Artificial intelligence (AI) goes mainstream has been saved
Artificial intelligence (AI) goes mainstream
It’s time for the technology leaders across the board in every industry to discuss how AI can be used to improve quality, speed, functionality, and even drive top line revenue growth. A confluence of forces has propelled artificial intelligence into the business mainstream. Add it to the growing list of potentially disruptive forces CIOs can introduce into their organizations for commercial benefit.
Is artificial intelligence poised to disrupt your industry?
The term “artificial intelligence” (AI)-referring to the use of computer systems to perform tasks that normally require human understanding—has been around for nearly 60 years. But it is only recently that AI appears on the brink of revolutionizing industries as diverse as health care, law, journalism, aerospace, and manufacturing, with the potential to profoundly affect how people live, work, and play.
A number of forces have converged to bring AI into its own. Increased processing power makes it possible for computers to execute complex tasks at speeds once unimaginable—at a cost that has fallen rapidly. The ramp-up in cloud computing and the outsourcing of data storage, which has come down significantly in price, have allowed companies to develop and use AI applications. Mobility and bandwidth ubiquity make it possible for workers to access applications from most remote locations. Finally, our increasingly sophisticated understanding of how the human brain works and our ability to embed brain-like elements into computers have engendered such capabilities as voice and pattern recognition, natural language learning, and machine learning.
Within the next three to five years, we expect there will be an exponential increase in the number of commercial AI-based applications. A Deloitte study titled Cognitive technologies: The real opportunities for business published earlier this year concluded that AI applications fall into three broad categories:
Product applications embed AI in a product or service to provide end-customer benefits. Examples include Netflix’s recommendation engine and the use of computer vision to improve car safety.
Process applications incorporate AI into an organization’s workflow to either automate processes or improve them by augmenting worker effectiveness. Automated voice response systems have been used for some years now to replace human customer service agents for first-tier customer support. The Hong Kong subway system employs AI to automate and optimize the planning of workers' engineering activities, building on the learning of experts.
Insight applications harness advanced analytical capabilities such as machine learning to uncover insights that can inform operational and strategic decisions across an organization. For example, chipmaker Intel employs a predictive algorithm to segment customers into groups with similar needs and buying patterns. It then uses this information to prioritize its sales efforts and tailor promotions. The company expects the approach will generate an additional $20 million in revenue once it is rolled out globally.
These examples are just the tip of the iceberg, and more are reaching the market every day. Leaders in all industries need to be thinking about whether, how, and where they should be investing in AI-based technologies. This means understanding the available AI technologies and then analyzing existing and potential business processes, staffing models, data assets, and markets to identify ways that AI can be used to improve quality, speed, and functionality, as well as to drive top-line revenue growth.
But imagining the possible is not just about the opportunities. Executives need to put on their “paranoia hat” and envision where AI has the potential to disrupt their business or even their entire industry. Now is the time to have this discussion. In three to five years it may be too late.