Artificial intelligence has long been on the agenda at many companies. New tools are being tested, initial use cases are being developed, and employees are gaining experience with AI-powered applications.
But the crucial question is not where AI can be applied technically.
Rather: Where does it actually create added value in process management and operational processes?
After all, individual tools or pilot projects do not automatically lead to better processes. Without a clear understanding of the workflows, the specific process problem, and existing work practices, there is a risk that AI will be used in an ad hoc manner—without having a lasting impact on efficiency, quality, or collaboration
Process needs come before technology
Not every process problem is an AI problem.
High manual effort, long processing times, or recurring errors can have various causes: unnecessary process steps, a lack of standards, unclear responsibilities, system silos, or time-consuming information processing.
Depending on the initial situation, process optimization, standardization, automation, or AI support may therefore be the appropriate solution.
Meaningful AI implementation therefore does not begin with an available tool, but with the specific process need:
What problem do we want to solve—and what measurable added value should AI create in the process?
Only once this question has been answered can we assess whether and how AI can be used effectively.
AI can provide support on two levels
For the targeted use of AI in a process context, we distinguish between two perspectives: AI for the process and AI within the process.
AI for the process supports the process management work itself. Information from interviews or workshops can be structured, process descriptions can be prepared, or existing workflows can be analyzed. AI can also provide targeted support to process managers in modeling, organizing process knowledge, and identifying opportunities for improvement.
AI within the process supports people and knowledge-intensive activities within operational workflows—for example, in the organization and classification of information, in accessing knowledge, in pattern recognition, or as an intelligent assistant in complex process steps.
Both approaches share the same goal: to provide smarter support for process work where concrete added value is generated.
Where this creates opportunities for the automated execution of process steps or workflows, process analysis simultaneously lays the technical foundation for further automation or technology implementation.
Not every possible AI use case is a useful one
As technological capabilities expand, the number of potential use cases is growing rapidly. A systematic evaluation is therefore crucial.
Where does a high level of manual or cognitive effort occur today? Which tasks are repetitive and information-intensive? Where can quality, speed, or access to knowledge be improved through AI support? What information is available for this purpose? And in which areas do human experience, judgment, and decision-making remain indispensable?
AI is not automatically the right answer. Depending on the process problem, the greatest impact may lie in traditional process optimization, standardization, or automation.
This approach transforms a multitude of technological possibilities into a prioritized portfolio of relevant AI use cases—with a clear link to the process problem and the expected benefits.
AI is an enhancer, not a replacement
AI can quickly structure information, recognize patterns, process content, and support employees with complex tasks.
But speed is no substitute for professional quality.
A process model must align with actual work practices. An analysis must be interpreted from a professional perspective. A proposal must function within the specific process and organizational context.
That is why people remain a central component of AI-enabled process management. Methodology, subject-matter evaluation, quality control, and responsibility are not handed over to AI.
Human review and clear responsibilities are therefore not an afterthought, but an integral part of the AI-supported workflow from the very beginning.
From an Idea to a Validated Use Case
Systematic AI implementation in process management requires more than just isolated experiments. Potential opportunities must be identified, prioritized, tested within the process context, and successful approaches must then be sustainably embedded.
Our approach follows three steps:
Identify: We analyze process management activities and operational processes, bring specific problems and workloads to light, and identify suitable opportunities for AI applied to processes and AI integrated into processes. In doing so, we also assess whether AI is actually the right lever—or whether process optimization or automation might be more suitable.
Test: Selected use cases are validated in a defined process context under real-world conditions. In doing so, we consider not only whether AI works in principle, but especially whether it meaningfully supports existing workflows and creates tangible process benefits.
Embedding: Successful AI-supported workflows are embedded into process management and the organization. Clear responsibilities, human review, appropriate governance, and empowering the teams involved lay the foundation for sustainable and scalable implementation.
From Individual Use Cases to an Organizational Capability
Long-term value is not created by implementing as many independent AI pilot projects as possible. What matters most is an organization’s ability to independently identify relevant areas of application, evaluate them effectively, and apply successful approaches to other process contexts.
Process owners and process managers do not need to become AI specialists to do this. However, they should understand which tasks are suitable for AI support, what level of quality they can expect from the results, and where human review and responsibility remain necessary.
In this way, individual use cases gradually evolve into a repeatable capability: AI-enabled process management.
Understanding the process determines the added value of AI
The added value of AI does not come from incorporating or automating as many process steps as possible with AI.
It arises when a specific process problem aligns with a suitable AI use case.
When used correctly, AI can support process managers in data collection, documentation, modeling, and analysis; reduce manual and knowledge-intensive work; and provide targeted assistance to employees within operational processes.
To achieve this, however, companies must understand what problem they want to solve, what specific added value AI is intended to create, and what responsibilities remain with humans.
That’s why, for us, AI-enabled process management doesn’t start with the question: “Where can we use AI?”
But rather: “Where can AI demonstrably improve our process work or our processes?”
Make AI a catalyst for your process management—in a targeted, effective, and responsible way.