How AI Is Making Micro-Mobility More Profitable
Artificial intelligence has fascinating humans for as long as computers have been around, but only in recent years has AI advanced to such a point that it’s capable of radically reshaping our lives. These days, AI is disrupting countless industries and reshaping the way we conduct business, get to know one another, and, increasingly, how we view micro-mobility.
How exactly is AI making micro-mobility more profitable, and what forthcoming developments can we expect to rock the world of transportation as we know it? Here’s how AI is supercharging micro-mobility and redefining the future of transportation.
The virtue of smallness
The staggering success of the recent micro-mobility revolution can largely be attributed to the virtue of smallness; whereas mainstream economics was once infatuated with the idea that bigger was better, recent technological developments have proven that sometimes, small, local operations which only temporarily serve customers for cheap amounts can be wildly successful. The astonishingly quick rise of the e-scooter and similar vehicles around American cities is a testament to this, and we can expect this trend to continue well into the foreseeable future.
Moving people over short distances has now been dubbed “micro-mobility,” which is a pleasant change in how we’re using language to discuss moving people from point A to point B. After all, once upon a time the public conversation surrounding mobility was centered on huge infrastructure projects designed to move large sums of people across huge tracts of land. These days, however, businesses, governments, and aspiring entrepreneurs are increasingly focusing on micro-mobility and how they can expand it with the power of artificial intelligence.
One major concern with micro-mobility is fleet management. If you’re an e-scooter company charged with getting thousands of customers to where they want to go, you need to keep track of all your equipment. This is easier said than done – until you apply powerful AI-driven algorithms to the problem, however, at which point it becomes a breeze. Companies like Avrios have raised millions of dollars in funding in the pursuit of an AI-facilitated fleet management platform. More startups like Avrios will spring up sooner rather than later, as centralized means of controlling expansive fleets of micro-vehicles will be in hot demand for the foreseeable future.
As e-scooters, mobility scooters, docked bikes, and other vehicles become more common in cities across the county, we’ll need additional AI advancements to manage the growth of micro-mobility services.
How do you find new markets?
One of the biggest issues plaguing micro-mobility focused startups is how to go about properly finding new markets. After all, many American cities are already oversaturated with micro-mobility services, so foraying into a competitive space and actually turning a profit is much easier said than done. With the help of data-driven analysis conducted by savvy AI programs, however, more companies will find it possible to find and cater to audiences hungry for micro-mobility services, which more and more people are recognizing as the future of transportation.
Because of the growing importance of data analysis when it comes to establishing new micro-mobility markets, American cities are getting much more serious about data, specifically that information which is collected on citizens who may not want anything to do with micro-mobility services. Thus, less-intrusive ways of accumulating market information will be necessary, and in that regard micro-mobility companies will be turning to AI to scrape and analyze consumer data in a non-invasive fashion. AI-driven algorithms will be imperative not just for locating new customers, but also for forecasting local demand as it suddenly waxes and wanes.
Micro-mobility is all about satisfying the demand of consumers the moment it arises; if you need an e-scooter or bike, you should be able to tap your phone and find one instantly. Humans can never offer such timely services but must instead rely on machine learning solutions which are capable of processing such demands at fantastically high speeds. Given that micro-mobility is clearly the future of urban transportation, startups based in cities will have to get started perfecting these solutions if they want to financially thrive in the forthcoming years.
Access to data is essential for the future of micro-mobility, and nothing manages huge sums of information better than AI-driven algorithms. Nevertheless, privacy concerns and the heightening need for transparency will force micro-mobility companies to rethink how they go about collecting and making use of information. Even if a business model satisfies customers, for instance, it could generate such a huge public backlash amongst those whose data is being analyzed that it could prove to be a faulty model in the long run. Don’t let the rapid adoption of e-scooters and similar tools fool you – the micro-mobility revolution is just getting started, and its future will be defined by the forthcoming restraints that society is going to place on AI-driven business models.