Investment in artificial intelligence technologies has exploded over the last half-decade thanks to impressive early returns in five key industries, according to a new McKinsey report.
Roughly 2.2 exabytes — or 2.2 billion gigabytes — of data is produced every day. Our inability to process such huge volumes of information even with traditional computing methods has led to a renewed wave of interest in a technology that has captured the imaginations of computer scientists since the 1950s: artificial intelligence (AI).
In fact, according to a recent report from the McKinsey Global Institute (MGI), the number of think pieces and academic articles addressing AI quadrupled from 2014 to 2016. That spike in public interest was matched in the business world: MGI estimates that investment in AI technologies enjoyed a 40% compound annual growth rate (CAGR) between 2013 and 2016, a 10% increase over its CAGR during the previous four-year interval.
This impressive expansion of global AI investment has been driven by promising early returns in a number of vanguard industries like retail, electric utilities, manufacturing, healthcare, and education, all of which are covered in some detail by the MGI report.
Inventory management has always been one of the most challenging aspects of running a retail operation. If a retailer orders too much of a particular item, excess units will sit idly on its shelves for months; if they don’t order enough, they risk losing potential sales. According to the MGI report, by leveraging an AI platform to forecast its product needs, a retailer can reduce excess inventory by up to 20%, and cut lost sales by an astounding 65%.
For ecommerce retailers, AI facilitates dynamic pricing based on the day of the week, the current season, the time of day, the weather, current competitors’ prices, and more. This enables online retailers to charge each customer the highest possible price they are likely to pay, a critical differentiator in an ecommerce space defined by razor-thin margins.
AI has immense potential at nearly every link in the electricity value chain. As the MGI report summarizes, “Machine learning can help optimize wind turbines’ yield based on their own past performance, real-time communication with other wind farms, the grid status, and changes in wind speed and direction.” GE Renewables predicts that this kind of machine learning-driven optimization could increase energy production by nearly 20%, creating an additional $50 billion of value across the wind power industry.
For consumers, AI-powered “smart” energy meters can optimize a home’s energy consumption, all but eliminating energy waste. This technology could save the average consumer 4-12% on their monthly energy bill, according to MGI’s estimates.
Both the R&D and production sides of manufacturing are ripe for AI disruption. By executing test and run scenarios at superhuman speeds, AI-based analytics platforms can help companies streamline the product development process, significantly reducing times-to-market. In fact, MGI reports that Intel achieved a 10% higher yield for its integrated-circuit products by introducing a single AI-based analytics component into its R&D cycle.
Similarly to its value-add in retail, AI can help manufacturers improve their input forecasts, ensuring production never has to slow down due to a lack of materials. As automation continues to find its way into the industry, AI will also play a critical role in minimizing machine downtime and maximizing machine performance through pinpointed preventative maintenance.
AI’s ability to analyze huge datasets at an incredibly rapid pace has the potential to revolutionize the healthcare industry as we know it. Everything from diagnosing specific patient conditions to curbing the spread of infectious diseases depends on analyses of patient histories, medical images, epidemiological statistics, and more, all of which can be done quicker and, eventually, more effectively, by an AI than by a human doctor.
All told, AI-driven improvements to the precision of patient treatments stand to reduce overall healthcare expenditures by up to 9% and increase average life expectancies by 0.2 to 1.3 years.
In an ideal world, a teacher would be able to adapt their instruction to suit the unique needs of each and every student. But instructors today deliver a single, standardized lesson to entire classrooms, leaving struggling students behind and fast learners bored out of their mind. AI promises to help teachers tailor their lesson plans on a student-by-student basis, integrating reams of performance data to facilitate “just in time learning” — that is, the delivery of the right educational content to the right student at the right time.
And as natural language processing becomes more sophisticated, AI platforms will be able to handle many of the monotonous tasks that currently monopolize teachers’ time. In fact, all the way back in 2012, a study conducted by the University of Akron found that an AI-based grading tool and a human grader returned matching marks for nearly 85% of a 16,000-essay sample.
While not highlighted in the MGI report, marketing is another field in which AI has already proven its worth. A tool like Albert™, the world’s first fully autonomous AI marketing platform, can execute the kind of high-volume, multivariate testing necessary to pilot thousands of micro-campaigns simultaneously, guaranteeing that a company’s ad spend only goes towards promoting the most effective messaging.
By allowing Albert to handle the “nitty gritty” details of ad spend optimization, marketers are able to dedicate more time to the tasks that marketers do best — big picture strategy and creative production. Albert has already helped companies in a wide range of industries bolster their bottom lines, and can help any company take its first step into an AI-driven future.