Robotic Process Automation (RPA) is an emerging technology that uses advanced scripting to learn by watching people perform a repetitive task and then automate the performance of the task directly through the user interface. According to McKinsey and Co., RPA, and other automation technologies, are projected to have a $6.7 trillion economic impact by 2025. The promised benefits of automating repetitive tasks, enhancing data entry accuracy, consistency at any scale, and reducing compliance risk through strict adherence to process and audit-logs make it an obvious choice.
With the rapid adoption of RPA technology across the enterprise, there is a strong case for TBM offices to exploit RPA. Once a TBM initiative moves away from a strategic program to being business as usual, TBM offices face cost pressures. This implies that TBM offices have to deliver more (as expectations have risen) with lesser resources than before. TBM offices can use RPA to alleviate these pressures by automating the mundane data-related tasks to focus on strategic analyses to transform.
Data is the lifeblood of TBM, and its quality is critical to the TBM office’s success. Anecdotally, about 75 percent of the time spent by TBM office resources is on data wrangling issues. TBM Analysts can focus on higher value-added tasks such as insight-hunting and strategic analysis if their productivity can be enhanced by automation. Anecdotally, in the billion-dollar IT spend club, there is an average of 50 or more data sources for getting the cost allocations as per consensus. The severity of this problem increases when international business is involved with Transfer Pricing, Tax, and other regulatory constraints. For as many data sources, the labor required to adhere to the time-sensitive TBM process of monthly book-closing increases significantly. RPA brings a deflationary force for the same.
Intelligent automation releases capital and talent to focus on other priorities of the TBM program, but it is not a silver bullet. To ensure a maximum ROI for automation, the process to be automated should pass a few simple criteria:
In the context of TBM data, there are following considerations as well:
TBM offices can leverage RPA to boost productivity and scale its efforts in following three scenarios:
For data ingestion, the primary modes of data sourcing are:
Candidate strength for the use of RPA for data ingestion mode.
The first two modes merit elaboration as they are eventually a target state for a matured data ingestion process.
As a best practice, the authors recommend that the TBM office must shape the data ingestion process to have only the first mode of data ingestion other than APIs.
Once data in a system TBM, data issues often arise because this is the only place disparate sets of data can be reconciled against each other. There is a strong business case for using RPA to help maintain clean data once it is in the TBM system. RPA can take over the repetitive task of identifying, contacting, and tracking individual users that input bad data into a feeder system- sending thousands of customized messages in a few minutes to a few hours. A task that would normally take a dedicated FTE.
Problems in this space tend to group in three categories:
Only the final problem fits the criteria for being a good RPA candidate, the problem occurs frequently, dealing with it is repetitive, and the problem is expected to be ongoing. If you are able to isolate and report on the errors in your TBM system, you have a strong candidate for automation.
The best way to correct these kinds of errors would be to contact each individual and discuss their problem and guide them to entering the correct data, but such an effort could be extraordinarily time-consuming in large organizations. RPA offers the next best solution by being able to customize each request for the specific audience and issue.
Just as with the prior case for data cleansing, in which RPA is used to contact individuals with custom notifications and instructions for fixing their data, RPA can also be used to enhance customer engagement with the final reporting out of the system.
TBM Systems have deep reporting capabilities, but using the reports requires users to log into the system. Moreover, once a user logs into a system, they have to find the specific information they need by locating the report then selecting the appropriate slicers, filters, and columns for their purpose.
RPA has the ability to enter the TBM System directly and interact with the various filters, slicers and column pickers to generate the report. RPA can then export the now customized report and send it the requesting individual.
The key to making this work is the construction of a table listing all attributes which each user needs to be modified for each report to be used in this way. Any standard table that both a user and the RPA could access would be sufficient for the report, Excel tables and SharePoint lists will be easiest for most organizations.
The table should be structured so that the report to be accessed, individual to receive the report and each filter or column selection should have their own column- essentially a table of filter values. The table would act as both registry of report recipients, and instructions for the bot.
Starting from the initiate signal, the bot would access and read the table, find the report, enter the filters, export the report and then email the requestor.
Nate Bender was TBMA at Exelon, the largest electrical utility in the US by a count of customers served, where designed and implemented Exelon’s cost transparency and consumption-based billing models. He is about to start a new role as Founder for the Distributed Energy Resources, an internal entrepreneurship role dedicated to looking for 10x solutions to customer problems. Prior to Exelon, Bender founded a consulting firm specializing in the design, development, and outsourcing of consumer products. He has a Global MBA from Johns Hopkins University and a BA in East Asian Languages from the University of Maryland
Manik Patil is about to start in his new role of Modernization Evangelist at American International Group (AIG)*. Prior to that, he was a Global Senior Director at AIG, a Fortune 100 Global Company, where he led Technology Business Management efforts for over $2B Tech Spend. He collaborated with top leaders in Product Management, Operations, and Finance to develop strategy, set priorities, and drive strategic initiatives. Manik combines his Business Operations expertise with a deep understanding of strategy, AI/ML-driven analytics, and governance to help CxOs plan and manage business transformation. He holds a Masters degree in Management from Carnegie Mellon University and is a principal member of the TBM Council. You can follow him on Twitter.
*Note: The views and opinions expressed in this article are those of the author and do not represent that of his employer.