The growing influence of artificial intelligence (AI) across a variety of domains, including healthcare, transportation, education, and wider business management, is making headlines. The advent of large language models (LLMs) such as ChatGPT has only contributed to furthering interest in the use of AI for all sorts of purposes.
One of the key subsets of AI is machine learning (ML). To put it simply, ML is a mechanism whereby a machine learns by understanding patterns in data and improves its prediction accuracy during the training process within the scope of the data fed into it and the algorithm it uses.
The applications of ML are wide and varied. It presents opportunities for its use in project management as well. In particular, as projects are often well-structured endeavours, there are opportunities to collect data about various aspects and stages of projects (e.g., initiation, planning, implementation, and handover) and use it for training ML algorithms, which could help improve efficiency in project-based work.
Despite the bullish outlook, the question of whether people within project management see it the same way or are excited about it is anybody’s guess. What this means is that the value proposition of the use of ML in project management is either not clear, not articulated, or both. Whatever the reason may be, it highlights the need to think about why people working in project management should know about ML. With that in mind, below we look at some of the broad reasons for project management people to know about ML and build knowledge about how to use ML for project work.
Needless to say, the list of reasons discussed is neither exhaustive nor conclusive by any means. It is not that there are millions of reasons to know more about ML, but surely there are plenty more than what is illuminated in the list here, which makes it vital to keep up with the pace of development in ML and AI broadly.
Reasons to know more and learn about ML
1. Everyone will be able a programmer
Everyone will be a programmer. Yes, you have read it correctly. The use of LLMs is revolutionising the way ML-based programming is going to be done. The low code or no code approach, as such, is gaining momentum. With some knowledge of how to use LLMs and how to interact with LLMs effectively, one can get the entire programme created without being technically a programmer. It does not mean that the programme created by LLM will be perfect and devoid of errors. So, there will still be a need for domain expertise to ensure the programme is correct before deploying it for operational use.
But the good news is that by becoming good at using LLM (which, in hindsight, just requires being nice to machines literally), one can take the lead to solve problems or at least try on its own before seeking the help of a domain expert. This is not a small thing by any means, and hence, people working with project management need to take advantage of LLM (LLM uses ML) just like everyone else will. It wont be an exaggeration to say that learning about ML is going to be a vital part of staying competitive in the marketplace. Therefore, it is important that people working in project management learn about ML.
2. ML will play a key role in business continuity
The current trends show that business continuity will depend on how conversant or comfortable an organisation is with the use of ML. It means organisations will rely on ML for operational and performance management. Since projects are embedded in an organisational setting, the culture or functioning of the organisation influences the project organisation’s functioning. Hence, in a scenario where a parent organisation uses ML, the expectations will be that the project organisation should be able to deal with the intricacies of working with ML. From that perspective, it is prudent for project management professionals to get to know about ML and understand how it can be used in a project environment to enhance work efficiency and project output(s).
3. Competence in data sciences will be the ticket to growth
As organisations use ML, they will be more likely to use data as a catalyst for their survival and growth. In fact, even today, organisations are heavily dependent on data. So, understanding how to work with data and extract meaningful insights to solve problems and create opportunities will become even more important. ML being data driven means people who can understand data science principles will be better positioned in the marketplace. Hence, from that perspective, data science will be a key skill, and expectations will be that people are competent in data science or at least have some good understanding of data science principles. This equally applies to project management professionals. Given the fact that projects are delivered in all sorts of industries and circumstances, it is even more vital for project management professionals to have some level of competence in data science to work effectively in a client-induced environment.
4. ML can help in project planning
The prediction power and accuracy that can be achieved through ML are huge. This presents opportunities to use ML for project planning. The data processing capabilities provided by ML enable us to use data for many projects to come up with better plans. This can be very important for large, high-stakes projects with large investments. However, to use ML effectively, project management professionals need to be skilled and conversant with the capabilities and limitations of ML to effectively use it for project planning purposes.
5. ML is useful in crack or fault line detection
The use of ML for fraud or anomaly detection is well documented. As projects are data-intensive entities, the situation is highly conducive to applying ML to project data for fraud, risk, and anomaly management in projects. Again, this can be effectively achieved when people working on the projects have an understanding of how ML works and how to use it in a project context. Hence, this is one more reason why project management professionals should know and learn about ML, its power, and its functionalities
Concluding thoughts
The advancements in AI and ML are happening at a lightening speed. Keeping up with the pace of such developments is not easy. That being the case, it is vital to brace for such developments and be ready to deal with them. This applies to people working in every industry and sector of the economy. However, as projects are delivered in all types of work situations, the need for project management professionals to be skilled and future-compliant in ML-oriented advancement is much more pronounced than ever before.
With that in mind, we have looked at some of the reasons for project management professionals to learn ML skills and become conversant and competent with ML. It will not only help in leveraging ML for beneficial purposes but will also help people understand the issues (e.g., ethics and morality of ML decisions) with the use of ML for project work. As explained in the article, the reasons discussed herein are just a few of many. It illuminates that there are many more things to consider when using ML, and we acknowledge that.
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