Deploying AI where it truly reduces the project workload.
AI can significantly ease project work: in summarizing, searching, evaluating, drafting, and identifying patterns. We combine specific use cases with reliable data, clear processes, and accountability that remains with the human.
Project and portfolio management generates a vast amount of information: status reports, risks, measures, minutes, decisions, resource plans, project proposals, and lessons learned.
The greatest benefit of AI often lies in unspectacular tasks: summarizing project status, extracting risks from texts, converting minutes into action items, or finding relevant project experience faster.
For this to work reliably, data, processes, roles, and access rights must be correct. That is why we do not view AI in PPM as an isolated technology topic, but as an integral part of project work.
Three Symptoms. One Cause.
Unclear use cases, inconsistent data, and a lack of accountability mean that AI pilots look impressive but fail to become a permanent part of project work.
The specific use case remains vague
AI is expected to write reports, identify risks, and prepare decisions. However, it is unclear which tasks are actually time-consuming today and what measurable benefit the support is intended to provide.
- Start with specific pain points
- Evaluate benefits, risks, and data availability together
- Select a clearly defined first use case
AI cannot get a reliable project overview
Project information is stored in different tools, formats, and levels of detail. Some projects report accurately, others only roughly. AI cannot simply compensate for these differences.
- Clarify reliable sources and data ownership
- Standardize status and terminology logic
- Secure access and timeliness of information
Piloting without governance and clear verification
A prototype works technically, but there is no definition of who verifies results, which data may be used, and how the application is integrated into processes.
- Define human verification and decision-making
- Consider permissions and confidentiality
- Plan for operation, monitoring, and scaling early on
The same AI support. Four different expectations.
Project management, PMO, IT, and management each require a different perspective on benefits, data, control, and scaling.
Less information work. More time for steering.
Project managers benefit where AI summarizes information, structures tasks, or makes relevant content accessible more quickly.
- Prepare status reports and minutes faster
- Find information more specifically
- Reduce routine work, retain accountability
Make large amounts of information actionable faster.
PMO and portfolio functions can use AI to prepare status, risks, measures, and correlations across multiple initiatives.
- Consolidate status information for comparability
- Identify patterns and deviations earlier
- Prepare management information
Properly regulate data access, quality, and control.
Technical and governance leads create the conditions for AI to access only authorized, traceable, and reliable information.
- Clarify data sources and permissions
- Ensure traceability and verification
- Prepare for operation and monitoring
Turn pilots into an accountable capability.
Management decides which use cases are strategically relevant, which risks remain acceptable, and where successful approaches are scaled.
- Prioritize use cases by impact
- Establish governance and responsibilities
- Scale proven applications in a controlled manner
Expansion based on maturity level.
AI should be introduced step-by-step in the project environment. It is crucial that each step provides functional value and remains organizationally manageable.
Clarify Use Cases
Identify work areas where AI can provide concrete help.
- Record pain points
- Evaluate benefits and risks
- Check data availability
How We Support Our Clients
Alexander Zorn
COO
„AI becomes valuable when it improves a specific task – with reliable data, clear verification, and a place in actual project work.“