03 June 2024 | 12:06
Jiwat Ram

AI-age terminology that project staff should know

The role and influence of artificial intelligence are expanding with every passing day. As it becomes mainstream, there is a need to raise awareness about it. This needs to be done both at personal and professional levels. While AI’s use for personal day-to-day life could happen gradually in a very natural manner, just like people started getting used to the internet, the situation may be a little bit different when it comes to the use of AI for professional purposes. 

At a professional level, it is not just about knowing the basics or understanding the utility of AI superficially. But it requires that people learn and know about AI from many perspectives, including functional, technical, legal, and ethical, just to mention a few. Hence, people across all functional domains need to be AI-ready. Project management is no exception.

In particular, as project management is regarded as the vehicle to implement change, organizations will rely on project professionals to use AI not only for carrying out project work but also using AI for the development of new products and services. Further, with the increasing use of AI within organizations, the use of AI for project work is going to become a necessity. It underlines the need for project professionals to be AI-ready. 

One way to get started on the AI learning journey is to understand various terms and concepts related to AI and its subsets, such as machine learning (ML), natural language processing (NLP), neural networks (NNs), etc. It is about going to the basics. And we all know that learning in any discipline starts with learning the lingo and terms used in order to understand the higher-order concepts and relationships among them.

With that in mind, below is a list of some useful terms related to AI and its sub-sets (e.g., ML, NLP, and NN). Certainly, it is not possible to include all terms and concepts in one list, as the list of such terms is unending (so to speak). But we have produced some key terms, contextualizing their utility in a project management context for ease of understanding.

  1. Model: A model is typically a representation (e.g., mathematical or physical) of an abstract phenomenon. It helps us understand and visualize the structure of a phenomenon or a system. In project management, AI predictive models can be built for estimation (e.g., time, cost) purposes. 
  1. Features: The term represents the measurable property or characteristics of the phenomenon. From a machine learning perspective, a feature represents the data point (or a variable or an attribute in the data). In project management, features of project data can be like task duration, team size, or project complexity. When building a predictive model for a project’s impact on social sustainability, the team can consider features like job creation, diversity and equal opportunities, health and safety, etc. 
  1. Training: This refers to teaching a machine learning model to make predictions or classifications by learning patterns in the dataset. Project teams can use historical project data to train models to make predictions of costs, time, risks, etc.
  1. Supervised learning: It is the process of using labeled data to teach a ML model to learn patterns in the data for making predictions or classification decisions. Regression or classification algorithms are used to arrive at such decisions. Data is called ‘labelled’ because it is marked for what it is. For instance, training a ML model to classify risky vs. non-risky projects.
  1. Unsupervised leaning: This is the process of using unlabeled (raw) data to train ML models to learn patterns in the data to find clusters or groups with similar data features. The data is not marked for what it is. Hence, the system tries to put together data into clusters based on the patterns it discovers through its learning from the data. The project team can use unsupervised learning to train models to identify projects that will require specific quality management strategies to ensure the best project outputs are created and the chances of project failures are lessened.
  1. Reinforcement learning: This refers to the learning of ML models based on their interactions with the environment. It can also be considered as trial-and-error-based learning. In a project management context, one example could be adjusting the project budget based on project progress and real-time analysis of project risks and issues.
  1. Federated learning: This refers to an ML-based approach where model training occurs across several decentralized machines or servers. The approach allows using local sample data without exchanging the data, which helps alleviate any concerns about data sharing. In a project management context, it could be useful if multiple parties’ data is involved and needs to be used for ML model training. The teams can collaborate to train ML models on their localized data while maintaining the privacy and security of their sensitive data.
  1. Transfer learning: This refers to using the knowledge learned from solving one type of problem to solve another type of problem, essentially transferring learning from one to the next. Project teams can look for available pre-trained ML models from related domains to see if they can use them to solve some of the problems they face in project management.
  1. Feature engineering: This is about creating, selecting, and modifying features (e.g., variables) for the purpose of improving the performance (e.g., prediction or classification accuracy) of ML models. In project management, it could be like creating a new metric for risk identification that can be used for training models to improve their accuracy of models. 
  1. Predictive modelling: This refers to building ML models that predict an outcome based on the learning of patterns in the data. In a project management context, teams can build models to predict if the project will meet its deadlines.

Concluding thoughts: 

The utility of AI and its growing influence are undeniable. Therefore, people working in any profession need to prepare themselves for using AI for personal day-to-day life and work purposes. This is particularly important for project professionals. It is because organizations are embracing the use of AI, and as projects are embedded in the organizations, people working on projects have no choice but to be AI-ready.

The situation, however, is not easy. People working in project management often feel overwhelmed by the amount of project specific knowledge that they need to learn. Let’s face it, that often means knowing a lot about a lot in the first place. So, learning a new set of knowledge that they may or may not use immediately (depending on their involvement in particular types of projects) could feel cumbersome. But that does not mean one can relax and let it pass. Because AI is going nowhere. Therefore, with an intention to build awareness, we have listed a few common terms that one should know to get started with learning about AI. The idea here is to show their linkage to PM context to make it easy. Certainly, the list and examples are neither exhaustive nor conclusive. To be honest, this is just a drop-in-the-ocean kind of list.

Written by
Jiwat Ram

Jiwat is a Professor in Project Management. He has considerable experience of working internationally in diverse cultures and business environments.

He has a growing portfolio of work on issues related to artificial intelligence, machine learning and large language models (LLMs). His work has been published in top scientific journals.

Jiwat actively contributes to project management community. More recently, he has published a number of articles on some of the contemporary issues confronting project management and business management in various industry based outlets.

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