Traditional AI
Terence C. M. Tse, Mark Esposito, Danny Goh, Paul Lee · 2025
Amidst the current fervour surrounding Generative AI (GenAI), this chapter revisits the foundations of traditional AI technologies that continue to deliver significant value to businesses. Our exploration highlights how traditional AI encompasses rule-based systems, machine learning techniques such as supervised and unsupervised learning, and deep learning—all of which preceded the GenAI revolution. The chapter traces AI&s;s evolutionary journey from early symbolic approaches through the “AI winter” to the resurgence driven by statistical methods and vastly improved computational capabilities. While acknowledging GenAI&s;s transformative potential, the text emphasises that organisations need not wait for GenAI maturity when proven traditional AI solutions remain highly effective. By examining the characteristics, strengths, and limitations of these established technologies, we provide a comprehensive understanding of the AI landscape beyond the headlines. This historical perspective allows for more informed decisions about implementing AI systems that align with specific business needs and constraints in today&s;s competitive environment.