Developing an AI use case that lays out what the project will cost, the value it will provide and the potential risks it will bring can be a head scratcher for CIOs. AI in the enterprise is uncharted territory for many companies.
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Research outfit Gartner advises that CIOs proceed step-by-step through a “framework” designed to sort out the type of AI applications, AI solutions and the core AI technologies they will need to produce the kind of results the company is looking for.
After CIOs nail down business objectives and use cases, they should start to think about how the technology will be used to integrate AI in the enterprise. Next, CIOs should look into the common solutions used by various enterprise applications such as a virtual customer assistant, an employee training tool or a process efficiency tool.
Then, CIOs should work to understand the core AI technologies that underpin these common solutions. Popular AI technologies today include machine learning, which Elliot described as “the most fundamental” AI technology, as well as natural language understanding, which enables machines to understand text and speech, and computer vision, which enables machines to see.
The final step of the framework is to think iteratively. CIOs should return to the beginning of the process and reconsider the use cases through the lens of the newly acquired context.
“Generally, the process we see being used is one where enterprises will go through that list of use cases, the technologies and how do we source this technology and then come back and refine the use case,” Bern Elliot said during a recent Gartner webinar on AI in the enterprise. “What they do is rank the use case after they’ve done their evaluation. And they pick those that are going to have low risk, high value, reasonable cost and that will get them some kind of return in the near future.”
For more on Gartner’s advice for deploying AI in the enterprise, go to this week’s Data Mill on how to use Gartner’s AI “value chain” to assess an AI project.