The last decade has brought about a huge revolution in the form of Artificial Intelligence (AI) and Machine Learning (ML) cutting across industries. These changes brought an evolution in the overall operating scenario of companies by providing them insights to improve their product and service offerings. It wouldn’t be wrong to say that AI made lives easier through chatbots, algorithms, recommendation engines, hardware infrastructure, language processing and much more. Now, the industry is expected to experience some strategic shifts from enterprises.
AI, ML seem to be some of the most trending terms in the technological world at the moment. So what are they? And more importantly, why are they so important? The answers lay in the enormous and unimaginable benefits they are lending to MNCs (Multi National Companies).
The machine learning market is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period, says a report.
Tesla is one of the topmost automobile companies in the world. What makes Tesla even more intriguing is the whole excitement around its self driven car, which makes an intense use of AI. Artificial Intelligence is the technology which has enabled the car to be self driven, as it is capable of learning and making decisions on its own without human intervention. This is what makes it stand out from the rest of the technologies we have ever heard of. Tesla has taken excellent use of AI and Big Data for expanding its customer base. The firm has made use of existing customer databases for its data analytics using it to comprehend customer requirements and regularly updating their systems accordingly.
With more than 90 million transactions a week in 25000 stores globally, Starbucks uses Machine Learning and big data analytics to help direct marketing, business decisions, and sales. By launching its mobile application and reward program they collected and analyzed their customer’s buying habits. The users themselves have created the data by defining where, what, and when they buy coffee.
Starbucks gathers this information about their customer’s buying habits. So that even when the customer visits an offline store their system is able to identify their preferences through their smartphone. In addition to this, the app can also suggest new treats that might go with the drinks they ordered.
All this is powered by Starbuck’s Digital Flywheel Program. It is a cloud-based Artificial Intelligence engine that recommends food and drinks options to the customers who are not aware but want to try something new.
The technology is so sophisticated that the recommendations will change according to the weather on that particular day, or if it is a holiday or a weekday, or at what location you are.
JP MORGAN CHASE :
A big player in the banking and financial world. The American multinational investment bank has invested $11.4 billion in 2019. It relies greatly on data analysis and analytics along with the use of Artificial intelligence. It has recently introduced Contract Intelligence (COiN) chatbot which is capable of searching internal documents for legal and extraction purposes. Manual review of 12,000 annual commercial credit agreements normally requires approximately 360,000 hours. Results from an initial implementation of this machine learning technology showed that the same amount of agreements could be reviewed in seconds. The Emerging Opportunities Engine, introduced in 2015, purportedly uses machine learning and natural language processing to help identify clients best positioned for follow-on equity offerings. The technology has proven successful in Equity Capital Markets and the company stated their intentions to expand it to other areas, including Debt Capital Markets, but it’s unclear if this has happened yet.
These case studies of machine learning listed above would have been almost impossible to even think as recently as a decade ago, and yet the pace at which scientists and researchers are advancing is nothing short of amazing. In the coming future, we’ll see that machine learning can recognize, alter, and improve upon their own internal architecture with minimal human intervention.