Digital transformation is no longer a passive phenomenon, but an active necessity for businesses that wish to thrive in a global competitive environment. At the heart of this transformation lie the optimisation and automation of business processes. Given increasingly complex market demands and rising customer expectations, it is no longer enough simply to understand processes; they must be continuously analysed, optimised and – wherever possible – automated. In this context, technologies such as process mining and various forms of automation – ranging from Robotic Process Automation (RPA) to sophisticated AI-driven agents – are becoming exponentially more important.

Companies are currently at a critical juncture. The trends forecast by industry analysts for the coming years – such as those for BPM 2026 – clearly point towards the convergence of process mining and hyperautomation, complemented by the emerging capabilities of agentic AI. Gartner confirms this development by ranking market leaders in the process optimisation sector, which underlines the maturity and strategic value of these technologies. At the same time, technological advances in areas such as low-code platforms are enabling the implementation of micro-automation, which makes even smaller, repetitive tasks more efficient and thus reduces the likelihood of errors. These developments are not merely theoretical; they are already manifesting themselves in concrete use cases that demonstrate impressive improvements in results. The ability not only to visualise processes but also to transform them in a data-driven manner has become a key success factor.

In today’s business world, companies operate in a dynamic and often unpredictable environment. The complexity of workflows, the interdependence of systems and the interaction between different departments make it difficult to gain an intuitive understanding of the actual process flow. Manual analyses, based on interviews, document reviews or random sampling, quickly reach their limits in this context. They are time-consuming, prone to subjectivity and often fail to capture the full range of possible process variations and their actual characteristics.

Shortcomings of traditional process analyses

Traditional methods of process analysis are often based on a description of the ‘target’ state – that is, the ideal or desired sequence of events. However, this carries the risk that the actual processes – the so-called ‘as-is’ processes – which often deviate from this ideal, remain undetected. Such deviations can arise from inefficient work steps, unexpected detours, system errors or even human error. Without a precise understanding of these deviations, targeted optimisation is virtually impossible.

The role of log data in process detection

The digital traces that every business process leaves behind in IT systems form the basis for a precise and objective process analysis. Every transaction, every click and every status change is logged by the systems. This log data, also known as ‘event logs’, contains essential information such as the time of an activity, the activity itself and the resource involved. By intelligently analysing this data, an accurate representation of the actual process flow can be reconstructed. Technical terms such as ‘event mining’ or ‘process discovery’ describe this process of automatically identifying processes from log data.

From theory to practice: transparency in the supply chain

Particularly in sectors such as logistics, where complex supply chains and a multitude of stakeholders are involved, transparency regarding actual processes is essential. Technologies such as process mining – as demonstrated, for example, by companies like Process.Science at events such as LogMAT 2026 – make it possible to scrutinise the entire value chain down to the finest detail. This includes identifying bottlenecks in real time, predicting potential delays and uncovering inefficiencies that would otherwise be lost in day-to-day operations. By integrating IoT data, this transparency can be further enhanced by incorporating physical operations directly into the process analysis.

Process mining as the foundation for automation

Process mining is more than just an analytical tool; it is the foundation upon which targeted and effective automation strategies can be built. Before companies invest blindly in technology-intensive automation solutions, process mining provides the necessary insight to correct To identify processes and the correct to select automation approaches.

Identification of opportunities for automation

A key aspect of process mining lies in its ability to identify bottlenecks, redundant steps, unexpected loops and significant deviations from the ideal process. These areas typically represent the greatest opportunities for improving efficiency and reducing costs. Through quantitative analysis – for example, of the lead times of individual process steps or the frequency of certain process variants – companies can clearly identify where manual work is not only time-consuming but also prone to errors. Such insights form the direct basis for deciding which processes should be prioritised for automation.

Precision over volume: using RPA in a targeted manner

Robotic Process Automation (RPA) is a powerful tool for automating repetitive, rule-based tasks. However, without prior process analysis, there is a risk that bots will automate ‘poor’ processes, which ultimately fails to deliver significant improvements or may even create new problems. Process mining helps to clarify this by identifying precisely those process steps that are best suited to being carried out by RPA bots. This is particularly true in the manufacturing industry, where an integrated analysis of RPA and process data from ERP and MES systems leads to maximum efficiency by eliminating bottlenecks before automation takes place.

Process optimisation through the interplay of analysis and implementation

Combining process mining with other automation tools, such as RPA or more advanced AI solutions, enables holistic process optimisation. Rather than implementing isolated solutions, a data-driven cycle is established: process mining analyses the current state, identifies areas for improvement and lays the groundwork for automation. RPA or AI-supported algorithms are then used to implement the optimised processes. A well-known practical example of this is the halving of order processing times achieved through such an integrated approach. This iterative process – analysis, optimisation, automation, re-analysis – is crucial for long-term success.

The evolution towards hyperautomation and agentic AI

Technological development is advancing at a rapid pace, and with it the demand for process automation. The industry is evolving from the simple automation of individual tasks towards highly integrated systems that orchestrate entire process chains and are capable of acting and learning independently.

