How does technology help with project management
Artificial intelligence (AI) in project management
"Computer - Please calculate the effects of the virus epidemic on my project plan and my risk log!" The project manager first gets a coffee. Meanwhile, the computer uses artificial intelligence (AI) to compile all the results that the project manager will later discuss with his sponsor ...
Yes, that would be it: The AI relieves the project manager of time-consuming routine work, while he can focus on the critical aspects of his project.
Such possible uses for AI are currently being widely discussed. For beginners, this sounds plausible at first and it seems only a matter of time before AI finds its way into everyday project management. For AI experts, however, on closer inspection, restrictions and obstacles very quickly arise that cannot be easily solved - at least not in the foreseeable future. One of the great challenges when using AI is to have a sufficient amount of machine-evaluable and homogeneous data sets available.
In the following we present some use cases for AI in the area of project management and show what needs to be considered when implementing it:
Create, regularly monitor and validate project plans
Predicting work packages and checking them for plausibility or determining the chances of success of a project plan are tasks that are predestined for the use of AI. The results can be very valuable for the course of the project.
The project management methodology - especially when using tools and the seamless maintenance of project data in databases - is not yet completely standardized in many companies. The respective procedure is usually based on a comparable standard, but has individual variations in practice.
Most project managers create project plans individually. As a result, there is not a sufficient amount of historical and homogeneous data sets. But this is what it takes to use AI methods to create reliable predictions at the push of a button. This applies to both the perspective of individual companies and group-wide approaches.
For example, the method of predictive analytics is used in purchase price allocation (PPA); however, a complex recording and homogenization of project data is necessary in order to be able to carry out a comparison with historical projects.
This does not work out-of-the-box and without general standards. A cross-company standard and tool use is therefore required for a standard product to prevail in the end. This enables project managers or portfolio managers to carry out evaluations of project plans at the push of a button.
Identify, monitor and assess risks automatically
The detection and continuous monitoring of risks using AI methods also depends on the availability of sufficiently large amounts of historical and homogeneous data sets. However, risk management in projects is still neglected in many companies.
Often it is up to the project managers which methods and tools are used. Risk management therefore varies greatly not only between companies, but also within individual companies. As a result, here too there is not a sufficient amount of homogenized data records to achieve usable results with AI.
AI should help project managers in the foreseeable future to process information and identify relationships. The AI thus provides a suitable consolidated and prioritized database to support risk management. Among other things, chats, emails and comments in Jira or Confluence can be evaluated by AI in order to show connections and aspects that a project manager would initially not notice.
Support personnel selection and optimize staff deployment
The first tools that support the process on the basis of AI are already available for selecting suitable project employees and putting together suitable project teams. They can be used to automatically determine relationships between data records and thus also find optimal correlations for employees. This could also be used very well to find out who is working optimally with whom. However, it should be remembered here that personal rights must be observed when analyzing personal data, which means that certain limits are set for AI approaches in the personnel area.
Automate recurring tasks
In the case of recurring tasks such as status queries or the control of data quality, it can be assumed that AI-supported approaches will establish themselves in the near future. Chatbots could take on various tasks for project managers; for example, approaching employees - even if only in a figurative sense - supporting them with the maintenance of project data or also reminding them of important tasks and appointments.
Conclusion: Standardization plays a major role
In summary, it can be said that the use of AI in project management still has to overcome a few hurdles before it can be used more widely. Standardization plays a key role in this. It lays the foundation for a sufficient database so that the AI can deliver reliable results. And it pays off: project managers can be relieved of routine tasks through AI and thus have more time to take care of the individual and challenging topics of their projects.
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