GoITSystems by Dr. M. Gottlieb
Department-Oriented Thinking vs. Process-Oriented-Thinking

Digitalization in German public administration does not primarily fail because of missing technology, but to a considerable extent because processes are poorly coordinated, only partially standardized, and rarely thought through end-to-end. Looking at organizational charts and departmental responsibilities is therefore not sufficient; what is needed is a process-oriented perspective that understands administrative services as end-to-end value chains. This line of argument can be anchored theoretically in business process management (BPM), business process reengineering (BPR), and a socio-technical view of administrative organizations (; ; ).

The Core Problem: Responsibility Instead of Flow

Public administrations have historically been organized primarily along functional lines of responsibility. This structure secures formal accountability, but at the same time it reinforces media breaks, redundant checks, sequential handovers, and a lack of transparency regarding the actual processing status of a case. For applicants, it is often unclear where a procedure currently resides, which office is working on it, and at which interface delays arise.

From a process perspective, this fragmentation is the central organizational problem. While responsibility thinking focuses on formal accountability, compliance with rules, and handing the case to the “next competent unit,” process thinking focuses on the entire case flow – on sequence, dependencies, information needs, waiting times, and output quality. Process management does not replace responsibilities; it complements them with a coordinating end-to-end perspective (; ).

Theoretical Framing

In the BPM literature, a business process is defined as a structured sequence of interrelated activities that transforms inputs into outputs and thereby creates value for internal or external recipients. Applied to public administration, this means: a process does not start at the internal desk of a caseworker, nor does it end with forwarding the case to another unit. It begins with the initial input and only ends when the service has been fully delivered to citizens or businesses ().

Three theoretical lenses are particularly relevant. First, BPM provides the conceptual framework for identifying, modeling, analyzing, executing, and continually improving processes. Second, BPR emphasizes that digitalization delivers substantial benefits only when processes are not merely reproduced electronically but fundamentally simplified and redesigned. Third, a socio-technical perspective highlights that sustainable administrative digitalization cannot be understood as an IT project alone, but rather as an interplay of organization, rules, roles, technologies, and institutional incentives (; ; ).

Responsibility Thinking vs. Process Thinking

  • Responsibility thinking asks: Who is formally in charge, which office processes the case next, and are internal rules and instructions being followed?
  • Process thinking asks: What must happen next to achieve the procedural goal, which information is needed, and where do delays, redundancies, or error sources occur?
  • Organizational implication: Responsibility orientation optimizes partial tasks within silos; process orientation optimizes the overall flow across organizational boundaries.
  • Digitalization implication: If only responsibilities are digitized, existing inefficiencies are reproduced in digital form. Only process-oriented design creates the precondition for effective digitalization (; ).

Why Digitalization Fails at This Point

Empirical evidence on administrative digitalization in Germany supports this diagnosis. The Behörden-Digimeter 2026 shows that, as of early 2026, only 823 of 7,509 relevant individual services were implemented nationwide; at the same time, no federal state had more than 291 of 577 Onlinezugangsgesetz (OZG) service bundles fully available. The study explicitly emphasizes that this implementation status does not yet reflect a deep digitalization of the underlying administrative processes ().

The Fraunhofer FOKUS study EfA im Fokus points to structural barriers that are not primarily technical either. The study shows that municipalities still face financial uncertainty, high support needs, complex procurement requirements, and heterogeneous organizational structures when reusing “Einer-für-Alle” digital services. It particularly highlights the need for stronger coordination, knowledge transfer, reliable rollout structures, and transparent process management ().

These findings make it clear: the deficit is not just a lack of online services, but poorly coordinated and institutionally weak process chains. Digitalization without prior process analysis therefore tends to reproduce existing inefficiencies in digital form instead of removing them (; ).

What an Administrative Process Actually Includes

In public administration, a process typically consists of a defined input, a sequence of rule-based and professional activities, and a clearly identifiable output. Inputs might be applications, notifications, or events such as birth, relocation, or business formation. Activities range from examination, decision-making, and documentation to clarification requests, involvement of other units, and delivery of decisions. Outputs are permits, notices, registry entries, or other administrative acts.

