While robotic process automation (RPA) promises to lower the cost and increase the efficiency and quality of many back-office and customer-facing processes, it's not without its challenges.

Several common mistakes cause as many as 30% to 50% of RPA implementations to fail, according to a 2019 study by consultancy EY. To prevent your next RPA or intelligent automation project from failing, here are four reasons companies aren't receiving the expected return on investment.

1. They automate the wrong process

The process selected for automation during the initial pilot is the leading cause of failure, according to a study by the Shared Services & Outsourcing Network. Another researcher found that not fully understanding the selected process was to blame, which is why choosing the right process matters, according to a study conducted by ABBYY, the company I work for.

Many organizations select "low-hanging-fruit" RPA initiatives without a true analysis of their workflows and how those affect other processes.

Most businesses are stumped by a deceptively simple question: Which are the right processes for automation? Determining where to start with your RPA program is critical to success.

Using advanced process mining and discovery tools to do a thorough analysis of your business processes will give you a "digital twin" of how they currently work and let you know which are best suited for digital transformation. Businesses can then safely select processes that range from those that are rules-based and repetitive in nature to those that are data-intensive and have high error rates.

The main goal of your RPA projects should be to reduce human involvement in labor-intensive tasks that don't require cognitive effort. One example is transferring data from an invoice to an ERP system. That's simply extracting data from a document, classifying it, and inputting it into a business system. However, if pertinent information is missing or mislabeled, the process is broken and the bot will continue to make a mistake or stop working because those exceptions weren't included in the rules. Rushing to target the wrong process can result in delays, additional costs, or abandonment of the project.

2. They focus too much on reducing head count

Leaders and pundits talk about empowering employees by reducing repetitive work, but some enterprises have been selecting projects with reduction of head count in mind. However, the greatest benefit of adopting RPA is that it allows your talent to devote their skills to higher-value tasks. This removes the burden of performing manual operations that contribute little to the organization's growth or the customer experience.

Take the role of compliance officers, for example. Many banks use RPA as a first step toward automating the collection of data from documents, but a compliance officer still must sift through documents and find data needed to make decisions. A better approach would be to have robots with content intelligence quickly read the contracts and pick out relevant data to execute decisions faster.

Companies also too often ignore the fact that they must prepare their knowledge workers to work alongside their digital counterparts by training them in digital skills. A concerning 75% of global enterprises in IDC's Future of Work report said it was difficult to recruit people with digital skills, and 20% cited inadequate worker skills and/or training as a top challenge.

Automation is also a way to address the developer shortage. With no-code platforms that let them drag-and-drop to train bots, teams such as legal, HR, accounts payable, claims, and customer service can augment and improve their work productivity.

3. They use bots that lack content intelligence

Since organizations rely on both structured and unstructured data for all business processes, bots must to be smart enough to "read," "understand," and "make decisions" about the content they're processing. You wouldn't hire a human that couldn't read or understand a document, and you wouldn't hire one who could do only one task.

RPA on its own cannot understand unstructured documents, so you need AI-enabled bots with content intelligence. In this way, bots can carry out tasks such as reading a document; categorizing, routing, extracting, and validating data from it; and doing other tasks related to understanding and processing unstructured content.

Using content intelligence with RPA can speed your processes and ready your organization to add more experiential opportunities to engage with customers via interactive mobile apps, cognitive virtual assistants that combine voice and conversational AI, and chatbots.

4. They fail to monitor after deployment

Don't let your intelligent automation project fail after launch by omitting continuous monitoring.

Many organizations' centers of excellence are using process mining to help businesses keep track of what their bots are doing best and reveal where they could perform better. This approach to process improvement, driven by advanced machine learning and data analytics, can considerably aid enterprises in the effort to optimize their automation. Combined with bots' event logs, this analysis will help identify bottlenecks, inefficiencies, control and data quality issues, and more, to give leaders comprehensive process intelligence.

With monitoring, you can evaluate the performance compared to the original process. Additionally, keeping track of deployed, active bots will help you monitor your KPI so that you can take immediate action if the goals are not being met or you run into issues.

Keep the project focus practical

RPA can be a practical tool to streamline ongoing transformation that touches many dimensions in an organization.

However, it needs to be connected to a broader, more holistic digital intelligence strategy that includes AI and process mining technologies to provide an end-to-end view of your business workflow, as well as the people using them and the content they contain.

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