Big hopes for the ROI of automation projects don’t always pan out. Here’s how to gain a clearer picture of if your goals are in sight.
Organizations considering a shift to cloud-native intelligent automation solutions — robotic process automation and artificial intelligence technologies whose use is designed to transform their businesses — often cite a number of goals when embracing the new technology. Some focus on improved operational efficiency and an enhanced customer experience. Others emphasize the ability to improve quality control and increase internal innovation.
Regardless of which goals are ultimately realized, two driving forces are almost always at the foundation of their decision-making: Will this solution help the organization make more money? And can the solution lower existing costs? If the answer to either (or both) of these questions is yes, the company will typically move forward — and with good reason. Lowering costs and making more money by automating manual tasks positively impacts the bottom line. It also enables the business to reallocate resources (including manpower) to other, more meaningful work.
The Tip of the Automation ROI Iceberg
All of that sounds relatively straightforward. The problem for many businesses, though, comes when they attempt to calculate their return on investment in intelligent automation solutions. Too often, organizations calculate ROI by estimating the cost of building out a new automation, the cost to maintain that automation, and then the estimated time savings provided by automating what once was a manual task. While this sounds logical, numerous factors can complicate this formula and cause company leadership to question whether the decision to turn to intelligent automation solutions is really delivering the kind of ROI originally envisioned.
To properly determine ROI for intelligent automation projects, organizations should start by considering the impact of redundant automations. Many companies fall into the trap of calculating their automation success by focusing solely on an individual bot and how much money and time using that single bot can save the organization.
Unfortunately, this kind of approach fails to take into account the need for a solid governance structure. Lack of governance can leave companies facing redundant or poor quality automations, as well as a host of other issues, all of which will cost them money they weren’t counting on spending (and in some cases, aren’t even fully aware of). On average, anywhere from 20-30% of the entire automation estate for most established companies is redundant.
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Digging Deeper Into the True Costs of Automation Projects
Redundant automations, however, are only the tip of the ROI iceberg. High maintenance costs, coupled with costs to repair or replace parts, can completely undermine a once successful automation. It is also one of the costs that organizations typically underestimate at the beginning of a new automation project because, frankly, it is not easy to calculate. Companies need to go into an intelligent automation project recognizing that when — not if — it runs into errors or stops working altogether, it is not simply a matter of replacing a part. Complex automations can get very pricey very quickly given the high technical skills needed to get them back up and running with as little interruption to the process as possible.
The cost for technical skills also factors into RPA design and compliance and, as such, can significantly impact the automation’s overall ROI. While most organizations recognize — and can readily calculate — the cost for a developer to actually build a bot, it is much harder to estimate the costs involved in learning about the process to be automated, designing the automation and making any needed revisions, assessing and accounting for any attendant security and compliance risks, and reviewing the automation with all stakeholders to assure buy-in. In short, design and compliance can be highly time-intensive and costs can easily get out of hand. As a result, organizations need to obtain solid cost estimates upfront and keep tabs on any additional charges being incurred. Doing so will enable them to assure there are no surprise costs and all charges can be considered when calculating ROI.
Application licensing fees represent yet another cost that businesses often underestimate or overlook. This is primarily because organizations tend to focus solely on the licensing fee for the RPA platform they are purchasing, ignoring the fact that one of the major cost drivers for any automation is the licensing fees for all software systems that the bot will need to access. So if an intelligent automation needs to access a company’s billing and ERP tools, for example, the company should prepare itself to pay a monthly licensing fee for each of those systems. Even if the cost per bot is relatively low, annual charges can add up quickly for businesses in the process of converting to multiple automations which then must access multiple software packages.
Finally, just like every other business, organizations using intelligent automation solutions must consider the rising cost of doing business and higher costs associated with RPA expansion in their ROI calculations. Starting with the former, the current wave of inflation translates into higher core RPA fees across the board. Add to that the fact that RPA platforms are regularly acquiring new technology, such as process mining tools, and the need to regularly recalculate intelligent automation ROI becomes evident.
Similarly, costs are likely to escalate as a company’s RPA practice grows. The addition of new automations is likely to be accompanied by the need hire new, senior RPA resources, again increasing overall cost. Sadly, many companies fail to account for these rising costs in determining ROI.
Related Article: So Long Automation, Hello Hyperautomation
Keep an Eye on Costs: They’re a Moving Target
Bottom line, organizations must take these and other factors into consideration when determining whether their intelligent automation initiatives are actually producing the ROI they originally anticipated. Moreover, companies must recognize that at least some of these costs represent a moving target. As such, it is essential to account for such factors before undertaking an intelligent automation conversion, and then regularly revisit those initial calculations to make certain they still hold true. Doing so will provide a more accurate determination of ROI and, ultimately, a clearer picture of whether the goals tied to the shift to automation are actually being realized.
