The biggest misconception of German managers: Why “optimize first, then automate” paralyzes your company
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Published on: November 4, 2025 / Updated on: November 4, 2025 – Author: Konrad Wolfenstein
The biggest misconception of German managers: Why “optimize first, then automate” paralyzes your company – Image: Xpert.Digital
Are you waiting for the perfect process? This mistake will cost you more than you think.
Automation: The truth many companies ignore – and what really works
A guiding principle that acts like an immutable law in the German business world is: "Optimize the process first, then implement the technology." This seemingly logical and risk-averse approach dominates discussions about digitalization and is often praised as a mantra for sound corporate management. However, this sequential way of thinking is not only outdated—it is increasingly becoming one of the biggest obstacles to the competitiveness of companies in the digital age. It is based on a fundamental misunderstanding of how modern automation works and what its effects are.
This article explains why the strict separation of process optimization and automation is a false dichotomy. We show that waiting for the "perfect" manual process traps companies in a static state that wastes valuable time, delays return on investment (ROI), exacerbates skills shortages, and builds up massive technical debt. Instead of a rigid sequence, we advocate a parallel approach where process improvement and automation planning go hand in hand from the outset. Using principles like simultaneous engineering, modern methods such as process mining, and agile methodologies, it becomes clear: true efficiency and future viability arise not from waiting, but from intelligent and simultaneous thinking in processes and systems. It's time for a new mantra in digital transformation.
Sometimes it takes a clear impetus to challenge an entrenched way of thinking. This came recently in the form of a LinkedIn post by Marco Gebhardt, Managing Director of GEBHARDT Intralogistics Group GmbH. His open frustration, summed up in the sentence, "I can't stand reading it anymore: 'You have to get your processes under control before you automate.'" was the trigger for this article. This statement puts its finger on one of the biggest myths of digital transformation and is the perfect starting point to examine why this approach is no longer up-to-date.
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Process optimization and automation in parallel: The false dichotomy in German business culture
Why sequential thinking slows companies down – and what actually works
The debate surrounding the order of process optimization and automation is currently raging in German-speaking professional circles. One axiom dominates the LinkedIn discussions: manual processes must run perfectly first, only then should technology be implemented. This mantra seems intuitively correct, compellingly logical, and economically conservative. It's not entirely wrong, either. However, a deeper analysis reveals that this approach leads to a fundamental misunderstanding of the nature of automation projects and systematically hinders companies' competitiveness.
The static trap: When processes are thought of as unchanging
The central problem lies in the fact that this approach treats business activity as a static quantity. It assumes that a process can reach an optimal state that can then be technically replicated. However, this does not reflect the reality of modern production and office processes. Rather, practical implementations show that processes fundamentally change with the introduction of automation technology. What works perfectly manually today will no longer fit into the automated system tomorrow, because automation brings its own logic, requirements, and constraints.
Research findings from the European automation industry underscore this dynamic. Analyses of the toolmaking sector reveal that nearly two-thirds of all surveyed companies cite process stability as one of the biggest obstacles to implementation. However, it quickly becomes clear that this stability is not something a process simply has or doesn't have. It is a gradual concept achieved through continuous adaptation. Seventy percent of the companies in the same study stated that component and process standardization had not progressed far enough to enable end-to-end automation. The logical fallacy would be to say: "Then optimize manually first." The realistic finding, however, is that standardization is often itself a result of the automation process, not its prerequisite.
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Parallel instead of sequential: The key to true automation
This becomes particularly clear when considering the so-called Simultaneous Engineering principle, which has become standard best practice in modern organizations. This concept states that all individuals involved in the process work on a result not sequentially, but in parallel. Applied to process optimization and automation, this means that teams work simultaneously on manually improving the process and, in parallel, consider how this process could be automated. This parallel perspective leads to more fundamental optimizations because it optimizes process steps not only for their current efficiency but also for their potential for automation. This is a qualitatively different approach.
