A survey of 580 executives from the banking, credit union and insurance industries in the U.S. finds 91% of respondents spent at least $100,000 on automation in the past 12 months, but only slightly more than half 52% report saving at least $100,000 per year because of investments in automation.
Conducted by SMA Technologies, a provider of an automation platform, the survey also finds more than (55%) spent more than $250,000, while 18% spent more than half a million. A full 92% also said they intend to spend the same, or more, on automation in the next 12 months as they did last year.
In fact, the survey finds there is still a significant gap between how many processes are automated today versus how many could be. The median percentage of operations currently automated is between 41-50%, but the desired level of automation is between 61-70%.
That 20-point gap suggests that even after years of investments in digital business transformation there is still much work to be done, says SMA Technologies CPO Ryan Dimick.
Top areas of planned investments include business process automation (BPA) at 41%, followed closely by intelligent process automation (IPA) at 40%, IT process automation (ITPA) at 39% and workload automation at 38%.
Exactly where organizations are investing in automation depends heavily on where they have made previous investments and which vertical industry segment the organization is in. The financial services sector, for example, tends to be more advanced than any other industry sector, but at the same time continues to invest heavily in automation. “A lot of it has to do with their digital business transformation maturity,” says Dimick.
For example, only 27% of respondents said their organization plans to invest in job schedulers, mainly because they are already widely used by most organizations.
Most organizations today have islands of automation that have been created using various platforms. Very few have an end-to-end approach based on a strategy that is being applied across the enterprise. Most organizations don’t have the budget, time or expertise required to make that level of investment, so there is a natural tendency to focus on individual projects, says Dimick. In fact, the more exceptions there are to a process the more challenging it is to automate, he adds.
Nevertheless, with the rise of generative artificial intelligence (AI) the pace at which automation initiatives are being launched should soon increase, he added.
Less clear is the degree to which those automation efforts will be based on reusing a library of natural language prompts to automate a task versus employing an automation platform. As the underlying large language models (LLMs) gain more robust reasoning capabilities, the range of tasks these platforms can automate will continue to increase. The issue is that the cost of using advanced LLMs to automate a process is, for the immediate future, going to be in many cases more expensive than existing automation frameworks.
Regardless of approach, investments in automation will continue unabated. The challenge, as always, will be determining what type of automation platform should be used to automate a diverse range of processes that span the modern enterprise.
