It feels like the right time to finally put my thoughts down on paper and publish the research I’ve been working on—a blend of practical experience, recent insights from my workshops, and the cutting-edge articles I’ve been diving into these past few months. Honestly, a large part of this urge comes from the success we’ve seen using Custom GPTs across multiple projects, especially for resource optimization and risk management.
I first introduced Custom GPTs in some of my oil and gas workshops earlier this year. We tested the models for automating reporting, improving communication among remote teams, and optimizing schedules. The results were impressive—predictive analytics allowed teams to identify bottlenecks before they happened, and tasks that previously took hours were handled seamlessly by the GPT. The enthusiasm from the participants made it clear to me: this technology isn’t just a trend; it’s here to stay. Since then, I’ve applied the same approach as a consultant with a private client, and the results have only deepened my belief that AI has tremendous potential in project management.
Given this momentum, it’s clear that now is the time to publish. But here’s the challenge: tech-related research ages fast. Submitting to established academic journals can easily take a year or more. Peer-review cycles are slow, and with multiple submissions and revisions, ideas that are fresh today could become outdated by the time they are published. This is especially problematic when dealing with something as fast-evolving as AI and large language models like GPTs.
Choosing the Right Outlets for Impactful Publishing
I’ve been weighing my options for where to submit. On the one hand, academic rigor is essential, and I want my research to hold weight in scholarly circles. But on the other hand, the goal is to make these ideas immediately accessible to practitioners who are already applying AI. This brings me to alternative outlets that balance relevance and speed, like:
- MIT Sloan Management Review (MIT SMR) – which focuses on practical insights for managers.
- PM World Journal (PMWJ) – where case studies and industry applications are welcomed.
- PMI Pulse Reports – which emphasize real-world trends in project management.
Publishing in journals like these ensures that my work reaches professionals quickly, helping them adopt these strategies before the next wave of technology renders them obsolete. Given how quickly LLMs like ChatGPT are evolving, this approach feels like the right one.
Shaping the Outline of the Articles
The outline of my work reflects both the journey I’ve been on and the insights I’ve gathered through experience and reading. I’ve divided the research into three interconnected areas:
- AI-Enhanced Automation and Workflow Optimization
In this section, I’ll talk about how AI automates repetitive tasks, streamlines communication, and integrates workflows—like the examples I shared in the oil and gas workshops. I’ll draw on tools like Taskade and ZBrain, which have proven valuable in managing remote teams. MachineMetrics’ case study will also feature prominently here, showcasing how predictive AI models improved operational efficiency in a manufacturing setting. - Predictive Analytics and Risk Management
This part will focus on proactive risk identification—an area where I’ve already seen the power of AI firsthand. In my client projects, Custom GPTs have allowed us to monitor risks dynamically, adjusting resource allocation in real time. I’ll reference Fluor’s IBM Watson case study, which demonstrated how predictive analytics could prevent costly disruptions in large-scale construction projects.I also want to include insights from He Li’s study on AI-driven risk management in project portfolios. This aligns perfectly with the use of ChatGPT in scheduling and resource management for construction, as detailed by Prieto et al. Both studies emphasize that AI’s greatest value lies in foresight and planning, which helps teams avoid risks rather than simply reacting to them. - Governance and Evolving Roles of Project Managers
As AI becomes more integrated, project managers will transition from operational roles to strategic leadership positions. I’ll explore how PMOs evolve into Centers of Excellence, guiding organizations through data-driven decision-making. This shift aligns with the idea that AI tools complement human expertise rather than replacing it—a theme emphasized in several of the readings, including Aladağ’s work on ChatGPT for construction risk management.