EMA Blog: Business Intelligence and IT Management

Companies who manage to put AI capabilities into the hands of each and every business employee will be vastly successful in the marketplace. Enabling business employees to back up their decisions with data will dramatically increase staff productivity. At the same time, employees will feel empowered by their new ability to make much more informed decisions and automating certain tasks, based on intelligence derived from operations and market data that is now available to them.

“Machine learning (ML) today is frustrating. There is so much potential and the algorithms are all there, but I just do not know how I can leverage it for my organization,” says the CTO of a major professional services firm. “My CEO wants me to ‘leverage ML to lower OPEX and differentiate our service offerings, but there is nothing out there in the market that would allow me to get this done in a manner that has a high probability of success.” Then of course he asks me what I would do and where I would start, because the guy with “Machine Learning and AI” in his job title must know for sure…

What should machine and artificial intelligence (ML/AI) do for IT operations, DevOps and container management? The following table represents my quick outline of the key challenges and specific problem ML/AI needs to address. The table is based on the believe that ML/AI needs to look over the shoulder of IT ops, DevOps, and business management teams to learn from their decision making. In other words, every virtualization administrator fulfills infrastructure provisioning or upgrade requests a little bit differently. Please regard the below table as a preliminary outline and basis for discussion. At this point, and probably at no future point either, I won't claim to know the 'ultimate truth.'

What should machine and artificial intelligence (ML/AI) do for IT operations, DevOps and container management? The following table represents my quick outline of the key challenges and specific problem ML/AI needs to address. The table is based on the believe that ML/AI needs to look over the shoulder of IT ops, DevOps, and business management teams to learn from their decision making. In other words, every virtualization administrator fulfills infrastructure provisioning or upgrade requests a little bit differently. Please regard the below table as a preliminary outline and basis for discussion. At this point, and probably at no future point either, I won't claim to know the 'ultimate truth.'