![]() It doesn't require a world-class data science team to implement them, though the engineering work to support them may still be substantial.įor other applications, an AI-driven product might be used to solve a specific business problem within the organization - fraud detection systems, SPAM filtering systems, network security systems and so on all fall into this category. For example, recommendation systems have been around for many years and are well tested by now. Leaders also need to understand exactly how much of their competitive advantage is derived from the benefits of these technologies so they can invest their resources accordingly.įor many applications, it is perfectly sufficient to take well-tested off-the-shelf methodologies and productionalize them for the particular environment at hand. This avoids their models collecting dust while they collect their paychecks.
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