The capabilities of AI-based tools have grown so much that it is time to consider how we can make effective use of these new opportunities. AI is unlikely to eliminate the need for project management, but it will provide a whole new set of tools. By taking advantage of these new tools, project managers can get rid of a large amount of routine work and focus on the things that really matter, such as people management, business benefits and stakeholder management.
1. How can AI be used to help manage projects?
If you are a project manager, how could you use AI in your work? What kind of support could a PMO get to manage project portfolios with AI? How could resource managers use AI to optimise resource allocation?
Could AI help with questions such as:
- What are the risks associated with the project I am describing?
- How much could I speed up the completion of the project by hiring three new developers?
- I can hire two more people. Which skills would I hire to get the best value for the whole project portfolio?
- Which of these three product development ideas is worth investing in in this market situation?
- What kind of message would I send to the project team and steering group about changes to the project plan?
1.1 AI helps with brainstorming, project definition and planning
AI can provide ideas and thoughts when it comes to defining the scope and content of a project. In listing project tasks, estimating workloads and durations, and scheduling projects, AI can draw on an organisation’s own experience and information available on the internet.
In risk brainstorming, AI can help retrieve a list of typical risks when given a description of the project at hand. The search for risks can be done freely from information on the internet, but more accuracy is achieved by feeding the AI with your organisation’s project risk database.
1.2 AI predicts project success based on historical data
The historical data collected can be used to predict the success and outcomes of projects. Data from an organisation’s own project management systems can be fed into an AI-based system that uses the data to learn which projects succeed and which fail. For example, delays in the early stages of a project might predict a delay in project completion, or a limited risk analysis might predict significant cost overruns. One of the strengths of AI is its ability to extract rules or causal relationships from large amounts of data that are otherwise difficult to see within the available working time.
1.3 AI alerts you to upcoming problems
AI can monitor project tracking and status data. When it detects anomalies or signs of danger, it can automatically alert the project manager or strategic project office, PMO, who will guide the project portfolio in setting target balances and monitor project progress.
1.4 Leveraging AI to automate data analysis and reporting
Microsoft Power BI and even Excel can generate graphs and analyses from data loaded into a data model in response to questions posed in natural language by the user. For example, a user can ask Power BI to generate a graph showing the costs of major projects grouped by location and time period. Once the user is familiar with the concepts of the data model, the data can be analysed quite easily using normal language questions.
AI-based systems can independently go through the project status data and highlight the desired alerts in the status report. In project reporting, it is often desired to highlight anomalies. Traditional schedules and budget comparisons allow the human eye to find delays and budget overruns, but finding anomalies requires sifting through and analyzing data and reports. AI can tirelessly and continuously monitor tracking data and raise alerts automatically.
The project status report can be created by using extensive language models combined with information from the organisation’s project management system. The language models allow the AI to transform numerical project data into a verbal analysis of the project situation. In other words, with access to project schedule information, workloads, actual and budgeted costs, AI can compile an analysis of the situation as a human would explain the situation to a steering committee.
2. What is required from an organisation to implement machine learning tools?
Microsoft will make Copilot and Azure AI tools available from early November 2023. AI is no longer science fiction or a vision of the future. It enables new ways of working with computers. AI applications will become more commonplace as they are made available to users of information systems in a way that is easy to adopt.
All tasks and jobs should be evaluated and we should think about how AI could help us in these tasks. Through experience, we will learn where AI can help us and make our work on projects more efficient.
The biggest users of AI will be the holders of large data sets. Where there is a lot of data, AI can help to make more versatile and efficient use of it. Organisations may have a lot of collected data and documentation of various kinds that could be used by AI-based systems. Finding, processing and cleaning up the appropriate data and making it available to AI enables AI applications to be used on the basis of the organisation’s own data.
3. Want to learn more about AI?
If you want to find out more about what AI is and how it works, I recommend you take a course on the subject. Excellent free basic courses for anyone include:
Practical AI: https://cs.edukamu.fi/practical-ai-fi
Elements of AI: https://www.elementsofai.com/fi/.
The journey is just beginning
Although we are at an early stage in the deployment of AI, there is no reason to delay any longer. What are your thoughts on the potential of AI? Post your thoughts and comments on the Proha LinkedIn page.