How to choose RPA software: 10 key factors to consider

Menial tasks rob workers of time they could spend on more productive activities. Done right, RPA can banish bucketfuls of mindless chores. Here’s how to select the right RPA solution for the job.

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RPA (robotic process automation) promises to rescue business users from performing boring, repetitive tasks. Hence the term “robotic”: The nature of the jobs performed by RPA tend to be rote and low-level. Businesses use RPA software to create software bots that perform pre-defined, structured jobs that typically involve filling in electronic forms, processing transactions, or sending messages.

Stitch those basic activities together into fleets of RPA bots, and you have tremendous potential to eliminate drudgery—in data entry, billing, order management, HR onboarding, and endless other areas.

Banks use RPA for due diligence reviews on loans, invoice processing, and customer checks. Sales organizations use RPA to automate quotes and invoices. Insurers use RPA to speed up claim adjudication. In addition, with the help of machine learning, RPA can automatically transcribe recorded conversations, extract text and numbers from images and videos, and populate databases from hand-filled forms.

Under the hood, RPA systems include process mining, bot creation tools, plug-ins for connecting to enterprise systems, and a scheduling or orchestration layer. The tools in RPA systems often have limits, so people sometimes fill those gaps with hand-coded automation scripts.

It’s important to keep expectations in line for what RPA can handle. Purveyors of RPA tend to imply their products contain more intelligence than they actually do, which has led to some disappointment with RPA in general. And rollouts need to be planned and executed carefully to avoid failed RPA deployments. You need to be clear about what you want to automate before you select an RPA product—and ensure you choose one that has the capabilities you need.

How does RPA work?

RPA works by pulling information out of your existing IT systems, either through an interface to the backend or by emulating how a human would access the system from the front end. With legacy enterprise systems, you must often go through the front end, because you can't access the back-end system directly.

Front-end RPA is an evolution of old-fashioned screen scraping. If you've ever used screen scrapers for an extended period, you know that they tend to be fragile: The minute something unusual displays, such as a number too large for its field, or as soon as the display format changes because of a software update, the screen scraper either returns wrong answers or stops working. Machine learning can reduce but not eliminate such showstoppers.

Once the RPA system has extracted the information it needs, it goes on to perform a pre-defined task. Common use cases include applying business rules, generating a report, sending an invoice for a receivable, or generating a check for a payable.

The bots that execute RPA tasks may run attended or unattended. Attended RPA bots run in response to an employee request. Unattended RPA bots run on a schedule—for example, to generate nightly reports. Almost all RPA bots need supervision and periodic auditing to ensure they continue to work properly.

A human must define the workflow for an RPA bot before it can work. This often begins with process recording—an activity not unlike recording a macro, only across multiple systems. The macros analogy extends to writing and editing scripts for bots as well. Many RPA solutions also offer a flowchart-style interface for stringing together elements of a bot’s task, enabling "citizen developers" to define workflows. Some RPA systems, however, still need to be set up by IT.

One of the difficult and time-consuming parts of reproducing existing business processes is identifying what the business processes are and how they work. Some RPA process mining tools can parse the logs from the existing processes; others need to observe and record employees at work. Worst case, this process discovery needs to be done manually.

How to choose an RPA product

Before you commit to an RPA product, you need to understand that every single one of them uses its own proprietary file formats. Despite their utility, they're all roach motels, completely lacking in portability. It's not like they're ignoring the standards: There are no standards. Evaluate carefully and do a proof of concept before committing your company to a rollout, because changing your mind later will be painful and expensive.

Verify that all basic features—and the differentiating features you think you’ll need—work in your environment. Build scripts using all the supplied tools and demonstrate that the orchestration works properly. Test out an unattended bot, verify that bots can parse your unstructured documents and PDFs, and go through process mining procedures.

