What problems does RPA

Interview: Robotic Process Automation doesn't solve any problems

Does employee turnover also play a role here? When do the people who wrote the RPA scripts leave?

As such, the scripts are relatively small and easy to read. The mass of scripts creates the problems. You are introducing RPA to save costs and thus employees - for relatively simple activities. When hundreds, if not thousands, of such small scripts are in use, they have to see through all of the changes and understand what they are doing. That costs an enormous amount of time and money and, in the end, doesn't save a lot. The complexity creates an enormous overhead. That's why I'm not a fan of RPA.

In itself, RPA scripts hardly change anything in existing processes. They do not create efficiency per se, but only take on the tasks that people have previously done. You replace employees with a simple automatism. But the logic is still the same. You can be much more efficient if you look at the entire end-to-end decision-making chain - which should be done regularly anyway.

And when the people are no longer there, there is no one to worry about whether the individual steps are still useful. The system then simply bubbles away. The knowledge is lost and nobody cares about the processing chain anymore. The disenchantment with the subject of RPA has already occurred in some companies, but it will become even greater.

Then why do companies still use RPA at all?

RPAs can be created by the business itself. You no longer need IT for this. The tools are very easy to use these days. But you thereby decouple IT from classic IT applications. If IT makes changes, the business may not notice them (and vice versa). So when I use RPA, the question is: “How do I have to position myself more intelligently as a company?” I have to rethink my organization, I have to make sure that the individual parts are fully informed. In my opinion, companies are intelligent when they are organized thematically according to clusters and when both business and IT employees sit in a cluster.

Often, however, RPA is also a KPI question (Key Performance Index = a performance parameter): The number of robots used is a positive parameter and is often used as a yardstick for automation and efficiency. This is where analysts also play a role who, based on this size, say that companies are not using enough automation - just because the number of RPA scripts is too small. I have just explained how useful that is.

So if that's not a promising approach to automation, then what is intelligent automation?

Intelligent automation is created via a digital operations toolbox. So that you use low-code platforms, possibly also an RPA tool (with all its side effects) and a decision management system. The question is: when do you use which technology profitably? Each of these tools has its raison d'etre. But I use decision automation when I have to make complicated decisions, when they often change, when IT and business have to work well together and - and this is the most important point - I want to store knowledge centrally in a system that can be accessed by different applications should. Because the other tools (like RPA) actually can't do that.

An example from the financial world shows why this is important: Here, regulation plays an important role because it is constantly changing (and this also varies by region and country). It would not be wise to store these rules in the individual applications.

It is not wise to store the compliance and regulatory requirements in many individual applications. It is intelligent to map them in a central system that can be accessed by other applications.

Because then I would have to follow up on the changes in all applications every time the legal requirements change and make sure that nothing is forgotten. Above all, this would have to happen in all applications using the same logic and the same tests. And this makes it clear that it is smart to store these rules centrally. Because I only have to wait for them in one place and I can be sure that they will be used everywhere.

From an IT point of view, you are building a central IT service based on a rule technology. The system becomes intelligent because it uses low-code elements and can be modeled graphically (because this also makes it easier to read and understand for business users). In addition, there is analysis and statistical data that the tools should show. This creates the - intelligent - end-to-end view, for which the entire toolbox of automation technologies can be used.

And it's also about storing knowledge. If I automate a lot, at some point the people who had this knowledge will no longer be there (not only through savings, but also through fluctuation). It is therefore important to store the knowledge centrally, in the case of decision management platforms in the form of rules. Ultimately, “normal” applications also store knowledge in the form of process flows.

If you want to set up intelligent automation, what is the best way to go about it? Sure, IT has to set up the system, but what happens next?

The first step is to realize that you want to use a central system. This may seem trivial, but it is the first important decision to make automated decisions a central service. Once you have set up the system, it goes step by step and process by process. You store the rules centrally and use them to generate the automation - also because there is the knowledge that a central service is necessary for this. And even with large projects, you proceed process by process. At the beginning you pick out an example and put it into practice.

