Read about AI winters and more on the history of AI.
In the 1980s, symbolic AI was dominant. In that time, expert systems proliferated. Many of them have been coded in Prolog.
Today, we still have (in some areas) business rules systems and business rules engines, and the business rules approach used in business rule management systems, which IMHO are the direct successors of expert systems from the 1980s. AFAIK, a lot of business oriented software is built on similar principles. I believe that many credit (or insurance) decisions are made today automatically (and daily) in banks with such systems. Rewriting systems like XSLT are also in daily use, and are descendants of 1980's expert systems ideas. Declarative programming (including CLIPS or even make or other rule-based systems) can be viewed as the dissolution of symbolic AI ideas in the general programming and software industry (as soon as something becomes "easy" and "widespread" it cannot be called AI anymore).
Today, AI is reduced to machine learning approaches (including neural computing). What (broadly) was called AI before the 1980s is currently called AGI.
The next AI winter might be some abstract interpretation winter. Abstract Interpretation is a theory and mindset about static program analysis. Today, that AI has become a buzzword, and is sometimes presented as the solution to most software safety concerns (which IMHO it is not).
Some persons (including me) believes that symbolic AI is not entirely dead (at least when combined with other paradigms). An interesting view is that of Jacques Pitrat (a retired researcher and French AI pioneer) in his blog.
But AI (both as "artificial intelligence" as defined in Dartmouth 1956 and as "advanced informatics") systems are hard to build. Many years of effort are needed to develop them. Remember Brook's insight: "while it takes one woman nine months to make one baby, nine women can't make a baby in one month". This is true for complex and challenging software systems (which might need nine years to be completed, but we live in a world which cannot afford paying a small team of talented software researchers for nine years). For social and economical reasons that I don't fully understand (but that I do deeply regret), software has no equivalent of large long term projects like ITER (and has not even small long-term research projects lasting more than 4 or 5 years with a dozen of researchers). See also the softwareheritage project, and notice that the software domain is today less creative, as a whole, than the many ideas that flourished in the 1980s. See Liam Proven's FOSDEM 2018 talk The Circuit Less Traveled
What was mostly called in the previous century (XXth century) AI is today called AGI. The terminology has changed, and the ambition is today nearly gone. These days, in the early 2020s, AI is mostly about neural networks and machine learning. My feeling in 2019 is that AI became a useless buzzword today (it is no more about Artificial Intelligence).