The role of AI in project management is currently raising questions in many organizations. The key question is: where could AI genuinely help project organizations in practice?
In the second part of Projektipomo’s AI blog series, the topic is approached from a practical and realistic perspective. Discussions around AI often revolve around big promises, while project organizations should focus on understanding where AI could truly make everyday work easier, improve visibility for decision-making, and strengthen competitiveness. The first part of the blog series is here.
In this follow-up article, Projektipomo especially reflects on whether AI could help project organizations gain clearer visibility into their operations and anticipate issues before they become visible in projects.
Could AI Help Identify the Most Profitable Projects?
Many organizations manage project portfolios containing a large number of initiatives competing for the same resources and budgets. One of the biggest challenges is determining which projects best support the company strategy and where investments should be directed.
Today, portfolio management already evaluates projects through factors such as costs, payback time, risks, and strategic importance. AI could help identify relationships between this information from much larger amounts of data than humans can realistically process.
For example, a system could detect that certain types of projects consistently succeed better than others, or that some projects repeatedly create resource bottlenecks and cost overruns. The goal is not for AI to make decisions on behalf of the organization, but to highlight observations that support decision-making and improve visibility into the overall project portfolio.
Is the Strategy Actually Reflected in Projects?
Strategy is implemented through projects. Yet in many organizations, gaining a clear overview is difficult, especially when projects are numerous and information is scattered across different systems. One of the most interesting opportunities for AI lies in analyzing how well strategy is truly being implemented.
If the project portfolio contains information about project goals, resources, costs, and priorities, AI could help identify how effectively the overall portfolio supports strategic objectives. At the same time, it could reveal conflicts and inconsistencies that would otherwise be difficult to detect from large datasets.
AI could, for example, identify situations where:
- resources are repeatedly allocated to projects that do not support strategic priorities
- critical development initiatives are progressing too slowly
- the project portfolio becomes overloaded with parallel small-scale initiatives lacking clear business value
- investments are not aligned with organizational goals
Projektipomo believes that in these situations, AI could serve primarily as a discussion starter and decision-support tool. The larger the project portfolio, the more difficult it becomes to understand the full picture without clear data visibility.
Resource Management Remains One of the Biggest Challenges
One of the most persistent challenges in project organizations is resource allocation. Who has capacity? Where are the skill gaps? Which projects require additional recruitment?
Currently, these questions are often solved manually using multiple systems and spreadsheets, even though project and resource situations constantly change. AI could help identify issues before they become visible in daily project work by analyzing schedules, resource allocations, and competency profiles.
At the same time, AI could help identify long-term capability needs. If certain types of projects continue increasing and the same skills are repeatedly needed, recruitment needs could be anticipated much earlier. In project management, anticipation is often the most valuable capability of all.
A Large Part of a Project Manager’s Work Focuses on the Past
Many project managers recognize the situation where a significant amount of working time is spent on reporting, updating status views, and investigating events that have already happened. Project management can easily become continuous reaction instead of proactive leadership.
The most interesting opportunity for AI may not be automating individual tasks, but shifting the focus toward prediction and anticipation. If systems can identify recurring issues, growing cost risks, or schedule delays from previous projects, project managers can react earlier. Automated reporting could also reduce manual work and free up time for actual project leadership.
AI is not meant to replace project managers. Quite the opposite. The more complex the projects are, the more important human experience, business understanding, and decision-making under uncertainty become. AI’s role is to support that work.
Everything Ultimately Depends on Data Quality
AI cannot create value without high-quality project data. If information is scattered across different systems, documents, and spreadsheets, it becomes difficult for both people and AI to form a clear overall picture. The higher the quality and consistency of the data, the more useful insights can be generated.
This is why project management systems play a critical role in AI discussions. Before organizations can benefit from advanced analytics and AI-powered support functions, they must first understand where their project data is and how well it is managed.
Camako Creates the Foundation for AI Utilization
Camako, developed by Proha, brings together essential project information into one system, allowing organizations to manage project portfolios as a unified whole. When schedules, resources, costs, and project status information are centrally managed, reporting and analytics also become more effective.
Camako’s open interfaces enable project data to be utilized in Power BI reporting and various analytics solutions. In practice, this means organizations can begin by leveraging their existing data and gradually build a foundation for future AI solutions.
In addition to project data, Camako’s open interfaces also provide access to the underlying data model. The data model describes relationships between data, data types, mandatory fields, and the logic behind calculated values. With the help of this data model and a single project identifier, AI can locate and retrieve all project-related information needed as a foundation for analysis.
The essential question is: What thoughts does AI evoke within your project organization? Could AI help with project portfolio prioritization, resource management, or monitoring strategy execution? Has your organization already considered how project data could be leveraged more effectively?
Projektipomo and Proha’s experts are happy to discuss different use cases and ideas related to the future of project management. Get in touch and let’s continue the conversation together.