Crucially, all internal handovers, idle times, and feedback loops are part of the process. These invisible in-between spaces are often the true sources of delay, opacity, and friction. From a BPM perspective, the primary analytical object is therefore not the individual processing step but the complete end-to-end process (; ).

Three Dimensions of the Required Shift

From Function to Flow

Administrative modernization must model cases as coherent flows rather than as a chain of responsibilities. At the federal level, BPMN 2.0 has been established as a key modeling standard; the Federal Office of Administration emphasizes that uniform process management standards enable processes to be steered and documented consistently using BPMN 2.0 (; ).

From Exception to Rule

Process thinking looks for recurring patterns and standardizable case constellations. In public administration, a substantial share of cases can be structured by explicit decision rules, while complex exceptions are treated separately. The Federal Office of Administration describes DMN expressly as a standard for modeling business rules and decision processes; this facilitates transparent documentation of standard decisions and, where legally and organizationally feasible, their technical support (; ).

From Silos to Process Chains

The greatest sources of friction typically arise at interfaces between organizational units, federal levels, and IT service providers. The Fraunhofer FOKUS study underlines the need for stronger coordination, intermediary actors, standardized handover structures, and audience-appropriate communication in the EfA rollout. Process thinking is therefore always also interface management: it makes handovers visible, reduces unnecessary loops, and strengthens coordination across departmental and jurisdictional boundaries ().

Organizational Preconditions

A process-oriented administration requires, first, visibility through documented and modeled processes. Second, it needs accountability through process owners or similar roles holding a cross-unit mandate. Third, it requires measurability – metrics like on throughput times, idle times, clarification requests, error rates, and variant frequencies. These three preconditions are primarily organizational and governance-related, not technical (; ). It is important to note that these key figures are both purpose-driven and economically measurable.

This leads to an often underestimated insight: technology cannot compensate for a lack of process clarity. If roles, rules, handovers, and escalation mechanisms remain vague, digital technology at best accelerates existing dysfunctions. From a BPM perspective, process analysis is therefore not a downstream optimization step, but the precondition for any meaningful digitalization (; ).

Implications for Leadership and Governance

Process thinking is not neutral, because it reduces organizational opacity. Once throughput times, idle times, and handovers become visible, responsibility diffusion, duplicate work, and avoidable delays are exposed. This helps explain why process-oriented reforms regularly encounter not only professional but also political and micro-political resistance ().

For leadership, this means that process management must be understood as a governance task. It is not sufficient to provide modeling standards or online services; leadership must also ensure prioritization, mandate, standardization, and enforcement across organizational boundaries. Particularly in federal systems, coordination quality matters more for digital transformation success than the mere availability of individual technologies (; ).

Conclusion

Process thinking is not software, but an organizational lens on administrative service delivery. Approaching digitalization mainly as the deployment of digital front ends misses the core modernization challenge. Only when administrations model, standardize, measure, and steer their services as end-to-end processes do the conditions emerge for effective, scalable, and user-centered digitalization (; ).

Related Articles

References

Process Intelligence

Executive Summary

Digital transformation in German public administration fails less due to a lack of technology than because administrative processes are insufficiently understood, documented, and managed. Process intelligence—understood as the public sector’s ability to systematically capture its processes, analyze them in a data-driven way, steer them in a targeted manner, and continuously improve them—forms the operational foundation of effective digitalization and responsible AI use.

Conceptually, process intelligence builds on established Business Process Management (BPM) but extends it through data-driven analysis and AI-enabled decision mechanisms (). From a design science perspective, process intelligence can be understood as a socio-technical artifact that integrates organizational structures, methods (e.g., BPMN, DMN, process mining), and technical systems in order to address the problem of fragmented digitalization in public administration ().

1. The Challenge of Digitalizing Public Administration

1.1 Digitalization without Process Reform

Digitalization initiatives in the public sector often focus on the technical implementation of existing procedures. In practice, this means that analog inefficiencies—such as redundant checks, media discontinuities, or unclear responsibilities—are transferred unchanged into digital systems. The result is digitized legacy processes instead of structural improvements, as documented in the e-government literature on fragmented modernization approaches ().

