AI for Recommendations is a No-Brainer

The ways artificial intelligence can expand on present uses is nearly infinite. Already widely used to make recommendations when people are shopping, searching, watching a streaming media service or listening to streaming music, for example, AI should rather quickly emerge as a foundational tool for any “search or comparison” operation, whether the number of passengers going to the same destination, from the same starting point, is one or some other number. 

And though much of that activity will be conducted on behalf of service suppliers, it’s easy to see how AI also can be used by services targeted at consumers, or by consumers to choose their travel modes.  

For example, I frequently travel places that are between 850 and 1,000 miles distant, solo. As I basically do not enjoy driving, I won’t drive more than eight hours a day, and often restrict hours to five or so. That makes those trips two to three days, each way. 

I virtually always fly, as my manual calculations suggest flying actually is the same price or cheaper than driving, when including hotel, meal costs and fuel, with the avoided need for a rental vehicle at the destination, when necessary, ignoring transit time. 

So, eventually, I can see comparison shopping sites that use AI to allow me to compare the all-in cost of flying versus driving to destinations of that distance, for shorter or longer trips, using my own assumptions about how far I’m willing to drive in a day, my preferences for lodging and so forth. 

If I’m right, the business model is built on steering more traffic to airlines and rental car suppliers, for example, as I am pretty sure the AI is going to suggest that driving is more expensive than flying, if not by a wide margin. In my stated case I also save 3.5 to six days of commuting time, roundtrip, for each of those journeys. 

In principle, AI could allow other combinations of transit methods as well (rail or other public long-distance transportation). 

Indeed, some studies of airline pricing support the thesis that airline pricing algorithms are used to price domestic flights at levels that make fares equal to or better than the assumed cost of driving. 

With AI, the level of analysis available to potential customers and travelers should be even more detailed and blindingly fast. 

University of California, Berkeley (2018)	The average cost of flying round-trip from Los Angeles to San Francisco was $190, while the average cost of driving the same route was $215. The cost of flying was lower than the cost of driving for about 60% of the trips analyzed.
University of Chicago (2019)	The average cost of flying round-trip from New York City to Chicago was $185, while the average cost of driving the same route was $200. The cost of flying was lower than the cost of driving for about 55% of the trips analyzed.
Journal of Air Transport Management (2019)	A study of “Airline Pricing and the Cost of Driving” found that airline fares are often equal to or better than the cost of driving, especially for longer distances. The study looked at the cost of driving and flying between different cities in the United States, and found that the cost of driving was typically higher than the cost of flying for distances over 500 miles..
Journal of Transport Economics and Policy, (2018)	“Airline Pricing and the Cost of Long-Haul Travel” found that airline pricing algorithms can be used to price long-haul flights in a way that takes into account the total cost of travel, including the cost of hotels, meals, and fuel.
Transportation Research Part A: Policy and Practice	The "Airline Pricing and the Cost of Driving" (2005) study found that airlines price domestic flights to be competitive with the cost of driving, especially for shorter routes.

And no doubt AI will demonstrate that flying, whether short, medium or long distance, will frequently be cheaper than driving, for one passenger. Obviously, the economics change as the number of passengers to be moved increases. 

The point is that lots of useful AI enhancements will be made to comparison and search engines of all types, whether for restaurants, home furnishings, clothing and personal items, books and content or anything else bought at retail. 

Similar use cases will be found in business-to-business transactions as well. And there are bound to be implications for all activities and jobs that are based on expert knowledge of products, where the “value add” includes recommendations.