This article by Wayne Ariola was originally published on cio.com
The following RPA drama is playing out across all-too-many Global 2000 enterprises today. The first RPA bots are built and initial wins are celebrated…but then the consultants leave and the exuberance wears off. From that point forward, any little change in the interface, data, process, or environment causes the automation to break. Unable to repair the script-based bots themselves, the process owners depend on consultants or IT to put them back together again. After plummeting into the so-called RPA Death Spiral, some businesses start researching alternatives. Many end up saving the day with a plot twist: they break free from brittle script-based bots and turn to more resilient model-based automation that they can personally control.
For a closer look at how this storyline unfolds, take a look at a few real-life episodes.
Episode 1: Evolving Excel formats result in data inaccuracies
A bank was using Excel as an input data source for SAP. When one of its key partners completed a system upgrade, the export format in their Excel sheets changed—two columns containing the price and total sum were transposed. The bank’s script-based bots relied on the data location to automate the process. Unaware of this column change, the bots continued searching for the same column number and entered the wrong data into the SAP column. Yet, since the automation did not technically “fail” to step through the prescribed tasks, they never suspected a problem…until data inaccuracies were reported. Once the root cause of the problem was uncovered, significant business damage was already done.
Rather than fall into this same trap again, the company shifted their RPA strategy to use Tricentis’ model-based automation. Instead of relying on the data location or column numbers, the replacement bots looked for the unique identifiers of specific information, i.e., the name of the columns such as price and total sum. This not only made their automation immune to subsequent data layout changes; it also restored their trust in the broader RPA initiative.
Episode 2: New form options bring processing to a standstill
At a B2B software company, the sales organization was using RPA to enter customer and prospect data into forms. To better engage customers/prospects with personalized offerings, the marketing department required new sets of information for input: age and profession. The new form was implemented just before their biggest user conference—a time when significantly more data entries were expected. The bots got stuck trying to understand these new fields, and the process failed to complete. With the user conference coming soon, they couldn’t wait for consultants to get them back online. Data had to be entered immediately, so they were forced to hire an agency to manually complete the mounting backlog of work.
After the company switched to Tricentis RPA, the automation team quickly rescanned the process with model-based automation, added these two new fields to the scriptless module, and connected them to the data source in the bot. It took them a mere 30 minutes to repair the problem.
Episode 3: Keeping automation aligned with constantly-changing regulations
A health care provider automated the process of determining patient eligibility for medical treatments. A process like this is highly variable because it follows industry guidelines that are constantly evolving. For example, certain codes that represent patient medical conditions indicate eligibility for full coverage of diagnostic tests, such as chest X-rays or CT scans. As the guidelines change, so must the impacted bots. However, business users could not make even simple “tweaks” on their own due to scripted bot complexity. The experts who knew how to fix them were backlogged with “breaking bot” repair requests—yet regulatory compliance required a much faster response.
After changing course and adopting Tricentis RPA, the business users could make these updates on their own. Each guideline change brought only a brief downtime because people in the claims department could quickly reimplement automation to suit the new requirements.
For RPA to achieve the expected business value, it’s absolutely essential that line of business process owners have a fast, reliable way to keep RPA in sync with the evolving process. That’s why Tricentis RPA taps 15+ years of research and development in resilient automation to provide the industry’s most resilient bots. With our model-based automation, bots don’t break easily—they rapidly adapt to application changes. We solve the most significant challenges affecting RPA implementation today: high RPA downtime and lack of skilled resources required for bot creation and maintenance.
Learn more about our unique approach to resilient RPA bots in Resilient RPA with Tricentis Model-based Automation.