Effective digitalization therefore requires prior analysis and redesign of the underlying processes, as formalized in the BPM lifecycle phases of process analysis and process redesign ().

1.2 Structural Root Causes

  • Fragmented IT landscapes: A lack of interoperability prevents end-to-end process logic and hinders an end-to-end view of public services ().
  • Low standardization: Similar services are implemented differently across organizations; this undermines the scaling and re-use of digital solutions ().
  • Responsibility-oriented structures: Processes are designed along organizational units rather than end-to-end process chains, which public management research describes as a major barrier to process reform ().
  • Front-end focus: Digitalization often targets citizen-facing touchpoints rather than internal core processes, thereby limiting potential efficiency and quality gains ().

1.3 AI as an Amplifier of Structural Deficits

AI systems strongly depend on clearly defined processes and consistent data structures. Studies on algorithmic decision support in public administration show that unclear decision rules and heterogeneous data sets lead to bias, opacity, and limited scalability (). In environments characterized by high process variance, unclear decision rules, and low data quality, AI solutions reinforce existing inefficiencies instead of compensating for them.

2. The Concept of Process Intelligence

2.1 Definition and Positioning in the IS Discourse

Process intelligence describes an organization’s ability to understand, manage, and adaptively further develop its business processes on a data-driven basis. It thus stands in the tradition of BPM, which the IS and management literature describes as a holistic approach to identifying, modeling, analyzing, improving, and automating processes (). At the same time, process intelligence addresses the “missing link” problem between information systems and actual process execution that is emphasized in the process mining discourse ().

In the sense of Design Science Research, process intelligence can be conceptualized as an integrated artifact that brings together methods (BPMN, DMN, process mining), organizational roles (process owners, governance bodies), and technical infrastructure (process-capable line-of-business systems, event logs) in order to address a clearly defined relevance problem—the stagnation of digitalization in public administration ().

2.2 Levels of Process Intelligence

  • Transparency: Systematic capture and modeling of as-is processes, typically using BPMN 2.0 and process screenings.
  • Analysis: Data-driven assessment of efficiency, quality, and variants, for example using KPI systems and process mining techniques ().
  • Steering: Continuous optimization, automation, and integration of AI applications, supported by explicit decision models (DMN) and governance structures.

3. Methodological Foundation: Artifact Building Blocks

From a DSR perspective, process intelligence consists of an ensemble of artifact building blocks—conceptual models, methods, and technical components—that jointly enable a process-intelligent public administration ().

3.1 Process Screening

Process screening is the starting point and serves to systematically capture, prioritize, and assess administrative processes. It operationalizes the process identification and documentation phases in the BPM lifecycle and creates a sound basis for subsequent design and evaluation steps .

3.2 BPMN 2.0 as a Process Language

BPMN 2.0 enables standardized and cross-organizationally comprehensible process modeling and is established in the IS literature as the de facto standard for process modeling (). For public administration, BPMN creates the precondition for integrating business and technical perspectives on administrative workflows and for supporting model-based automation approaches.

3.3 FIM as a Federal Standardization Framework

The Federal Information Management (FIM) framework complements BPMN with public sector–specific structuring of services, data, and processes. It operationalizes the idea of standardization, which the digital government literature highlights as a prerequisite for cross-organizational re-use and scaling ().

3.4 DMN for Modeling Decision Logic

Decision Model and Notation (DMN) enables explicit, formally structured representation of decision rules. From an AI governance perspective, DMN provides a transparent foundation for rule-based and AI-supported decisions and is crucial for the traceability and auditability of algorithmically supported administrative decisions ().

3.5 Process Mining as Data-Driven Analysis

Process mining closes the gap between modeled to-be processes and real as-is executions by using event data (event logs) to reconstruct actual process variants, bottlenecks, and compliance deviations (). For public administration, process mining provides the empirical basis on which process intelligence can evolve from a purely model-driven approach into a data-driven capability for process steering.