German business culture has a pronounced tendency to break problems down into phases. This isn't ineffective—historically, the phased approach has led to great successes in German engineering and manufacturing. However, in modern digitalization, this approach proves counterproductive. An empirical study by Bitkom revealed that 45 percent of companies identify the conversion of existing systems and processes as a central challenge of digitalization—particularly prevalent among larger companies with over a thousand employees, where this figure rose to 66 percent. This is no coincidence. The longer a company clings to a sequential model, the wider the gap becomes between the optimized state and the requirements of automation.
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The hidden costs of waiting: ROI, skills shortage, and technical debt
The ROI problem becomes even more pronounced when considering the reality of automation projects. A typical scenario: A company invests fifty thousand euros in an automation solution for invoice processing. The reduction in manual effort leads to annual savings of forty thousand euros. The ROI is therefore minus twenty percent in the first year. This is not a failure of automation, but a typical curve. In the second year, the investment is fully recouped. In the third year, the solution begins to become profitable. Companies that wait too long for the "perfect" manual phase lose these valuable years of amortization.
The situation becomes even more critical when considering the shortage of skilled workers. According to current surveys, the lack of IT and digitalization specialists is a limiting factor for automation projects. A study by Deloitte shows that companies that rely on automation achieve, on average, 20 percent higher productivity and a 15 percent lower cost base. However, these companies typically didn't wait for the "perfect" manual phase. They started building parallel structures early on. This also creates the foundation for a deeper understanding between IT and business departments, which in turn significantly increases the success rate of automation projects.
The technical debt that arises from overly sequential planning is also a significant factor. If a company waits until everything is running perfectly manually, technical systems, databases, and software components continue to operate during this time. They become outdated, accumulate inconsistencies, and develop "debt" in the form of outdated code, obsolete hardware, and suboptimal database structures. The burden of repaying this debt later, alongside automation implementation, increases exponentially. A study on managing technical debt in large companies shows that debt repayment must be a continuous process involving the parallel implementation of avoidance, identification, measurement, prioritization, and monitoring. A purely sequential approach leads to technical debt accumulating to such an extent that it ultimately hinders automation itself.
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Parallel instead of sequential: Process mining as a turbocharger for automation – Agile, Lean, Kaizen and how parallelism improves processes faster.
From Process Mining to Agile: How modern methods enforce parallelism
Process mining offers a completely new approach. This technology reconstructs current processes from execution data and reveals inefficiencies. Prioritization is particularly valuable: Process mining can objectively determine which process changes will yield the greatest ROI. A study on measuring the success of process mining identifies four key success factors: Process recording and analysis visually reconstruct current processes; process harmonization and optimization standardize similar processes; the selection of automation processes follows the ROI criterion, focusing on high-volume standard processes; and monitoring during operation continuously measures process performance.
This systematic approach cannot wait until all processes have been manually optimized. It must run in parallel. Process mining only works with real data from real processes. You can't apply process mining to an optimized process that never actually ran in practice, but only to a process that exists and produces data.
The challenges of implementation are another aspect that speaks in favor of parallel approaches. A CGI market study found that change management, collaborative partnerships between business and IT, integration into the existing system landscape, agile implementation methods, and employee training are the five success factors for automation projects. These factors function significantly better when parallel approaches are used. When business and IT work together on the solution, better mutual understanding develops automatically. Change management works better when employees understand why a process is being optimized in a particular way—because they see that this is specifically to make it automatable.
The statistical error rates in automation projects are also revealing. One of the most frequent sources of error is the incorrect selection of automation processes. Conversely, this means that the correct selection of processes is essential for success. However, this correct selection cannot be made externally by consultants who wait until everything is running optimally manually. It can only be achieved through a shared, parallel understanding, in which engineers and subject matter experts work together to determine which processes are suitable candidates for automation.
When examining agile methods in software development, it becomes clear that the parallel model is superior to the water model. Agile transformation with DevOps pipelines demonstrates that encapsulating products in microservices and containers enables fast, parallel, and resource-efficient deployment. This allows for parallel and therefore faster automated testing, significantly reducing lead time. This is not a new phenomenon, but a proven principle that shows parallelism is more efficient than sequentialism – even in complex technical environments.
