A Deeper Look into the Fourth Industrial Revolution
Industrial revolutions only happen once in a blue moon, and yet, we find ourselves in the process of a profound revolution, the Artificial Intelligence (AI) revolution. Over 200 years ago, we experienced the first Industrial Revolution when the steam engine was invented. A century later, we invented electricity, and a century after that, the internet. Like all of these discoveries, artificial intelligence has revolutionized our economy and has disrupted every industry that you can think of.
But first, what makes AI so revolutionary?
Like the steam engine and electricity, AI has extended society’s upper bound on productivity. Unlike humans, who are inefficient and prone to making errors, algorithms can work 24/7 and aren’t prone to the same mistakes that humans make. Beyond productivity, the current capabilities and future potential of AI are essentially limitless. AI applications have led to enhanced automation of complex processes, personalized customer experience, improved risk management, and more.
In this article, we’ll see how AI has revolutionized several industries.
Artificial Intelligence and Finance
Financial Services is one of the few industries that have significantly adopted artificial intelligence practices, and consequently, some companies have seen a profit margin of over ten percent higher than the industry average, according to a McKinsey report.
Below are some common applications of how AI has changed the finance industry.
Generally, AI applications seek to increase revenue or cut costs. But when it comes to fraud prevention, AI does both. There’s an expense to having to pay customers their money back and there’s also lost revenue in not being able to invest that money. In 2016, $16 billion was stolen due to fraud and identity theft, which is why it’s one of the biggest applications in AI. By analyzing clients’ activities, location, and buying habits, fraud detection models are able to flag events that seem suspicious or unusual.
One of the biggest challenges traders face is managing their emotions. Developing a sound trading strategy is one thing, but sticking to the strategy regardless of what turmoil you face is another. With algorithmic trading, however, it eliminates the emotional aspect of trading. Machine Learning and Artificial Intelligence have significantly improved algorithmic trading capabilities, allowing algorithms to learn and improve each day as it’s fed more data.
Many fintech companies are leveraging AI to provide personalized banking services for their customers. A customer can get a unique portfolio of financial instruments based on their risk appetite, and he/she can also get a personalized financial plan based on their spending patterns, income, and goals. You can expect to see even more personalized banking services as AI progresses.
If you want to learn more about AI’s impact in the finance industry, check out this article, “2020 AI Trends in Banking”
Artificial Intelligence and Marketing
For the longest time, marketing was more of an art than a science, until recently. With the emergence of machine learning and artificial intelligence, data scientists are able to quantify marketing decisions and enhance marketing practices overall.
One of the biggest problems in marketing was figuring out how to quantify the impact of various marketing channels. This is especially difficult when it comes to offline marketing channels, like TV, billboards, or radio.
That being said, there’s been an emergence of two popular marketing modeling techniques to solve this problem, attribution modeling and marketing mix models. Attribution modeling used to determine how credit for sales and conversions is assigned to different touchpoints in a customer’s journey (eg. a customer sees a Facebook ad, then a YouTube ad, then an SEM ad). The problem with attribution models is that they don’t account for offline channels, which is where marketing mix models come into. A marketing mix model is a form of multivariate regression that seeks to estimate the impact of marketing channels, based on dollars spent, to determine the impact on conversions or revenue.
Customer profiling, or customer segmentation, is the practice of identifying one’s customers better through profiling them. By using clustering techniques and other machine learning techniques, marketers can better understand customer demographics (age, gender) and geography (location). By doing so, marketers can better target advertisements and messaging to connect with their target market.
Artificial Intelligence and Logistics
Normally, logistics isn’t the hottest topic in business, but not when it comes to artificial intelligence. In fact, some of AI’s greatest potential can be seen in logistics, including automated warehouses and automated vehicles.
When you think of automated warehouses you’re probably thinking of Amazon’s warehouses with hundreds of thousands of mobile robots moving inventory from point A to point B. And while that’s true, there’s actually a lot more to it. Automated warehouses also mean using data to optimize inventory levels, resulting in less warehouse space needed, lower transportation costs, and lower costs overall.
Another use case of artificial intelligence in logistics is autonomous vehicles. As I said earlier, humans are inefficient and prone to error. This is especially the case when it comes to transportation. Humans need to sleep, eat, use the washroom, take breaks, etc. With autonomous vehicles, no longer will transportation be limited to eight hours a day, 5 days a week.
Tesla is the prime example of autonomous vehicles, building not only consumer vehicles but also self-driving trucks. Another example that’s less commonly known is Rolls-Royce and Intel. Together, they built an Intelligence Awareness System that opened the opportunity for autonomous ships.
Artificial Intelligence and Retail
In arguably one of the most competitive industries, retailers are finding innovative ways to stand out and add value through AI. Artificial intelligence is being applied in many ways across the entire product and service cycle. Below are a few of the many use cases for AI in retail.
Chatbots and Robot Assistants
Chatbots have significantly improved over the past few years. While they aren’t yet capable of fully replacing customer service representatives, they are very good at answering simple questions and are used to guide customers to the right support teams. What’s even more interesting than chatbots are robot assistants — Pepper, a robot designed by Softbank, is a social humanoid robot that’s used in physical retail stores to engage with customers and provide assistance.
Some retail companies are also using AI to provide customers with personalized recommendations. Frank and Oak claims to use AI to provide a style subscription box of clothes that’s unique for each customer by asking a series of questions. Amazon is another great example that uses AI to provide recommended products based on previous search history and purchase history.
Artificial Intelligence and Telecommunications
The telecommunications industry had an estimated value of $1.4 trillion — with a value that large, every difference matters, which is why telecom companies have found several AI use cases to improve their customer experiences and ultimately maximize profits. Below are three main applications of AI in the telecom industry.
Churn Prediction Modeling
Customer churn is defined as the rate in which customers stop doing business with an entity. Because customers technically provide perpetual income in the telecom industry, the cost of a customer churning is high. Thus, telecom companies have leveraged AI to predict when a customer is likely to churn based on levels of activity, the number of complaints, etc.
AI has become pivotal in building self-optimizing networks, giving operators the capabilities to automatically optimize networks based on traffic data. Already, more than 60% of operators are investing in AI systems to improve their networks according to the IDC.
I think we can all agree that one of the biggest detractors in telecom services is network failures — don’t you hate it when your wifi doesn’t work? Well, companies are now leveraging AI to predict when networks are likely to fail based on the state of equipment and by analyzing network patterns so that they can proactively prevent network failures.