For the digitisation of business processes, a comprehensive overhaul is not necessary. The mammoth task can be divided into smaller parts, and there are already digital solutions available for each section. These solutions are becoming increasingly powerful and ‘smarter’, while the access and usage requirements are simultaneously decreasing.
Digital Transformation is an unstoppable process that has been underway for years and is accelerating steadily. No longer do companies ask whether they should digitise, but rather how to do so. The good news is that digitalisation does not necessarily have to entail million-dollar investments all at once; it can also be done gradually — process by process and application by application.
The most crucial requirement for success is a change in mindset and a shift in the company’s culture. It is necessary to perceive digitalisation as an opportunity and to involve all stakeholders in this process of change. For most employees, the mindset shift will be, ‘I am not losing something, I am gaining something.’ Others will need to adjust the focus of their work. For all, the significance of their work may be different, but not necessarily less important.
The benefits are apparent: increased efficiency, faster and more accurate decision-making processes, better customer satisfaction, and increased competitiveness. Digitalisation is, therefore, the way to keep up in the market, not the obstacle. It is also an opportunity for small and medium-sized enterprises to develop themselves further.
Multi-year software projects costing millions of dollars can be a massive challenge. However, there is often an alternative: using so-called low-code platforms that reduce requirements while still having a significant impact.
In the following, we present possible application areas for these user-friendly digitalisation tools that can be quickly implemented and have a very high leverage effect. The focus is on essential topics:
- Process optimisation and automation
- Optimisation and insight gained through data preparation
- Digital collaboration
Low-code applications provide companies with the opportunity to create tailor-made applications themselves without requiring extensive knowledge of programming. Using a visual development environment, users can quickly create prototypes and assemble applications based on pre-built blocks. Unlike traditional development processes where each function needs to be programmed by hand, low-code platforms allow for faster application development. Developers and non-developers can collaborate and do not have to spend a significant amount of their time writing code themselves.
Low-code applications
Low-code applications can be used for various purposes, such as automating highly specific processes, analysing business data, creating individually customised workflow management systems, mobile apps, web applications, and more. The added value of these applications is a more efficient and agile way of working, as well as improving customer experiences.
Robotics
One approach to digitally automating processes using low-code applications is RPA: Robotic Process Automation, which automates repetitive tasks. There are various providers on the market with different solutions that differ mainly in their licensing models and technical approaches. Most of them require little to no coding knowledge but, instead, provide a platform where functions and modules can be inserted and assembled into workflows.
All RPA applications have in common that:
- The ‘robots’ are not traditional machines but software robots.
- RPA robots imitate how users interact with applications.
- Robots do not ‘think’ and can initially only perform rule-based tasks. However, most providers allow access to AI functions (such as text recognition from images), which expands their potential application scope.
RPA is a very user-friendly automation method. Anything a person can do on a PC, a software robot can do as well. It only requires a few prerequisites to achieve significant efficiency gains. For instance, the time it takes to program a robot should be less than the time it takes to perform the manual process regularly. However, only simple to moderately complex processes based on consistent rules can be automated.
Here’s an example from practice:
The financial accounting department of a company works with calculations that relate to several hundred individual products in hundreds of separate Excel spreadsheets. The spreadsheets are connected to a database and must be manually updated every two weeks by clicking a button. The given situation does not allow for any changes to be made to this starting point or for temporary staff to be hired. As a result, a trained businesswoman spends two full working days every two weeks opening hundreds of tables, clicking a button, and then closing the saved spreadsheet.
Clicking on buttons can also be done by an RPA robot. After a short development time, it can take over the entire process. The specialist only needs to start the process and can continue working while the robot updates the tables. Specifically, they can work on the tasks for which they were hired as a qualified professional.
The term ‘given situation’ also describes a whole group of other use cases, such as dealing with legacy systems. RPA can bridge the gap between old computer systems, some of which are still in use decades after support has been discontinued, and the company’s already upgraded IT systems. Even if it’s just clicking, copying, and pasting. Click, copy, paste... A software robot doesn't get tired of repetitive tasks, and can reliably transfer the hundredth table cell to the right place in the legacy system.
