumes.
The report can be downloaded in full here and below is a handy infographic showcasing some of the key findings.