As artificial intelligence (AI) and machine learning (ML) continue to mature, companies are finding ways to extend the technology beyond traditional use cases. A recent piece by IDG contributor Keith Shaw explored some of these opportunities, among them:
- Computer vision: Shaw writes, “Computer vision lets machines identify people, places or objects with accuracy above human levels. Combined with ML models, computer vision can be deployed across many different areas of a business, from identifying defects in a high-speed assembly line to automating content management.” Fulfillment centers offer a great example of how this technology can enhance operations, as robots equipped with computer vision and sensors can route packages faster and more efficiently. As we highlighted in a previous APEX of Innovation post, computer vision will also increasingly be utilized by digital voice assistants to make these applications even more convenient and user friendly.
- Predicting behaviors: Anticipating future customer behavior is a perennial objective and an area in which ML is poised for innovation. Shaw’s article describes an initiative by a Domino’s franchise holder with brands in Australia and Europe that used a custom-built predictive ordering solution to help stores anticipate what pizza their customers were likely to order. According to Shaw, “This capability is part of a broader initiative to reduce pickup and delivery times: Pizzas can be ready for pickup in as little as three minutes or delivered within 10 minutes.”
- Sustainability and the environment: When you think about ML applications, it’s likely that personalization, fraud detection, speech recognition, and similar use cases come to mind. But what about reducing waste, preserving natural resources, and raising awareness of sustainability initiatives? According to Shaw, “…startup Saildrone used ML to help complete environmental projects such as quantifying the behavior and trends of fish stocks and their predators, including sharks and seals, for better and sustainable fishery management.” The organization also used ML to aid its autonomous sailing drones in circumventing Antarctica, giving researchers important insights into ocean and climate processes. For more on how ML is driving environmental initiatives, check out this previous APEX of Innovation post.
It’s evident that the opportunities inherent in ML and AI extend far beyond the traditional enterprise use cases. Is your business prepared to capitalize on this potential?