The opportunity for process automation can also be used for process optimisation. However, RPA already pays off by taking over simple but time-consuming tasks, allowing professionals to use their freed-up capacity for more meaningful work. This not only creates space for innovation and new ideas for the next digitalisation steps but can also prevent the dismissal of frustrated employees.
Collaboration
In the low-code toolbox, there are also platforms available for developing custom apps to digitise collaboration processes. These platforms allow for the creation of complex functions and business logic by linking modules and building blocks without the need to write a single line of code.
An example of such a custom development is a simple project management app. Companies can ensure that features such as task management, resource planning, time tracking, and reporting exactly meet their needs with their developments. The user interface can also be designed to allow for adding tasks and deadlines, tracking project progress, and creating reports without extensive user training. The application's design can be directly tailored to the familiar IT landscape used within the company.
Choosing the low-code approach instead of buying a pre-built solution or developing a solution from scratch brings several advantages:
- Short development time: Low-code applications are quickly available as they are based on pre-built modules and tools.
- Lower costs: Development is not only faster but also simpler and, therefore, cheaper compared to traditional development methods.
- Scalability: As low-code applications are based on pre-built modules, they can be easily extended when new features need to be added.
- Adaptability: Low-code applications are highly flexible and can be tailored to the specific requirements of the business.
With this approach, micro-solutions are also possible that only digitise individual sections of collaboration and would not be worth commissioning in traditional development. This could be a simple task list in which users create, edit, and manage tasks for themselves and others. Such an application could also send notifications when a task is due or when changes have been made.
Another application area for the low-code approach is gaining insights through the analysis of business data. Through analysis tools, non-data scientists can access information from machine learning and AI-driven analyses with a superficial understanding.
Business Analytics
Business Analytics (BA) is a process in which data from various sources is analysed to make data-driven decisions. BA tools serve this process by providing a platform where data preparation, data visualisation, and decision-making based on the data can be integrated.
Each step, from data preparation to presenting a business decision based on the data, is separable within such tools and does not have to be performed by the same highly qualified experts. As in the above example, the specialist in financial accounting who has better things to do than click buttons is relieved of the burden of having to deal with visual data preparation in addition to creating a complex data model and analysing relationships. An undoubtedly important task but one that can also be taken on by people from other departments or even automatically provided by some BA tools.
The integration of AI and machine learning functions into BA tools goes even further. Recognising correlations and influential factors, as well as creating predictions, are tasks that BA tools can already perform for users without deep statistical knowledge. An example could be:
Sales data is the basis for analysis. The BA tool provides a pre-made dashboard template that displays the usual KPIs in a clear manner and only needs to be connected to the company’s data. After customisation, the visualisations also include highlighting of threshold values and their exceeding. A user could then click on a graph showing revenue development and receive an info pop-up with automatically generated information about which sales representative with which product in which region has the greatest impact on the revenue result. Another graph could directly show that the purchase price of a product has exceeded the threshold at which sales are expected to decline if the sales price continues to follow the usual calculation. A possible click on this graph again displays the result of a machine learning analysis: further products that are frequently purchased in conjunction with the original product and have the potential to compensate for the decline in sales.
BA tools enable companies to obtain valuable information and insights from their data with comparatively low investment in software and knowledge, allowing them to make better decisions.
Conclusion and Outlook
Digital transformation is a task that companies cannot simply ignore. It is also not something that is ever completed. Even the most agile tech companies are constantly challenged to recognise developments as unstoppable and react accordingly.
This does not happen without friction. All individuals and functions within companies will need to adapt to their tasks, workflows, and significance in the company, changing at ever shorter intervals.
However, this does not mean that more traditional companies are automatically doomed to fail. Digital tools reduce the demands: AI functions are being integrated into more and more applications and produce better results with the same effort. The low-code approach opens access to software development for a larger group of people. For specific requirements such as image editing or translation, there are already ready-made AI-based applications.
The media’s highly visible chat solution ChatGPT has given this already rapid development another boost, which will have major consequences. Certain professions and specific knowledge will lose significance — even in the field of software development. Others, on the other hand, will become more important. It will be those professions that develop and control these solutions, as well as those who are able to use these tools wisely. All it takes is the courage and will to do so.
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