- Almost one-third of executives use artificial intelligence and machine learning to drive data security through risk identification, early detection, operational improvement and corrective action, according to an Apex survey of more than 600 international executives.
- Personalized data visualizations and dashboards and real-time analytics for identifying fraud, dynamic pricing and product offerings tied for the second and third most popular AI/ML use cases for almost a quarter of executives.
- Companies are also using AI/ML for data integration, preparation and management, sales forecasting and personal security, according to the report.
Executives are finding a use case for AI/ML solutions across all areas of IT.
Companies that struggle to implement AI/ML capabilities are falling behind competitors. Disruptive technologies have a tendency to scare companies, but before long, these technologies blend into solutions used on a daily basis.
The companies prepared to integrate AI/ML, however, are causing a ripple effect in their industries.
McDonald's recently acquired a personalization and decision logic tech company in a bid to roll out predictive ordering for customers. The burger chain wants its drive-thru menus to make order recommendations for customers based on what's already in their order or the weather.
Starbucks is applying Microsoft's ML technology to use feedback to "make decisions in complex, unpredictable environments" for the company's mobile app users. The reinforcement technology, a branch of ML, will give customers order suggestions, similar to McDonald's plans.
Executives are pursuing a mindset focused on rebuilding technologies or creating new scalable business platforms, instead of focusing on cost alone. In the meantime, adoption rates of hyped technologies have slowed in favor of a tech refresh; companies are addressing legacy system modernization before implementing new tools.
For companies that are ill-prepared for AI/ML or lack an integrated solution approach, adoption can splinter. Pinterest relies on small AI implementations before scaling it across use cases on its platform.