Artificial intelligence (AI) has long been used as a plot device in science fiction, from the adorable “droids” in “Star Wars” to the more sinister “replicants” in “Blade Runner.” But while human-like, bioengineered androids remain the stuff of imagination, AI is being adopted by a wide range of industries today.
Manufacturers have employed robotics for decades, but AI is now enabling advanced robots that can move around the factory floor and learn to solve problems. AI is being used to analyze the movement of stock prices and automate trades, and to improve diagnostics in healthcare. More visible applications of AI include autonomous cars and “intelligent assistants” such as Siri and Alexa.
Gartner has predicted that, by 2020, AI will be a top five investment priority for more than 30 percent of CIOs. The research firm notes that in January 2016 “artificial intelligence” was not in the top 100 search terms on gartner.com. By May 2017, the term ranked No. 7, indicating the popularity of the topic and interest from Gartner clients in understanding how AI can and should be part of their business strategies.
AI is an overarching term that encompasses a variety of technologies, including machine learning, computer vision and natural language processing. It refers to systems that change behaviors without being explicitly programmed, based on data collected, usage analysis and other observations.
While there is a widely held fear that AI will replace humans, the reality is that today’s AI and machine learning technologies can and do augment human capabilities. Machines can do some things better and faster than humans, once trained. Machines and humans can accomplish more together than separately.
Nevertheless, AI is a disruptive technology. It promises to change business models and processes in ways that are hard to imagine, and few organizations have fully developed plans for how to integrate AI into their operations. Gartner’s 2017 AI development strategies survey found that organizations are currently seeking AI solutions that can improve decision-making and process automation. However, more than half of respondents indicated that the lack of necessary staff skills was the top impediment to adopting AI.
Because AI requires substantial processing power, the graphics processing unit (GPU) has emerged as a driving force behind AI applications. Originally used to generate smooth, lifelike graphics in video games, GPUs are designed to handle mathematically intensive tasks very efficiently. They have thousands of processor cores that can perform millions of calculations simultaneously, making them ideal for AI and machine learning. However, few organizations are in a position to invest in GPU technology without a clear business case for AI.
Meanwhile, the cloud is making AI resources more accessible. Watson on the IBM Cloud enables software developers to tap the deep-learning and data analysis capabilities of IBM’s Watson AI platform, while Azure Machine Learning lets users run deep-learning training jobs, real-time analytics and other accelerated tasks in Microsoft’s Azure cloud platform.
Organizations that use AI technologies seem to experience immediate results. In a recent survey by Infosys, companies with mature AI strategies reported faster revenue growth over the past three years compared to companies without such strategies. Three-fourths of survey respondents said AI is pivotal to their continued success, and 64 percent said future growth is dependent on large-scale AI adoption.