The introduction of Artificial Intelligence in various applications is set to overhaul the economics of multiple industries. Due to rapidly advancing technology within Artificial Intelligence, the cost of prediction is decreasing at a fast pace. This decrease in prediction costs results in projection being used to solve many new problems, even ones that we generally don’t use prediction to solve.
For example, let us consider the case of autonomous driving. Before AI, autonomous driving, as we see it today, did not exist. It merely consisted of engineers programming a vehicle to move around in a controlled environment with instructions to run in case of obstacles and following directions to reach a destination. But with the introduction of modern AI, autonomous vehicles have gotten the capability to be smarter.
AI in Autonomous Cars
Today, when an autonomous vehicle is being “taught”, a human driver is put behind the wheel and drives as they normally do. The AI then uses various sensors onboard the vehicle to observe how the human drives and comes up with its protocols for use in particular situations.
The predictions that the AI makes, in the beginning, will undoubtedly be flawed sometimes, but the AI can learn from its mistakes and update its protocols accordingly. The more “practice” that the AI gets in this way, the more accurate its predictions keep getting and can ultimately replace the human at one point. This method of “learning” by the AI works the same way wherever it is applied.
Errors in Prediction
As the cost of prediction drops, the demand for human-based prediction will decrease. Human prediction is prone to failure due to a lot of factors like human error, clouded judgment, or even negative emotions. Using AI for forecasts removes all of these problems. Hence if adequately applied, AI can make much better predictions when compared to humans. Since AI is more efficient and costs less, eventually the value of the organization or company using it goes up.
The only area where AI falls short is human judgment. An AI can make predictions and give them to a human, but it is ultimately up to the human to decide what to do with it. Some companies like Amazon are working to remove these limitations, and their work has shown that ultimately AI can be used to make judgments based on their customers' preferences and spending habits. For example, if a customer regularly orders a product, then the AI can decide to place the order for the customer when the time comes, thereby increasing the chances of selling the product.
AI will be the most beneficial to organizations that can define their objectives and goals clearly. As we have seen above, the method of “training” AI makes it essential to have clear-cut objectives to reap the benefits. We have already seen AI making substantial disruptions in industries where it has been applied.
A 2013 study conducted by Oxford University estimated that AI could replace 47% of jobs in the coming years. A similar survey conducted by OECD estimated that AI could return 9% of jobs just within the next two years. Another study conducted by Accenture concluded that over 84% of all managers advocate the implementation of AI to make things more efficient.
Hence, to conclude, AI will have drastic implications for every industry, with it replacing humans in several roles. However, the savings to be gained from AI will make business practices more efficient and increase profitability.