That notion is belied by Scotiabank (officially the Bank of Nova Scotia), which has pursued a results-oriented approach to artificial intelligence over the past two years. While some of its resources are devoted to exploring how new technologies -- including blockchain and quantum computing -- might drive fresh business models and products, the great majority of its data and AI work is focused on improving operations today rather than incubating for the future. By all accounts, this integrated reporting structure is what allowed Scotiabank to move rapidly to gather and manage the necessary data and put analytics and AI capabilities in place. The analytics application uses a machine learning model -- called the Customer Vulnerability Index -- to identify consumers who are likely to have short-term cash-flow issues, using transactional data such as deposits and spending levels. It has found substantial returns from automating tasks in the back office of its global banking marketing division and improving security on the front line.

Some organizations may feel that acquiring AI capabilities is a race, and if a company starts late, it can never catch up. That notion is belied by Scotiabank (officially the Bank of Nova Scotia), which has pursued a results-oriented approach to artificial intelligence over the past two years. While some of its resources are devoted to exploring how new technologies -- including blockchain and quantum computing -- might drive fresh business models and products, the great majority of its data and AI work is focused on improving operations today rather than incubating for the future. As a result, Scotiabank -- one of the Big Five banks based in Canada -- has caught up to competitors in some crucial areas. It has done so by more closely integrating its data and analytics work; taking a pragmatic approach to AI; and focusing on reusable data sets, which help with both speed and return on investment.

Keith Hausmann, Chief Revenue Officer, Globality, explains how the AI revolution is transforming the way companies source complex categories – typically services, delivering faster, better decisions; more autonomy for stakeholders; and greater opportunities for procurement professionals to drive innovation. With the huge volatility seen in today's business environment – and in the world more generally – global enterprise companies are facing unprecedented challenges. They need to increase their adaptability and resilience to withstand future economic shocks, all while keeping costs down and driving innovation in order to remain competitive. To succeed in these demanding conditions, many businesses are turning to AI (artificial intelligence) and automation in order to find ways to improve decision-making, accelerate routine processes, and free up their people to focus on driving more strategic problem-solving and growth-related activities. Given AI's seemingly limitless potential, no wonder that it's being increasingly used by procurement functions.