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4 Major Components to Artificial Intelligence That Are Easy to Get Wrong

By Redapt Marketing | Posted on December 20, 2019 | Posted in Artificial Intelligence and Machine Learning

From chatbots to fraud detection to remote resource monitoring, enterprises are increasingly adopting artificial intelligence (AI). But while it can be tempting to jump right into the game, successfully implementing AI takes an understanding of your needs and capabilities.

If you’re exploring integrating AI into your enterprise, here are four components that are easy to get wrong or overlook.

server-racks_redapt_icon_11. Storage

To be effective, AI requires access to data. Lots of data, usually from many different sources. At the same time, properly housing that data takes a large amount of storage.

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When you’re working on utilizing cloud native tech stacks on-premises or at a co-location, it can be easy to misjudge just how much storage AI needs. This can result in either not having enough storage or investing too much in an overbuild.

data-quality_redapt_icon_12. Data quality

Results from AI are only as good as the data utilized to get them.

Unless you have the fundamental data processing and data pipeline in place, you are not going to be able to get actionable data in the end and your AI model will fail. So as you build out your AI capabilities, you need to ensure the data you’re using is of a quality that can produce actionable information.

data-tagging_redapt_icon_13. Data organization

AI models require pathways in order to successfully utilize data. Put another way, they need to know where to look.

If your organization fails to properly organize your data with sourcing and tagging, your AI model will basically be lost in a forest with no sense of direction.

building-with-check_redapt_icon_14. Technical maturity

At the end of the day, this is the most important component to successfully implement AI solutions.

By understanding your own technical maturity, from what kind of data you rely upon to where your AI workloads will run best, you can build out an AI plan that is effective. Our own Technical Maturity Framework will help you nail down exactly what your business is trying to achieve and whether actually using something like AI makes sense in the first place.

For more on leveraging AI and cloud-native tech stacks in general, download our free eBook Migrating Cloud-Native Tech Stacks On-Premises.

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