Ultimately, this is a question of budget and the money in companies is finite. When you start a project, you will see the first efficiency gains in a month or two. You can use it to finance the next project and so on. In the past, you tried to do everything at once in a large transformation project, but nowadays you do it differently - step by step. It is important to start quickly, to deliver values ​​quickly. For me this is also intelligent automation.

Even if you do not yet have this knowledge - that you need a central decision-making system - you still know that you want to become more efficient and deal with it. And at the end of this consideration, the solution is always a form of automation, at least in part.

So it is primarily the potential for cost savings that is driving the introduction of intelligent automation systems?

The cost is only one side of the equation. The flexibility and speed that such systems make possible can be of considerable benefit. Take the frequently cited example of a loan decision: Nowadays, users can immediately receive a loan approval or rejection via various Internet platforms. He no longer has to go to a bank branch, where various data is queried and a decision is made later. He receives more or less immediately a response as to whether any data is still missing, or an acceptance or rejection straight away.

And that also applies to many other areas of normal life - whether it is online purchases, which advertisements and offers are displayed to me online, which prices I get to see: These are all automated processes that lead to higher sales and less to cost reduction are aligned.

And there are almost an infinite number of other examples where intelligent automation can be used, be it in production and quality assurance, in trade or in administration. There is almost no area in which there is no automation potential ...

... but in which RPA is also often used?

Yes, but it is not just a matter of optimizing individual processes, but of gaining an overview via a central system; to have a system where data converges and in which I can analyze in order to solve possible problems in the long term instead of circumventing them with a small RPA script. Only in a central system can I see how often something is called up and needed. The goal is to automate what is useful and to fix nonsense.

The goal of intelligent automation is to automate what makes sense and to fix nonsense.

Using the statistical analysis of which rules are used frequently or not at all, I can see in a central system what customers actually use and what not and how to optimize and refine my processes - that is also part of intelligent automation.

Are decision management systems also used in support, as is the case with large Internet providers?

Yes of course. Support processes in particular have many central elements. If you call support there, an AI-supported system can very quickly determine the causes of a problem and suggest possible solutions with a few questions. The advantage of a DMS is that you can quickly add new attributes and aspects to the system and integrate them into the models (for example, if the provider delivers new routers to its customers). This is the great advantage of a DMS: You can simply copy the logic for the new router and only have to make minor adjustments.

Another good example is the delivery logistics of large grocery stores: Here, enormous amounts of data arise (inventory data, sales, stocks, delivery times and delivery chains), seasonal fluctuations must be taken into account and the knowledge is often in the heads of the department and logistics managers. Changes to the logistics are the order of the day here. A DMS helps to reduce manual checks, gain more transparency and significantly reduce incorrect deliveries. Our graphic ACTICO modeler is particularly valued by the department heads, as it generates Java code directly, which can be easily integrated into the existing IT landscape.

And here is the decisive point for the selection of a DMS: How easily can I make changes, how clear is the system and how quickly can the adapted models be taken live?

In addition to costs and sales, Gartner also mentions risk minimization for intelligent automation.

There is risk management in many areas of the economy, for example also in production. Machines must not fail here, as this would quickly result in additional costs in the millions. By using machine learning and rules, possible failures can be predicted and avoided, keyword: predictive maintenance. The advantage of a DMS is that the production managers as well as the workers on site can incorporate their knowledge into the control system and upcoming tool changes, for example, automatically flow into a ticket system.

Of course, risk management is also a topic that should not be underestimated for financial institutions. The many regulatory requirements cost financial institutions incredibly large amounts that companies have to spend on adjustments every year. Much is already automated in this area, but the degree of automation varies greatly from institute to institute. Many banks are dealing with hundreds, if not thousands, of applications, all of which have to be checked for changes issued by supervisory authorities such as BaFin or FINMA. Often these applications are also networked with one another and create enormous complexity, which in its entirety even the banks are unlikely to see.

Shouldn't banks then actually have to use a lot more DMS? Or is that utopian?

No, I don't think that's utopian at all, but very realistic. I would strongly recommend building such a system. Because banks should centralize this knowledge and convert it bit by bit. It is utopian to want to convert all systems at once (because that would probably cost billions). But I think it's very realistic to always map new regulatory requirements in a new system and to transfer all decisions bit by bit.