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How to Capitalize on Robotics: Savings Drivers with Digital Labor

For many of today’s organizations, moving forward with digital labor is no longer a question of if, but when.

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For many of today’s organizations, moving forward with digital labor is no longer a question of if, but when. Companies know they need to jump on this trend as a differentiator, which encompasses robotic process automation and is the application of software technology to automate business processes ranging from transactional swivel-chair activities to more complex strategic undertakings.

However, like any other business decision, a business case needs to be made for digital labor and robotics efforts, which is built first by understanding the investment and financial savings opportunities, says David B. Kirk, PhD, Managing Director, Digital Labor / Robotic Process Automation - Shared Services and Outsourcing Advisory at KPMG. A case can’t be created, he explains, without understanding both the “cost to achieve” and the anticipated benefits – which includes direct cost savings as well as more qualitative benefits, such as improved customer satisfaction.

Digital labor: A financial puzzle

Understanding the investments and expected returns for digital labor is complicated by the fact that no two automation opportunities are the same — that is, your mileage will vary. In addition, digital labor can be categorized into three different classes that require different investments and that provide returns varying not only in magnitude, but also in the drivers that impact those savings. Basic Robotics Process Automation (RPA) leverages capabilities such as workflow, rules engines, and screen scraping/data capture to automate existing manual processes. Enhanced Process Automation leverages additional capabilities to address automation of processes that are less structured and often more specialized. Finally, Cognitive Automation combines advanced technologies such as natural language processing, artificial intelligence, machine learning, and data analytics to mimic human activities.

There are challenges in several of these areas, says Kirk. On the robotic process automation (RPA) end of the spectrum, one is “the simplicity of the deployment which can result in its adoption across the enterprise being explosive and disjointed, resulting in unnecessary expense and missed opportunities,” he says. On the cognitive side, the journey to get there is more complex, requiring proper guidance. “Predicting both the investment and anticipated outcomes is more of an art than a science,” he adds.

In order to solve the digital labor puzzle and glean the right understanding, organizations need to have a plan. “Understand that you need alignment between your opportunity, your appetite for both change and technology, and the skillsets you either have internally or are willing to purchase,” says Kirk. Also, organizations must recognize from the very beginning that digital labor is an enterprise-wide opportunity and is worth an enterprise-wide strategy. 

Opportunities and capabilities of digital labor and automation

One of the biggest opportunities for RPA, which automates repetitive, routine explicit steps, is providing a “quick hit” automation fix for connecting disparate legacy systems together, where a human takes data from one system and then uses that data to perform activities in another system. 

Enhanced process automation is similar, but it adds on other capabilities such as the ability to handle unstructured data, or built-in automations (such as an out-of-the-box knowledge library), as well as capabilities to assist in capturing new knowledge to add to the knowledge base (such as watch and record).  It is most applicable in automating activities in a specific functional area, in which the built-in knowledge can be leveraged, such as in finance or IT.

Cognitive tools are substantially different, he adds. “Those need to be taught about the work they will do, as opposed to programmed, and their future success depends greatly on the success of this training,” he explains.

Foundational and specific savings drivers 

There are certainly some common foundational drivers that will impact the overall success and financial returns of digital labor investment. An important one is executive support, Kirk points out, in order to build an enterprise-wide plan that avoids duplication of investments and promotes best practices to maximize savings.

In addition, governance is critical. “Governance insures participants deliver on the business case and associated savings, leverage the agreed upon tools and methodologies, and follow the risk, compliance and security policies to avoid unnecessary risk and expense downstream,” says Kirk.

There are also specific savings drivers for each class of digital labor, which have “triggers” that identify opportunities for automation and degree of the associated savings impact. For example, in the RPA space, processes that follow well-defined steps, that are prone to human error, suffer from inconsistent execution, have a high execution frequency and require significant human effort to accomplish are likely to provide the most significant impact when automated.                                                  

Next, enhanced process automation tends to be more expensive than basic process automation, but as a result of built-in learning support, savings also tend to increase more greatly over time. Its biggest savings drivers are the availability of industry/process-specific starting knowledge; complex processes; automation expertise and rapidly evolving processes.

Cognitive process automation also is more expensive, but also provides enhanced savings capabilities and can be truly transformative, with savings drivers such as natural language; automation experience; highly regulated domains; and quality source documents.                                                          

Preparing for the digital labor journey to capitalize on savings

How can organizations best prepare for their digital labor journey in terms of capitalizing on savings?  Starting small is key, says Kirk. “We advise our clients to identify an executive sponsor and understand what that means from an enterprise deployment perspective,” he explains, “as that helps define required roadmap activities and challenges.” It can help to pinpoint a handful, or fewer, of processes that are good RPA candidates and canvass the associated automation tools for a good fit for your opportunity and your organization. 

Also, companies should understand, and document, the mission statement for the automation of each process – “It’s not always about pure cost savings,” explains Kirk  – and use these processes as a proof of concept with a well-defined business case.  “As business units see the results of the proof of concept automations, prepare for the onslaught of requests by leveraging a well-defined intake process and centralized governance,” he says.