Hyperautomation as a strategic imperative

Hyperautomation is more than just the sum of its parts; it is a strategic approach that aims to automate business processes as far as possible by utilising a combination of various technologies such as artificial intelligence (AI), machine learning (ML), process automation software (such as RPA) and other tools. The aim is to transform not just individual tasks, but entire workflows, from data entry right through to complex decision-making processes. This leads to a drastic reduction in manual tasks, shorter turnaround times and improved compliance with regulatory guidelines.

Agentic AI: Autonomous AI agents are transforming business processes

A particularly promising trend in the field of AI-driven automation is the development of ‘Agentic AI’. These AI systems act as autonomous agents that can understand complex tasks, make decisions and act proactively, often without constant human intervention. In the context of business processes, this means that AI agents not only analyse data, but can also independently carry out optimisations based on these analyses, react to unforeseen events or even initiate new process steps. This approach promises an unprecedented increase in efficiency and a new dimension of agility.

Agentic Process Mining for Process Hygiene 2026

The combination of agentic AI and process mining promises to revolutionise the field of process monitoring and optimisation. ‘Agentic Process Mining’ means that autonomous AI agents continuously monitor process data in real time. These agents can not only detect deviations but also immediately initiate corrective measures to resolve process disruptions before they escalate or have a significant negative impact. This ‘process hygiene’ ensures that processes remain in optimal condition at all times and that organisations can respond proactively to changes – something that is likely to become the norm by 2026.

The specific business benefits: efficiency and cost savings

The implementation of process mining and the associated automation solutions is not an end in itself. It is a strategic investment that pays off through measurable business benefits. These range from significant time and cost savings to increased staff satisfaction and an improved customer experience.

Reduction in manual work and error rates

The automation of manual processes using RPA and AI-powered solutions leads to a drastic reduction in the amount of human labour required. The results are impressive: studies show that error rates can be reduced by 80–85 per cent, whilst time savings on complex tasks range from 30 per cent to 45 per cent. This enables staff to focus on higher-value, strategic tasks rather than dealing with repetitive and error-prone activities. Such a productivity increase of up to 30% is no longer an abstract promise, but a real possibility for businesses.

Faster turnaround times and increased agility

Process optimisation through process mining and automation has a direct impact on the speed at which companies conduct their business. By eliminating bottlenecks, streamlining workflows and intelligently automating decision-making processes, lead times are significantly reduced. This is particularly crucial in highly competitive sectors, where a rapid response to market changes and customer enquiries can make all the difference. Companies become more agile and are better able to adapt flexibly to new circumstances.

Improved compliance and risk minimisation

The detailed analysis and transparent representation of business processes through process mining ensures robustness in relation to compliance requirements. It becomes easier to provide evidence of regulatory compliance and to optimise internal controls. Automation solutions based on clearly defined and analysed processes also reduce the risk of errors and irregularities that could lead to compliance breaches. This is a key factor, as regulatory requirements continue to increase worldwide.

The integration of process mining into existing IT environments

For many companies, SAP is the central nervous system of their business processes. The integration of process mining into SAP environments is therefore of crucial importance. Tools specifically designed to analyse SAP log data make it possible to increase transparency across complex processes such as order-to-cash, procure-to-pay or production processes. Combining this in-depth process knowledge with the targeted use of RPA to automate transactions and data transfers within SAP can lead to significant efficiency gains and cost reductions.

Conclusion: The future of processes is data-driven and intelligent

Tomorrow’s fast-paced business world will be dominated by organisations that not only understand their processes, but also continuously optimise and intelligently automate them. Process mining has established itself as an indispensable technology that lays the analytical foundation for this transformation. It provides the empirical evidence needed to uncover bottlenecks, identify inefficient practices and quantify the actual performance of processes.

The synergy between process mining and advanced automation technologies such as RPA – and, increasingly, agentic AI – opens up entirely new dimensions of efficiency and agility. The emerging era of hyperautomation promises to transform not only routine tasks but entire process chains, leading to significant time and cost savings, minimising error rates and improving compliance with regulatory standards. Companies that proactively embrace these developments and invest in the data-driven optimisation and intelligent automation of their processes will clearly set themselves apart from the competition and lay the foundations for sustainable success in the digital economy. The journey towards an optimal process landscape is a continuous evolution based on transparency, data-driven decision-making and a relentless pursuit of efficiency.

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FAQs

What is process mining?

Process mining is a method for analysing business processes using data from IT systems. It involves analysing event logs to visualise and understand the actual flow of processes.

How does process mining work?

Process mining uses specialised algorithms to identify patterns, deviations and opportunities for optimisation in business processes from the data collected. Various types of process mining techniques are used for this purpose, such as discovery, conformance and enhancement.

What are the benefits of process mining?

Process mining enables companies to gain a better understanding of their business processes, identify weaknesses and optimise process flows. It also facilitates data-driven decision-making and helps to improve efficiency.

What is meant by ‘automation’ in the context of process mining?

Automation in the context of process mining refers to the integration of process mining techniques into automated workflows and systems. This enables process optimisations and adjustments to be implemented automatically.

What role does process mining play in the automation of business processes?

Process mining plays an important role in the automation of business processes, as it forms the basis for identifying, analysing and optimising processes. By combining process mining and automation, organisations can make their processes more efficient and reduce costs.

Improving efficiency through process mining and automation