How firms are using artificial intelligence to up their game
After decades of false starts, artificial intelligence (AI) is already pervasive in our lives. Although invisible to most people, features such as custom search engine results, social media alerts and notifications, e-commerce recommendations and listings are powered by AI-based algorithms and models. AI is fast turning out to be the key utility of the technology world, much as electricity evolved a century ago. Everything that we formerly electrified, we will now cognitize.
AI’s latest breakthrough is being propelled by machine learning—a subset of AI which includes abstruse techniques that enable machines to improve at tasks through learning and experience.
Although in its infancy, the rapid development and impending AI-led technology revolution are expected to impact all the industries and companies (both big and small) in the respective ecosystem/value chains. We are already witnessing examples of how AI-powered new entrants are able to take on incumbents and win—as Uber and Lyft have done to the cab-hailing industry.
Predictive analytics, diagnostics and recommendations: Predictive analytics has been in the mainstream for a while, but deep learning changes and improves the whole game. Predictive analytics can be described as the ‘everywhere electricity’—it is not so much a product as it is a new capability that can be added to all the processes in a company. Be it a national bank, a key supplier of raw material and equipment for leading footwear brands, or a real estate company, companies across every industry vertical are highly motivated to adopt AI-based predictive analytics because of proven returns on investment.
Japanese insurance firm Fukoku Mutual Life Insurance is replacing its 34-strong workforce with IBM’s Watson Explorer AI. The AI system calculates insurance policy payouts, which according to the firm’s estimates is expected to increase productivity by 30% and save close to £1 million a year. Be it user-based collaborative filtering used by Spotify and Amazon to content-based collaborative filtering used by Pandora or Frequency Itemset Mining used by Netflix, digital media firms have been using various machine learning algorithms and predictive analytics models for their recommendation engines.
In e-commerce, with thousands of products and multiple factors that impact their sales, an estimate of the price to sales ratio or price elasticity is difficult. Dynamic price optimization using machine learning—correlating pricing trends with sales trends using an algorithm, then aligning with other factors such as category management and inventory levels—is used by almost every leading e-commerce player from Amazon.com to Blibli.com.
Chatbots and voice assistants: Chatbots have evolved mainly on the back of internet messenger platforms, and have hit an inflection point in 2016. As of mid-2016, more than 11,000 Facebook Messenger bots and 20,000 Kik bots had been launched. As of April 2017, 100,000 bots were created for Facebook Messenger alone in the first year of the platform. Currently, chatbots are rapidly proliferating across both the consumer and enterprise domains, with capabilities to handle multiple tasks including shopping, travel search and booking, payments, office management, customer support, and task management.
Royal Bank of Scotland (RBS) launched Luvo, a natural language processing AI bot which answers RBS, Natwest and Ulster bank customer queries and perform simple banking tasks like money transfers.
If Luvo is unable to find the answer it will pass the customer over to a member of staff. While RBS is the first retail bank in the UK to launch such a service, others such as Sweden’s SwedBank and Spain’s BBVA have created similar virtual assistants.
Technology companies and digital natives are investing in and deploying the technology at scale, but widespread adoption among less digitally mature sectors and companies is lagging.
