Despite public dispositions of terminator-esque results, and apocalyptic amounts of data accumulation, artificial intelligence is making big steps toward diminishing irrational decision making.
Consumers and buyers have always approached decisions with diffidence and certain inefficiency. Think of the consumers and buyers decision as a language barrier. In the modern world the consumer communicates what they want through action. For example on freemium platforms such as spotify or matching markets like eBay, consumers listen to songs they like and buy items they want, these actions prompt recommendations, such as “artists recommended for you”, or “others also purchased”, which try to predict and encourage consumers to behave in a certain way, or continue to follow a certain pattern. In a perfect world, the buyer could predict exactly what the customer wants and when, eliminating decision making from the equation, transitioning the consumer to plainly subservient capacities.
Data collection and utilization has been the cornerstone in recent economic and business advancement. With data, predictions can be made, and with accurate predictions comes more identifiable profit. There is only so much data that humans can compute, understand, and then make decisions upon. Artificial intelligence is the solution for too much data, and in a world defined by consumerism, data collection has become an undeniable facet in understanding consumer behavior. What AI offers is the potential to look for patterns, which are just logical extensions of legitimate data collection, allowing it to determine the best possible outcome given the available inputs.
Artificial intelligence has been in the headlines for nearly a decade now, this is what makes it important now. AI needs data to observe. For the last 3 years early adopters of AI, that have been been data mining through companies like Salesforce over the last decade, are starting to see some serious returns to their investment in artificial intelligence. These returns are predated by what is called the valley of despair. Essentially when a groundbreaking advancement becomes viable, there is mass amounts of hype and media traction, drawing in a multitude of individuals which mean to implement it. Then there is a big crash do to a lack of experience. Many groundbreaking software such as Salesforce’s customer relationship management (CRM) took years to find legitimate traction in big business, and now Salesforce is grossing over 2.5 billion in profits. The valley of despair for artificial intelligence has come and gone.
The prerequisites to efficiently use artificial intelligence exist in few major companies at this current moment, yet these precursor companies like Amazon and spotify headline this second surge in efficiency because they have had the data prerequisites that needed to be in place for AI to become efficient. Artificial intelligence learns, it is the first ever learning capital, which means that it can take years for it to begin efficiently providing suggestions. Amazon and other early adopters are proving that AI works, and everyone wants in on the action.
Will artificial intelligence take human jobs? Similar to the IT scare of the late 90’s, AI won’t take jobs from individuals, it doesn’t perform physical labor, and actually requires teams of additional professionals to monitor it. What it will certainly do is move jobs around, replace certain professions with new ones, and limit local job availability in the long term. Similar to how India is on the cutting edge of IT, understanding AI is something that takes time and practice, and most likely will be necessary in upholding and implementing most AI into big business platforms. Since AI takes years to become efficient it will give time and reason for companies and individuals to adapt and make necessary changes towards understanding artificial intelligence, effectively lowering the division of labor over time, as it learns to do more with the data it is provided.
Helpful Sources:
“The Economics of Artificial Intelligence.” McKinsey & Company, McKinsey Analytics, www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-economics-of-artificial-intelligence.
Marr, Bernard. “The Economics Of Artificial Intelligence – How Cheaper Predictions Will Change The World.” Forbes, Forbes Magazine, 10 July 2018, www.forbes.com/sites/bernardmarr/2018/07/10/the-economics-of-artificial-intelligence-how-cheaper-predictions-will-change-the-world/#7e21ec935a0d.