10 criteria for choosing RPA tools

Pay particular attention to these key factors in your evaluation:

  1. Ease of bot setup
  2. Low-code capabilities
  3. Attended vs. unattended
  4. Machine learning capabilities
  5. Exception handling and human review
  6. Integration with enterprise applications
  7. Orchestration and administration
  8. Cloud bots
  9. Process and task discovery and mining
  10. Scalability

Ease of bot setup. There should be a range of ways to set up a bot for different personas. Business users should be able to point and click the applications they normally use while a recorder takes note of the actions. Citizen developers should be able to use a low-code environment to define bots and business rules. And finally, professional programmers should be able to write real automation code that calls the RPA tool's APIs.

Low-code capabilities. Typically, low-code development is a combination of drag-and-drop timeline construction from a toolbox of actions, filling out property forms, and writing an occasional snippet of code. Writing small amounts of code, for example "loan_amount < 0.20 * annual_income" can be much quicker than graphical methods of specifying a business rule.

Attended vs. unattended. Some bots make sense only if they run on-demand (attended) when a business user needs them to perform a well-defined task—for example, "turn this graphic into text and put it on the clipboard." Other bots make more sense if they run in response to an event (unattended), such as "perform due diligence on each loan application submitted from the website." You need both kinds of bots.

Machine learning capabilities. The RPA tools of just a few years ago had trouble extracting information from unstructured documents—and typically, 80% of a company's information is found in unstructured documents rather than databases. These days, it's common to use RPA machine learning capabilities to parse documents, find the required numbers, and return them to the user. Some vendors and analysts call this hyperautomation, but the fancy language doesn't change the functionality.

Exception handling and human review. Categorical machine learning models typically estimate the probabilities of the possible results. For example, a model to predict loan defaults that returns a 90% probability of default could recommend denying the loan, and one that calculates a 5% probability of default could recommend granting the loan. Somewhere in between those probabilities there's room for human judgment, and the RPA tool should be able to submit the case for review.

Integration with enterprise applications. A bot isn't much good to your company if it can't get information out of your enterprise applications. That's usually easier than parsing PDFs, but you need drivers, plug-ins, and credentials for all your databases, accounting systems, HR systems, and other enterprise applications.

Orchestration and administration. Before you can run any bots, you need to configure them and supply the credentials they need to run, typically in a secure credential store. You also need to authorize users to create and run your bots—and provision your unattended bots to run on specific resources in response to specific events. Finally, you need to monitor the bots and direct exceptions to humans.

Cloud bots. When RPA started out, RPA bots exclusively ran on user desktops and company servers. But as IT estates have grown into the cloud, companies have set up cloud virtual machines for use by bots. Recently, some RPA companies have implemented "cloud-native" bots that run as cloud apps using cloud APIs rather than running on Windows, macOS, or Linux VMs. Even if your company has invested little in cloud applications today, it will eventually, so this capability is highly desirable.

Process and task discovery and mining. Figuring out your processes and prioritizing them for automation is often the most time-consuming part of implementing RPA. The more the RPA vendor's app can help you mine processes from system logs and construct task flows by observation, the easier and quicker it will be to start automating.

Scalability. As your RPA implementation rolls out to the enterprise and handles more automations, you can easily run into scalability issues, especially for unattended bots. A cloud implementation, whether native, in VMs, or in containers, can often mitigate scalability issues, especially if the orchestration component is capable of provisioning additional bots as needed.

Ultimately, the success or failure of your RPA implementation will depend on identifying the highest-reward processes and tasks for automation. For example, if the highest-reward process for a bank is performing due diligence on loan applications, make that (or a key task from that process) your RPA proof of concept.

Don’t cut corners on your testing cycle. If it turns out the RPA solution you’ve adopted has some missing or inadequate capability, and you need to switch, you’re in for a world of hurt. To mitigate the risk of having to re-create all your bots from scratch, you should document all the steps in each task and process. When you change horses, you might still need to spend a week re-implementing each bot, but you can avoid the month you spent figuring out each process.

Key RPA vendors

While there are dozens of RPA vendors, the same handful enter into the discussion again and again. The following seven vendors have been selected from the most current Forrester Wave and Gartner Magic Quadrant analyst reports and arranged alphabetically. Inclusion in this list is not a recommendation and exclusion is not a condemnation:

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