How Shifting Mindsets Unlocks AI’s Potential in Project

By Marcus Glowasz

Artificial intelligence is the next big thing in project management – if project professionals allow it to unfold.

The discussion about the use of artificial intelligence (AI) in project management has recently gained momentum, largely due to the hype surrounding ChatGPT. This is not surprising given AI’s undeniable abilities, especially with ChatGPT’s latest version GPT-4.

This increased interest in AI has sparked curiosity in the field of project management, particularly in the wake of consistently disappointing project outcomes. Project failures or delays often result from wrong decisions made in projects, typically caused by human bias. However, many project decisions are delayed or avoided due to insufficient information to make sound judgments. In a time of perceived «information overload», this seems to be rather surprising. Or maybe not, because the rapid increase of available data and information is overwhelming for many. While the exponential data growth comes with a byproduct called Fake News or Fake Data, it makes it difficult for project professionals to retrieve reliable information to support their decision-making.

The challenge is not only to find the right information in an ocean of data but also to separate the signal from the noise and identify misinformation. Analyzing data to make informed decisions can be a time-consuming process that, in today’s fast-paced environment, may not be affordable. Data-informed decisions are crucial in today’s information age, and speed is equally important.

AI can contribute to better and faster decision-making by collecting relevant data, logically evaluating it, and making fact-based recommendations in a fraction of the time it would take for people to do the same. Hence, project management appears to be a perfect candidate for adopting some sort of a project management ChatGPT to get better, relevant, and correct information fast, helping project managers to bridge the hurdle of lengthy data collection and analysis.

For instance, we may need to determine the best solution for a particular type of project, including a related work breakdown structure, project schedule, task estimates, and so forth, to determine cost and time parameters for a new project. Although projects are unique in their entirety, they usually include many components that are similar and sometimes even identical in their nature across a series of projects. By analyzing past projects with those similarities and comparable characteristics, and identifying patterns, related risks, and working solutions, AI can assist in building evidence-based solutions, project plans, saving the project manager a lot of valuable time.

So where is the hurdle, or why are we not yet working with AI in project management?

The availability of data should not be the problem as countless data are generated daily in projects. Project plans, risk registers, stakeholder maps, Gantt charts, lessons learned, etc., are typical artifacts that form the basis for project evaluations, reports, and decisions.

Besides the existence of data to be analyzed, the quality of project data is equally important, primarily its contents. If people produce misinformation or fake news, or a necessary depth of information is missing because people don’t want to share some project details, then naturally only limited insights can be derived, possibly leading even to wrong conclusions.

Hence, adapting the usual behaviors and habits of project professionals are crucial to create a necessary data culture that ensures sufficient and reliable data to retrieve valuable insights. Here are some common settings that are hindering transparency and data flow, and therefore would limit the potential of data-driven practices:

  • Knowledge silos
    We usually see ourselves as owners of the artifacts and the resulting knowledge we produce. This ownership thinking often reduces the willingness to share it across the company, and knowledge silos are the result.
  • Embarrassment
    No one is proud to have failed a project, and therefore rather prefers not to talk about it. Accordingly, information about such projects is scarce and only the bare minimum is produced and shared.
  • Ego
    Project managers prefer not to fully expose their precise approaches and techniques as they could be questioned which in turn could negatively affect their reputation in the organization. As a result, workarounds, shortcuts, or any unconventional methods are usually not documented or shared.
  • Positioning
    The truth is often stretched due to political or individual motivations. For instance, to maintain ongoing confidence and trust from stakeholders, a project manager may choose not to disclose certain difficulties in order to reach a particular milestone, allowing the project to run at the edge of failure. Once the milestone is successfully reached, the project may appear to be in a very healthy state, while the situation may be quite different.

In order for AI to effectively learn from human behaviors and processes to enhance project work, it is crucial to provide a realistic and honest portrayal of data. This includes extensive information about project failures and any ongoing issues within projects. Only then will AI be able to provide valuable insights to accelerate project work and improve its overall quality and value.

Consequently, it requires a shift in the mindset of project professionals towards transparency, authenticity, and accountability. This change should be in their own interest, as project team members not only produce project data but also consume it. Therefore, they stand to benefit from the increased effectiveness of AI. Project managers will have the opportunity to move away from technical and administrative project management tasks and instead focus on the strategic direction and leadership of project teams.

The integration of AI in project management represents a turning point, redefining the role of the project manager and repositioning the field of project management in the organization through enhanced project intelligence.

However, a change in mindset and a new project and organizational culture that prioritizes transparency, openness, and willingness to experiment is essential to meet the demands of today’s fast-paced and constantly changing information-driven era.

Marcus Glowasz is a project management specialist, coach, and advisor based in Zurich, Switzerland. With almost 30 years of experience in technology and data-driven projects across numerous organizations, from large corporations to small businesses around the globe, he is well-versed in project best practices, including strategies for innovating and advancing project delivery practices. The emphasis of his work is on raising data literacy skills among project management professionals and facilitating a transition to future-proofed and evidence-based project delivery practices that align with today’s rapidly changing business demands.

He is the author of the book “Leading Projects with Data”, which provides invaluable insights on overcoming cultural and behavioral barriers to achieving success in data-driven project delivery. You can learn more about Marcus at

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Disclaimer: The ideas, views, and opinions expressed in this article are those of the author and do not necessarily reflect the views of International Institute for Learning or any entities they represent.