4. Process Intelligence as a Precondition for AI in Public Administration

4.1 Design Relevance: Why AI Fails without Process Intelligence

From the perspective of the DSR relevance criterion, process intelligence addresses a clearly defined practical problem: AI pilot projects in public administration often remain isolated, non-scalable, and difficult to explain. The literature on algorithmic decision-making in the public sector points in particular to poor data quality, unclear decision logic, and weak governance structures as key causes .

4.2 Maturity Model as a Conceptual Artifact

The outlined maturity model (from “ad hoc” to “AI-ready”) can be understood as a conceptual artifact in the sense of . It structures the development paths of public organizations and makes it possible to plan and evaluate design decisions along defined maturity levels (documentation, standardization, data-drivenness, AI integration).

  • Ad hoc: Undocumented processes, no consistent view of workflows.
  • Documented: Modeled processes, but not firmly institutionalized.
  • Standardized: Binding process models, clear roles and governance.
  • Data-driven: Continuous measurement, monitoring, and improvement.
  • AI-ready: Integration of AI systems into stable, standardized, and data-driven process landscapes.

5. Impact Potential and Evaluation

5.1 Efficiency, Quality, and User Orientation

Empirical studies on BPM show that structured process design and management lead to measurable gains in efficiency and quality, for example in the form of shorter throughput times, lower error rates, and improved service quality (). In a DSR setting, these metrics can serve as evaluation criteria for process intelligence artifacts.

5.2 Transparency, Legal Certainty, and AI Governance

Explicit process and decision models increase traceability, auditability, and legal certainty—a core requirement in the public sector. In the debate on “algorithmic accountability,” it is emphasized that transparent decision rules and documented workflows are prerequisites for legitimate AI-supported decisions ().

5.3 Scalability and Re-use

Standardized models following FIM and BPMN logic support the “one-for-all” principle, which the German-speaking e-government discourse views as key to scaling digital public services (). From a DSR perspective, this represents an important criterion for the broad impact of the artifact.

5.4 Adaptability and Resilience

Documented and standardized processes facilitate the implementation of regulatory changes and organizational adjustments. The public administration and public management literature discusses this as a central dimension of administrative resilience and change capability ().

6. Implications for Design Science Research

6.1 The Artifact Nature of Process Intelligence

Process intelligence can be understood as a comprehensive socio-technical artifact in the sense of . It combines conceptual models (maturity model, levels of process intelligence), methods (screening, BPMN, DMN, process mining), and technical implementations (event-based system logs, workflow systems) and thus addresses a key relevance problem of administrative digitalization.

6.2 Research Design and Evaluation Paths

For a DSR paper, formative evaluations along the maturity levels and summative evaluations using metrics for efficiency, quality, transparency, and scalability are particularly suitable (). In addition, applying the Hevner guidelines to process intelligence artifacts themselves can become the subject of a conceptual or empirical study ().

7. Conclusion

Process intelligence is a central precondition for successful digitalization of public administration and scalable AI deployment. In the IS discourse, it links BPM, process mining, and AI governance into an integrated, design-oriented approach that addresses the relevance problem of fragmented and technology-driven digitalization initiatives (, ). As an artifact in the sense of design science, process intelligence offers a structured framework for integrating and systematically evaluating process, data, and AI perspectives in public administration ().

Literature

  • Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.
  • Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS Quarterly, 37(2), 337–355.
  • Dumas, M., La Rosa, M., Mendling, J., & Reijers, H. A. (2018). Fundamentals of business process management (2nd ed.). Springer. https://doi.org/10.1007/978-3-662-56509-4
  • van der Aalst, W. (2016). Process mining: Data science in action (2nd ed.). Springer.
  • Janssen, M., & Cresswell, A. M. (2006). Enterprise architecture integration in e-government. Proceedings of the 39th Annual Hawaii International Conference on System Sciences. IEEE.
  • Scholta, H., Mertens, W., Kowalkiewicz, M., & Becker, J. (2019). From one-size-fits-all to user-centered digital public services: A literature review and research agenda. Government Information Quarterly, 36(1), 11–25.
  • Bouckaert, G., & Halligan, J. (2008). Managing performance: International comparisons. Routledge.
  • Veale, M., & Brass, I. (2019). Administration by algorithm? Public management meets public sector machine learning. Public Management Review, 21(4), 615–642.

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