Improvements in Capabilities Make AI Viable for the Enterprise
Artificial Intelligence has long been viewed as the solution to all of the problems we will encounter a decade from now. Ever since the term was coined in the 1950’s, it has been peering over our shoulder, teasing us as seemingly within reach but simultaneously just beyond our grasp. Bold predictions about its power and proximity of use go unfounded, and we resign our hopes for AI to the future while maintaining the norm in the now.
Many of the difficulties in creating more advanced AI lie within a theory known as Polanyi’s Paradox; many of the things that humans know, whether it be how to play a hand of poker or how to recognize a face, cannot be easily explained, to either another human or a machine, and are instead tacit knowledge that is acquired through some form of the human experience. This is natural advantage humans have long held over artificial intelligence, which is not capable of “unsupervised learning,” as it were, instead reliant upon personal instruction.
But the time may have finally come. AI advances, coupled with huge steps in machine learning, have resulted in huge improvements in the capabilities of machines to accomplish goals that previously were left to the human mind. Tasks relating to perception and cognition have seen serious progress in just several years; the error rate of speech recognition has dropped by nearly fifty percent since 2016, and companies like Google and PayPal are using machines to independently enhance and protect their systems.
But how can enterprises more reliably and effectively take advantage of AI? Most of these businesses have remained profitable by controlling costs and exploiting economies of scale, which offers stability and predictability as it pertains to costs and profit, but very little when it comes to creating growth or sensing opportunity and threat rising and falling from within the market. They operate on a very large, very slow scale and are built on a foundation of huge wells of data, each one limited to one aspect of their internal operation.
If enterprises, instead of relying on these large warehouses of data, can focus on specific use cases within industries, they will find an increase in demand, a lift in revenue, and an improvement in sales. Specific, valuable use cases are what drive data monetization, especially when spanning multiple, horizontally-oriented functions.
Another great advance in the development of AI is the decrease in the length of the implementation cycle. Previous systems took years to develop and were borderline outdated by the time they were fully operational and maximized. Now, within three months, data can be brought together to create initial automated actions and construct a client-specific view of the market. And thanks to the continuous stream of data, the system will always be updating and improving to fine-tune and validate its recommendations in the market. Every piece of data helps the AI devise a more complete view of the market, and unlike old systems, there is no level-off at a certain point; the more data you have, the better the computer’s predictions will be.
Enterprise AI has also been used to drive retail. Matching sales, production, and resources with consumer demand can be achieved through a cross-channel analysis of the market through artificial intelligence. Data tells you who your customers are and how to market to them each individually, and then to prioritize outlets and discover demand to allow you to efficiently meet the consumer’s needs.
Artificial intelligence has been used by trucking companies to optimize their routes and even to offer advice to their drivers while in the cab, such as whether to speed up or slow down. Reducing delivery time and optimizing fuel use has saved one European trucking company over 15% in fuel costs from sensors that monitor driver behavior and vehicle performance. Similarly, airlines have used AI to predict problems such as airport congestion and bad weather, helping them avoid cancellations that can prove extremely costly.
Customer service and marketing fields have also benefitted greatly from the growing versatility of AI, thanks to the improvement of both AI pattern recognition and voice recognition. Companies like Amazon and Netflix that use “Next Product” recommendations in order to target individual customers can see large sales increases by using machines to determine what products a consumer is more likely to buy based on their purchasing history. And any company with an automated customer service system will benefit not only from better voice recognition, but from improving voice analysis that allows automated answering systems to recognize when a customer is becoming upset and automatically transfer them to a human representative.
Deep neural networks, the technology behind machine learning and advanced AI systems, was shown to improve upon the performance of other analytic techniques in 69% of cases. Use cases show that modern deep learning AI has the ability to boost value above traditional AI by an average of 62%, with industries like retail and transport seeing a boost of almost 90%, and travel enjoying a fantastic 128% benefit with modern AI as opposed to traditional. When aggregated, AI has the potential to create up to $5.8 trillion, which would be 40% of the overall potential impact of all analytics techniques.
Retail stands to gain the most from advanced AI in marketing and sales areas such as pricing and promotion, where they could gain $500 billion dollars a year. Combining that gain with advances in task automation and in inventory and parts optimization, the retail industry as a whole could see up to a trillion dollars as a result of new AI techniques.
Consumer package goods can see the greatest benefit in supply-chain management and manufacturing, where predictive maintenance and inventory optimization could result in a net of $300 billion dollars a year. There are dozens of areas in which goods enterprises can improve themselves with AI, among them yield optimization, sales and demand forecasting, budget allocation, and analytics-driven hiring and retention.
Banking can see additional profits by applying enterprise AI to their marketing, sales, and risk sectors. Just those three could allow the banking industry to see another $300 billion. Using advanced analytics to handle, as we’ve discussed, customer service, risk assessment, pricing, and customer acquisition.
The discovery of farmer/customer archetypes has been driven by AI. Every farmer has a “digital fingerprint,” created through characteristics such as crop yield, geography, operational performance, among many others. Products are then rated for customers to reveal taste and preference, specific to product attributes, which in turn boosts products that have many similar attributes as the most popular products. These attributes becoming more or less popular provide the farmers with insight into consumer trends, and provide the customer with thematic farmer archetypes. The creation of these archetypes helps the underlying enterprise AI undercover the true drive in demand for products, find lookalike customers, focus growth targeting, define targeted customer concepts, and target promotions to simulate cross and upsell.
