AI Engineering

            AI, or artificial intelligence, is a blossoming field in technology which, as it grows, can be made more useful for businesses and into a utility instead of a novelty. AI is already present within different products such as autofill features in Microsoft Excel and Outlook. However, businesses still struggle with accurately placing value on AI, overestimating its abilities, and underestimating the work that needs to go into it to get it to its full potential. So, what Bob Violino’s article suggests as a solution is AI engineering.

            An issue companies have with using AI is that they can be relatively easy to make, but much more difficult to put into use.  AI engineering can help companies make their prototypes into a reality and help them integrate the AI into their current systems. One of the difficulties businesses have with using AI is making sure they are getting reliable output. This is where AI engineering comes in handy. What they can do is use specific testing databases to make sure the AI is producing the desired results. Of course, this requires having access to reliable data sources. Here, Violino brings up DataOps which can be used to ensure and improve data quality. This technique is machine learning validation and can help make sure models are ready for use and will provide reliable output, which is something companies need in order to effectively use AI.

            A part of AI engineering that is important is keeping the business use in mind when working with the AI.  It is very easy to get lost in the technology side of AI, and while that is certainly important, it is equally important to align function with what the business needs and keeping the user in mind.

            I think that AI is important to furthering the capabilities of all businesses and that AI engineering is an important asset. AI engineering will help to bring more AI to its full potential and more useful to businesses investing in it. Making reliable AI that have been tested and then utilizing them is essential. AI engineering should be one of the biggest focuses when investing in AI to make sure that it is properly tested, installed, and upkept. Businesses that intend to use AI need to use a wholistic approach to it, thinking about every aspect of the AI and how to integrate it into the business. Successful companies use a systematic approach to installing AI which so far is what works best and gets the best results, getting the AI to the operational phase. I agree with Violino in that the most important aspect of AI is reaching the operational end, and to do that, companies need to use AI engineering to get it to where it can be made useful.

Source: https://www.idginsiderpro.com/article/3613152/ai-engineering-can-help-organizations-get-the-most-out-of-artificial-intelligence-deployments.html