AI News, What Executives Should be Asking about AI Use-Cases in Business

What Executives Should be Asking about AI Use-Cases in Business

When contemplating a new venture into AI or machine learning, companies need to take on a number of important considerations that relate to talent, existing data, and limitations.

“So AI and analytics are often used to improve decision-making or automation.” In many use cases, Lorica says teams which create a successful AI model often take the following steps: When discussing the example of creating an AI algorithm to organize and rout customer support tickets to the right staff member, Lorica noted important steps to take for a smooth implementation process, as well as what questions a business leader may answer with a case study: “When routing support tickets, we want to be able to get those to the right people.

It would be worth looking at: “In that specific example…the data is already available — but right now, a lot of the routing and a lot of the work is being done manually …You might already have an initial corpus of data, and more importantly, that data has been routed properly by a human domain expert.

With continuous research showing that there is a growing talent gap in the areas of AI and machine learning, a business leader may want to use case studies to see how companies assigned or trained team members to handle an AI project, or how they scouted an ML or AI expert.

The chart below, from O’Railly’s recent study, shows how most participants said that their number one bottleneck was a growing talent gap For example, if data has not been collected or cleaned, a company may need a team of data scientists or data engineers to find and organize it in preparation for an AI project.

“At this stage in the revolution of these technologies, you have access to cloud computing services so you can go that rout if your comfortable using managed services — Which means you might need to have less people to maintain infrastructure particularly on these open source projects.

… Not only do they have the technical skills, but they have the business sense to identify the opportunities where the solutions can be applied.” Lorica similarly noted that successful companies also use a combination of data scientists and data engineers even after an AI program or platform is implemented.

A combination of data engineers and data scientists are what I want to have access to.” Lorica continued, “You can feed data into a library and get a result but You still need experience and training to interpret those results.” “If you don’t have historical data, you must come up with a process to start labeling and storing data that may be able to help you scale,” Lorica says.

[Some must take into account] all the data prep and cleaning you have to do along the way, or data augmentation if you have to add more data.“ Based on the research of O’Reilly, Lorica, and TechEmergence, we’ve provided a few key questions that a strong case study will answer: Along with the above questions, it is also important to focus on case studies which include the following details “There also must be an understanding that there’s a lot of considerations like privacy, security, and end-to-end requirements much more than modeling skills,” Lorica concluded.

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