Is problem solving a problem for AI development?

These trends point to the fact that AI technology is being deployed to build solutions for all sorts of uses, both from a business and customer perspective. One of the key functions of AI, therefore, is to provide a decision-making answer as an output to a user-driven input or activity. As such, it won’t be an exaggeration to say that the AI system operates on the notion of giving an answer to a question in relation to the problem that the user may be interested in. Artificial intelligence technologies such as machine learning/deep learning and natural language processing (NLP) essentially infer and predict from the data fed into them and provide an output that is just like an answer to a particular question or query for decision-making and further information processing purposes.

For instance, an AI system trained on MRI scans helps with a cancer diagnosis and treatment protocols. The data fed into the system is processed by the system to generate MRI scans that help human decision-makers answer the probability of the presence or absence of cancer in the patient (NIH, 2018; Recht & Sodickson, 2020).

What it means is that AI’s functioning is predominantly problem-solving oriented. Luger (2005; p.25) concurs and highlights that AI programmes are designed to solve useful problems. As such, the term “intelligence” in artificial intelligence causes confusion for people, as AI does not possess any genuine intelligence per se.

For AI machines to be called intelligent in any extant imagination, they need to possess at least some limited intelligence capabilities similar to those a human mind possesses. It raises the question of the current AI systems’ abilities and functioning are limited by their focus on problem-solving? Is problem-solving focus a problem for the evolution and development of AI? If so, what should be done for the further development of AI?

What are the implications for project management?
Just like any other discipline, the limitations of AI, as discussed above, carry implications for project management (PM) too. In regard to PM, AI is expected to help planning and delivery of projects. Hence, the current data-driven focus of AI could lead to potential issues. If the data is biased and is of lesser quality, it will influence decision-making and planning for the projects. Moreover, if organizations do not collect and maintain data in an organized and transparent manner, it will make it quite challenging to use machine learning technologies for predictions in relation to projects. Since projects are done across all industries and economic sectors, it also adds to complications for gathering data in a transparent and structured manner to use for AI purposes.

However, all the developments in AI must not be at the cost of bringing adverse effects on humanity. While leveraging the potential of AI technology is important, any developments must be within the bounds of ethical and harmless use and should be aimed at aiding the human living experience.