In 2016, the management consulting firm McKinsey predicted that up to 70 percent of all tasks are potentially automatable with so called next generation technologies. Back then, companies world-wide jumped on that bandwagon and invested heavily into one of the hottest topics when it comes to automation – robotics.

Today, only a few months later, some experts claim that robotic process automation (RPA) has only been a fast-paced trend being based on the excitement of industry leaders and that it is not the predicted “panacea” for all the challenges enterprises face regarding automation. Recently, another blog post by McKinsey tackled this topic and qualified the initial enthusiasm for bots and their supposed potential to incur all sorts of back office processes. In fact, the rapid adaptation of robotization waived the consideration of its potential downsides.

According to McKinsey, “installing thousands of bots has taken a lot longer and is more complex than most had hoped it would be” and “not unlike humans, thousands of bots need care and attention—in the form of maintenance, upgrades, and cybersecurity protocols, introducing additional costs and demanding ongoing focus for executives.” All in all, the authors state that the economic results of robotic process automation underperformed the estimations, especially with regards to cost reduction. The impression has been strengthened that “people do many different things, and bots may only address some of them”.

The Next Level of Automation

Considering the latest trends in robotics that came along with unexpected complexity, little flexibility, and additional maintenance, SAP still pushes automation forward helping enterprises to realize their potential and switch focus from just “keeping the lights on” through human manpower to growth generation triggered by automation technology. Our dedicated automation approach to achieve the intelligent enterprise – with SAP S/4HANA as its digital core – involves three interacting levels:

  • SAP engines are SAP software components that provide automation relying on high specific process knowledge.
  • Machine learning is the practice of teaching a computer how to spot patterns and make connections by showing it a massive volume of data. It describes algorithms that can learn from experience without having to be explicitly programmed.
  • Robotic process automation is a third-party software that operates another application without the support of a human user. It helps to run repetitive, rule-based monotone tasks and bridges temporary gaps.

In contrast to the mere RPA approach many companies pursued in the past, we hold the view that only the integration of all three layers lifts the enterprise on the next automation level. Engines are the basis of enterprise automation activities. They enable them to shape their processes by taking decisions on where to direct incoming inquiries at subsequent steps. But engines have a fix logic and a limited configuration possibility. Therefore, they cannot cover all facets of the business processes and only have the potential to facilitate automation in up to 60 percent of all cases.

In credit management for example, credit rule engines can help to evaluate personal creditworthiness and process credit limit applications in a structured way by automatically categorizing them based on defined scoring rules and assigning a specific credit limit to the customer after the examination was completed.

But what happens when a scenario occurs that wasn’t encountered by the operator? Through adding intelligent automation technologies to the automation portfolio, processes become noticeably intelligent. Machine learning can upgrade the automation level of a process up to 98 percent. How? By setting up general guidelines without exactly telling the system what to do. The underlying algorithm learns from the operator’s previous actions and takes all available data into account to deliver the most relevant response to an occurrence.

Applying this to credit management, machine learning is useful in those cases where a customer lacks a dedicated credit history. Here, machine learning fills in with more accurate forecasting models based on people’s overall payment history, on information related to the borrower’s interaction behavior on the lender’s website and other unstructured datasets.

Robotics, as the third automation layer, can help automate the remaining two percent of repetitive, monotone tasks in a process, but due to its lower integration level, RPA is limited on its reach and adds the percentage on top on much higher costs. In financial risk management processes like bank lending, robotics can be used to deal with requests for overdraft protection or credit card approvals.

A Genuine Alternative to Mere Bot Systems

Related to the downsides of bot systems, and in combination with SAP’s multi-automation-layer approach, the cloud-based SAP Leonardo Machine Learning portfolio of digital innovation and its related services are intended to help our customers to set up a stable and holistic automation concept. Thus, it is our endeavor to provide an alternative to pure RPA and opportunities to flatten or avoid the disadvantages bot systems entail by nature.

First, our automation software is easy to integrate into the customer’s SAP system in place. This means our customers can not only rely on a stable software portfolio, but also on an integrated automation strategy including the appropriate solutions by SAP.

Furthermore, SAP relieves the enterprise on their path to automation by handling all incidental maintenance and operation efforts for its customers, especially with respect to our cloud services. The integrated machine learning based software takes care of itself, as it improves on its own and does not cause additional maintenance for the customer.

Finally, as machine learning can imitate human actions by identifying recurring patterns in people’s behavior, a wide variety of back office tasks can be automated, where bots are indeed capable of doing so as well, but where the operating costs and the complexity are much higher. This emphasizes the superordinate standing of machine learning in comparison to robots, and strengthens our strategy to broaden SAP’s positioning in terms of automation. The McKinsey authors support our thesis that robotics should rather be used in exceptional cases, instead of being applied as the universal remedy to deal with repetitive tasks.

All in all, enterprises are heavily searching for ways to shape their processes and automate parts of their work. Robots are perceived as being too inflexible, expensive and complex in their maintenance to accomplish these goals in a satisfactory manner. By expanding the automation portfolio with engines and machine learning, SAP offers a meshing system of automation technology that addresses these concerns and forces a holistic implementation of automation throughout the enterprise.

Currently, companies and CIOs are resetting their bot programs. Figuring out what the desired goal of automation is might help to steer it into the right direction. However, SAP’s automation strategy in general, and our machine learning portfolio in particular, are ready to step in and to fill the automation gaps bots leave.

The SAP automation approach consists of three layers: engines, machine learning, and robotic process automation