Many companies will fail in their implementation of various AI projects.
Many companies underestimate the cost and effort associated with advanced automation / “AI” projects. In addition, companies often fail to clearly define the business problem they wish AI to solve upfront in their AI project process.
1.) Define the Business Problem: This is not so much a cost issue as it is an operational issue. Too many companies fail to define the business problem they wish AI to solve. Your AI project will fail if you are not clear about the project’s objective.
2.) Data: The quality of a given company’s Enterprise Data is key to implementing a successful advanced automation project. For example, a machine learning (ML) platform will only be as effective as the underlying ML models which will only be as effective as the quality of the data those models are trained on. Companies must first define the relevant Enterprise data sets before feeding that data through to ML models for training purposes. Once identified, the data must be cleansed to ensure the integrity of the data set. Once cleansed, that data ought to be tagged/labeled appropriately so as to properly train the ML models. This process of identifying, cleansing and tagging/labeling data is a painstaking, expensive process but worth it if you want to extract value from the ML models. Just remember the old saying as it relates to data: “garbage in, garbage out”.
3.) Qualified People: There are not a sufficient number of experienced professionals who are qualified to work with Enterprise data (data cleansing / data prep; data tagging / model training) as it relates to broadly-defined “AI” projects. Companies often fail to scale when it comes to data labeling/tagging for purposes of training ML models. It would make sense for Enterprises of all sizes to consider outsourcing Core AI elements to Microsoft Azure, Amazon AWS or Google Cloud Platform rather than build the entire AI stack in-house.
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