One of the fundamental elements of product development is solving a problem that many people have. Successful products address many local problems of scattered individuals in a unified, easily repetitive way. Think of products that enable people to conduct online meetings: as the world around us changed over the last year, face-to-face meeting shifted into screen-to-screen, camera-to-camera. A product that enables instant multi-party video calls to anyone and anywhere — as long as they have an internet connection — is indeed a great solution. In theory, AI products should follow the same principle: a repetitive solution that meets common needs shared by all users, with a set of features that are similarly used by all users.
Unfortunately, when it comes to AI things are far more complicated. Generally speaking, AI solutions create data-driven predictions to solve pre-defined problems. These problems are as diverse and widespread as the businesses that order them, across industries, markets, and business cases. Even two companies competing on the exact same market share with similar offerings typically require very different AI solutions: these two seemingly similar companies have different data, different pain points and different business objectives that AI can help solve. To do so, these AI solutions have to be hyper-customized and tailored to these needs. With AI, there really is no “one-size-fits-all.” That specificity characteristic is one of the core challenges in implementing AI at scale nowadays.
Continue reading: https://venturebeat.com/2021/08/29/the-long-tail-of-ai-problems-requires-hyper-customized-solutions-not-a-silver-bullet/
Unfortunately, when it comes to AI things are far more complicated. Generally speaking, AI solutions create data-driven predictions to solve pre-defined problems. These problems are as diverse and widespread as the businesses that order them, across industries, markets, and business cases. Even two companies competing on the exact same market share with similar offerings typically require very different AI solutions: these two seemingly similar companies have different data, different pain points and different business objectives that AI can help solve. To do so, these AI solutions have to be hyper-customized and tailored to these needs. With AI, there really is no “one-size-fits-all.” That specificity characteristic is one of the core challenges in implementing AI at scale nowadays.
Continue reading: https://venturebeat.com/2021/08/29/the-long-tail-of-ai-problems-requires-hyper-customized-solutions-not-a-silver-bullet/
