As I investigate AI-based solutions to a variety of project issues, I find that my research into new theories often has practical implications. The vast range of project types and sizes makes generalizable solutions difficult. I study machine learning, genetic algorithms, and ethical issues in adopting and using AI. Here are my observations:
- AI has many branches and possibilities. How can project organizations decide which solution will work for them? Within machine learning, the popular models are supervised, unsupervised, and reinforcement learning. In genetic algorithm research, optimization problems are commonly formulated as constraint-based problem classes, such as the knapsack problem, and solved using evolutionary and swarm-based methods. There are several good large language models (LLMs), each with a differentiated focus or strength.
- Consideration must be given to ethics, accountability, security, and governance. Organizations and individuals need to be aware of and properly manage these aspects of the technology. Without clear governance and decision accountability, AI systems risk amplifying bias, obscuring responsibility, and weakening trust in project decisions rather than strengthening it. In project management, knowledge and formal training in AI lag behind adoption, leaving many practitioners ill-equipped to evaluate, select, or challenge AI-driven solutions.
- In 2017, Andrew Ng said, “AI is the new electricity,” implying that it will become pervasive in our society. From that early observation, we are now seeing the global impact. There is value in AI implementation, and we are still in the very early stages of this technological wave, especially in project management, where adoption often focuses on efficiency rather than decision quality.
Posted on: January 22, 2026 08:48 AM |
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