The Lean Management concept, deeply rooted in German industry, also supports this parallel approach. Lean Management is based on continuous improvement and minimizing waste. However, waiting until a process is manually perfect wastes time – one of the most valuable resources. Combining Lean Management with Industry 4.0 technologies enables even more precise monitoring and control of manufacturing processes. This combination only works, however, if both aspects – Lean expertise and technological planning – are developed in parallel.
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More than just technology: people, culture, and continuous improvement
An often overlooked aspect is the psychological dimension. The statement "processes first, then technology" is not just a strategic one; it's also reassuring. For many employees and managers, it means that they don't need to do anything yet, that they still have time. Those opposed to automation within companies often use this mantra to slow down projects. This is understandable, but economically disastrous. McKinsey shows that companies that drive automation early and in parallel with process optimization are, on average, among the winners in their market.
Hybrid integration is a term that has gained importance in modern system architecture. The concept states that companies can modernize gradually without abruptly replacing existing systems. A practical example is the integration of an SAP solution with a cloud-based application. This hybrid approach allows for parallel modernization without impacting existing operations. New development occurs alongside ongoing operations, enabling testing without causing availability issues. Users can actively choose between the two solutions until the new one completely replaces the old one. This demonstrates that evolution, not revolution, is the right path—but an evolution that runs parallel, not sequentially.
The implementation obstacles cited in studies are not necessarily arguments against parallel processes. Nearly a third of companies report a lack of process understanding. However, these very companies could benefit from parallel approaches because intensive engagement with automation technology fosters a deeper understanding of the processes. Discussions between IT and business departments about "how would we automate this step?" lead to a more thorough understanding of the process than a purely analytical phase.
Continuous improvement is another concept that supports parallel processes. After implementing an automation project, a phase typically begins in which monitoring is carried out, identified inefficiencies are addressed, and the process is further optimized. This phase becomes much more effective when it is not completely separate from the initial optimization phase. If teams already understand how processes can be automated, they can implement continuous improvements more quickly and effectively.
The Kaizen principle, which means continuous improvement, is often misunderstood as a very slow process. In reality, Kaizen means that everyone involved is constantly seeking and implementing improvements. This works exceptionally well with parallel automation initiatives. The combination creates a culture of continuous improvement that doesn't wait for the "perfect" manual phase, but rather achieves it during the automation implementation process.
The demands on change management in automation projects are notoriously high. An automation project is an organizational change, not primarily a technical one. Resistance from employees is normal and human. The solution lies in transparent communication that regularly reports on goals, potential, impact, and status. However, this communication works better when it happens concurrently. If employees see from the outset that their expertise is being incorporated into the design of automation solutions, trust and acceptance are more likely to develop than if they first have to observe how a process is "perfectly" optimized only to then be automated "from the outside."
Another critical observation: Companies that wait too long for the perfect manual phase often miss the windows for technological investments. Funding, subsidies, and tax incentives for digitalization are time-limited. A culture of waiting leads to these windows being missed. Agile companies take advantage of these windows because they can already think in parallel and act more quickly.
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A new mantra for digital transformation
The conclusion from all these perspectives is clear: The classic statement "processes first, technology later" is economically and strategically outdated. It may have been appropriate in an Industry 2.0 or 3.0 world, where changes were slower and large investments in individual systems had long lifespans. However, in today's Industry 4.0 reality, where flexibility, speed, and continuous adaptation are crucial, this approach is counterproductive.
The right mantra is: Understanding processes is essential. Thinking about automation isn't just optional; it's necessary for truly understanding processes. Progress happens when you bring both together. This doesn't mean implementing poorly conceived automations. It means always incorporating the automation perspective into process optimization. It means process analysts and IT architects working together from the outset. It means companies don't wait for endless manual perfection, but start early with small, iterative automation steps.
Companies that understand and practice this will be among the winners in their industry. Those that continue to think sequentially will fall behind.
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