As shown in this chapter, when approaching implementing AI, it’s critical to look at two paths. The first is to get the maximum use of any third-party systems that use the technology. But there should also be a focus on data quality. If not, the results will likely be off the mark.
The second path is to do an AI project, which is based on your company’s own data. To be successful, there must be a strong team that has a blend of technical, business, and domain expertise. There will also likely be a need for some AI training. This is the case even for those with backgrounds in data science and engineering.
From here, there should be no rush in the steps of the project: assessing the IT environment, setting up a clear business objective, cleaning the data, selecting the right tools and platforms, creating the AI model, and deploying the system. With early projects, there will inevitably be challenges so it’s critical to be flexible. But the effort should be well worth it.
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