![]() To saddle corporate IT with AI when it is completely experimental is a political folly. AI projects, on the other hand, are meant to disrupt the status quo in unknown ways. Their primary responsibility is to make the trains run and to minimize risks to the business. While there are exceptions to this, corporate IT teams are typically under-resourced and overtaxed. ![]() Corporate IT Should Manage AI ProjectsĬorporate IT already has a lot on its plate. It appears to be an expensive option but is far cheaper once you factor in all the true costs of enterprise AI systems. It seems private cloud, where your data stays within your firewall, is your best bet. You still want to be able to scale your hardware on demand, and not rely on traditional IT constructs (see below). In any case, due to training complexities of an AI system, a lot of benefits of public cloud, like multi-tenant architecture, are not applicable. ![]() ![]() CIOs need to be very careful about this, and they are. However, these vendors often use your data to train their AI models and then sell solutions based on those models to your competitors. You want it completely secured, which a lot of credible vendors like IBM promise. One of the key problems with implementing AI solutions is that of managing data. Sure, it does make sense to try new technologies, but in that case you need to manage the political costs well, because it may turn out to be vaporware. ![]() It’s not worth your time to engage with vendors who have not already solved the problem for someone else of your size and complexity. Sometimes vendors are outrightly lying, but mostly they are either too hopeful, or have made the stuff work in a limited way on some limited data. Here are seven myths about the true costs of AI systems that every executive should know:Īt the current state-of-art, the balance of probability is that it doesn’t. ![]()
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