To avoid being left on the sidelines by fast-developing AI, organizations should lean in but be cautious. Moving forward with the right use case and structure is key to implementing successful AI technology.
By finding, prioritizing, and narrowing your use case list based on AI-ready data, you will be in a strong position to take advantage of AI opportunities.
To overcome this hurdle, take an in-depth look at your data and conduct a feasibility study to make sure it’s ready for AI. Your use case may seem perfect but, if the data doesn’t support it, the models won’t work. By finding, prioritizing, and narrowing your use case list based on AI-ready data, you will be in a strong position to take advantage of AI opportunities.
The last step in determining your ideal use case is to ensure that there are clear metrics to support it. AI technology is somewhat like peanut butter—it can be easily spread around everywhere but won’t necessarily provide a lot of value. Make your choice data driven.
Armed with a priority use case list, or what Gartner calls the “most likely wins,” the next step is to create a product roadmap and seek C-level executive alignment and commitment. Compared to other technologies, AI carries some inherent risk—in potential misuse and other ethical issues—and executive-level commitment, including collaboration among the legal and data privacy teams, the chief data officer (CDO), and the chief information officer (CIO) will be key.
Leveraging this technology, with tools that are already available and integrated, is still going to be demanding work and will require change management and ongoing communication as there will be resistance. Despite the challenges, putting AI technology—that is available today—in the hands of your employees or customers will deliver a wealth of benefits that makes it well worth the investment.