Modern process optimization operates in a complex tension field between standards-compliant documentation (ISO/DIN) and actual work reality. While certification procedures according to ISO 9001/14001 or ISO 27001 demand clear, documented ideal processes, practice shows a different picture: workarounds, disruptions, and exceptions shape everyday work.

useEngineer addresses this tension through a reality-oriented approach to process documentation, which goes beyond classic PDCA cycles (plan–do–check–act). The tools useProcessCheck and useProcessMap not only capture ideal processes, but deliberately also allow the documentation of deviations and disruptions as an integral part of work reality.

• Bridging different ways of thinking: useEngineer considers processes not only as technical sequences, but also takes into account the action regulation of the people involved: how they plan, execute, monitor, and regulate.
• The classic PDCA cycle is linked with the human-centered action regulation model (plan–contact–execute–control).
• Process atomization as an analytical instrument: By systematically breaking down business processes into the smallest, clearly delineable work steps, process atoms emerge. These describe individual actions so precisely that they are both comprehensible to humans and suitable for later machine analysis.
• Classification by degree of automation and criticality: Process steps are systematically evaluated according to:
  • degree of automation (automatic, supervised, interactive, manual)
  • criticality/risk (time-critical, safety-relevant, cost-intensive)
  • frequency/workload (regular processes vs. special cases)
  • documented deviations as a regular part of the process

Current developments in artificial intelligence raise fundamental questions for process optimization. While AI systems achieve impressive results in language-based applications, they encounter structural limits in process optimization:

• Redundancy vs. compression: Language models (LLMs) thrive on redundancy in texts and therefore are strong in the domain of “language, texts, dialogues.” Processes, however, are highly compressed—well-designed workflows contain little to no redundancy, which makes it harder for AI to generalize.
• Abstraction-level problem: Automatic generalization works meaningfully only at very high, abstract levels (e.g., “information flow”), but hardly at the level of individual, concrete tasks. Yet that is where the decisive usability issues lie.
• The workaround dilemma: Workarounds and disruptions are the rule in practice and must be documented in systems. AI models struggle with this because they tend to “smooth out” deviations rather than seriously account for them. They tend to normalize exceptions instead of understanding their significance for work reality.

• Pseudo-accuracy through metrics: The real difficulty lies in defining suitable metrics for usability and process quality. Cluster analyses can help to find patterns—but they can also create pseudo-accuracy or even produce false correlations.
• Loss of exception logics: AI-optimized systems often make deviations “invisible” by fitting them into statistical normal distributions. In doing so, the very information crucial for process optimization is lost: the contexts and “exception logics” that only humans can truly understand.

Modern process optimization leads to a characteristic form of semi-automation, which creates new challenges:

• Computer support shifts complexity: Standardized processes run very well with computer support. While computer support reduces routine errors, it shifts more complex decisions to humans—interventions become rarer but more demanding.
• Consequences for workers:
  • Interventions are rarer but more complex,
  • require more judgment and systemic understanding,
  • increase demands on the organization and individuals, because appropriate decisions must be derived from a multitude of possible actions
• The ERP system example: Even large ERP systems like SAP reach their limits here—standardized processes run well, but as soon as real disruptions, exceptions, or workarounds occur, bottlenecks arise. These non-standardized workflows are difficult to map algorithmically and are currently hardly “learnable” for AI.

• Rising challenge of manageability: Computer support optimizes processes in regular operation but shifts the “difficult cases” to humans—and these cases are often the decisive ones (disruptions, workarounds, unpredictable contexts). This raises the challenge for usability engineering and process design: how can such systems remain manageable without causing overload?
• Reality-oriented vs. normative documentation: Classic process documentations (PDCA, quality standards, ISO 9001/14001) document ideal processes plus contingency concepts. They can be managed well with computer support and create a kind of “semi-automation”: the computer monitors, signals, structures—and the human intervenes in case of deviations.

The useEngineer tools do not stop at the ideal process, but create a reality-oriented process documentation that goes beyond classic PDCA/ISO approaches. This provides a more honest data basis than AI-optimized systems that make deviations “invisible.”

AI can certainly make a valuable contribution to process optimization—but only as a supplement, not as a replacement for human judgment:

• Meaningful AI application areas:
  • cluster analyses for pattern recognition,
  • anomaly detection in process deviations,
  • statistical evaluation of large data sets,
  • visualization of complex process relationships.
• AI-critical areas:
  • assessment of contexts and exceptional situations,
  • decision-making with incomplete information,
  • creative problem solving for workarounds,
  • ethical and social evaluation of process changes.

The central insight remains with the human—because only humans can truly understand the contexts and “exception logics.” AI must not create the impression that it could replace the complex judgment of workers.

It combines the best from different worlds:

• standards-compliant documentation for certifications (ISO/DIN).
• reality-oriented capture of deviations and workarounds.
• human-centered action regulation instead of a purely technical process view.
• preparation for AI use without AI dependency.

This creates a more honest picture of the organization: quality becomes visible, weaknesses are identified, and potential for improvement is revealed—without the illusion that automation could solve all problems.

The useEngineer concept also serves as a foundation for hybrid collaboration between humans, machines, and AI. Human creativity and evaluative competence are combined with machine data analysis and optimization suggestions—but final decision-making authority and responsibility remain with the human.

This approach is not only methodologically more robust but also ethically more responsible: it respects the limits of automation and strengthens the role of humans as thinking, judgment-capable actors in complex work systems.

The future of process optimization does not lie in complete automation, but in the intelligent combination of human and machine capabilities. useEngineer creates the methodological and technical prerequisites for this:

• transparent process documentation as a basis for informed decisions.
• preservation of human autonomy of action even in automated environments.
• preparation for AI integration without loss of human control.
• sustainable handling of process know-how of employees.

Thus, process optimization becomes an instrument of digital sovereignty: organizations retain control over their workflows and can consciously decide where automation makes sense and where human intelligence remains indispensable.