This post is my reflection on the following important paper:
Title: Artificial Intelligence Enabled Project Management: A Systematic Literature Review
Published in: Applied Sciences, 2023
Authors: I. Taboada, A. Daneshpajouh, N. Toledo, and T. de Vass
As I sit here, reflecting on the literature I’ve been diving into recently, I’m still keeping up my habit of reducing my coffee intake over the weekends to reset my caffeine tolerance. Turkish coffee is my go-to during the week, but lately, I’ve been trying to stay sober over the weekends—it helps me keep the edge when Monday comes around. Now, with a fresh cup in hand, I’ve been going through this excellent paper: Artificial Intelligence Enabled Project Management: A Systematic Literature Review, and I can’t help but think this would have been the perfect starting point for my current research phase into AI’s role in project management.
Published in Applied Sciences, this paper is an excellent example of a comprehensive and structured review of the various AI techniques being used in project management today. The level of detail in this article is striking—it covers a vast range of AI applications, from machine learning to natural language processing (NLP), showing just how much AI is transforming project management across industries; therefore, my reflection on this article will be a long one.
One of the highlights of the paper is how AI can handle the immense amount of project data collected over time, especially in industries like construction and large-scale engineering. It brought to mind some of the oil and gas construction projects I’ve been involved with, particularly the sheer amount of data generated over the course of these multi-year projects. The historical data was immense—ranging from material delivery schedules to team performance metrics over the years. The number of lessons that could have been extracted from this data is overwhelming, and AI, as the article discusses, is the perfect tool for this kind of work.
In one of these projects, which took place in a hot environment, data accumulated not just from the construction itself, but from every aspect of procurement, logistics, and team management. AI’s potential to analyze this kind of historical project data and extract meaningful patterns—especially for risk forecasting and resource allocation—is one of the most exciting aspects highlighted in the review.
Had we been able to leverage AI-driven analysis, the insights gathered from these vast datasets would have likely led to far more efficient planning. The ability to pull trends from past projects and anticipate potential challenges based on similar conditions from previous experiences could have changed the game in avoiding delays and cost overruns.
This brings me to one of the paper’s most important discussions: AI’s role in risk management. In projects like the ones I’ve worked on, predicting risks was one of the hardest parts of the job, especially when working with such massive scopes and timelines. The review emphasizes that machine learning algorithms can now analyze vast amounts of historical project data and predict risks with a high degree of accuracy.
I remember how we used to handle risk management the old-fashioned way—relying on manual reports, historical knowledge, and good old gut instinct. That was especially true in projects where a simple delay in materials could cascade into significant project setbacks. The review shows how AI can step in, analyzing historical patterns and flagging potential issues long before we would have caught them. It’s a prime example of how AI doesn’t just automate project management processes; it also enhances our ability to make strategic decisions in unpredictable environments.
As I was reflecting on AI’s ability to analyze patterns from historical data, something clicked. It dawned on me that this capability is actually contributing to project knowledge management—helping us learn from past projects and apply those lessons to future ones. It feels like a bit of a discovery. The idea that AI can assist in creating knowledge assets for future project use makes perfect sense, and it’s surprising that I haven’t seen this exact point emphasized in the other articles I’ve read.
But then again, I’ve yet to search deeply into AI’s role in project knowledge management. I’m sure there must be a few good studies on it—after all, it’s crystal clear how AI’s ability to identify trends and extract valuable insights is a natural extension of knowledge management. I’ll have to dig deeper into this in the coming weeks.
What’s clear from this review—and from my own experiences with data-heavy projects—is that AI’s strength lies in its ability to learn from the past. As much as we, as project managers, rely on our instincts and interpersonal skills to guide teams through challenges, there’s only so much that human memory and experience can account for. With AI, we now have tools that can digest years’ worth of lessons learned and give us insights that would otherwise be buried under the weight of historical data.
One thing the review highlighted was how AI’s ability to parse through large datasets allows for more proactive project management. I’ve seen how even the most experienced teams sometimes overlook key data points or patterns, especially under the stress of tight deadlines or unexpected changes in the project’s scope. But AI, as the paper suggests, never tires or gets distracted—it’s always processing, always learning from the data at hand.
On the human side, this capacity of AI allows project managers to focus more on team dynamics and communication, while leaving the heavy data work to machines. I know firsthand how challenging it can be to manage a project team in harsh, demanding environments, and how much more attention I could have given to morale and team cohesion if I hadn’t been bogged down by manual risk assessments or endless data analyses.
I can’t help but reflect on how this article could have been the ideal starting point for my current research. Having read several articles on AI’s applications in project management over the past few weeks, this paper offers a more holistic and well-rounded view than many of the narrower studies I encountered earlier. It brings together all the key areas where AI is currently being applied, and more importantly, it provides a balanced perspective on the strengths and limitations of these technologies.
While I’m glad I’ve finally gotten to it now, there’s a small part of me that wishes this had been the first article I read in my deep dive into AI. It would have provided a stronger foundation and given me a clearer roadmap of where to focus my efforts. But, as is often the case, we only realize these things after the fact. At least now, I can appreciate how this review has tied together so many disparate threads I’ve encountered in other articles.
In wrapping up, the systematic approach this review takes toward AI in project management reinforces my excitement about the future of the field. Whether it’s applying AI to predict risks in large-scale construction projects or using it to analyze years of project data, there’s no denying that AI is a game-changer. It’s not just about automating the mundane tasks but about truly enhancing how we plan, manage, and execute projects at every level.
As I continue my research, I’m sure that the lessons from this article will stay with me, guiding me as I explore more specialized AI tools and their applications. And for now, I’ll keep up my weekend caffeine detox, so I can stay sharp enough to absorb everything this fascinating field has to offer.