#Shorts How does a company make a profit from an AI project?

#Shorts How does a company make a profit from an AI project?

When it comes to ensuring profitability from an AI project, the first step is to maintain a diversified analytics portfolio. It's crucial not to make this an extremely top-down process, where the data science team isn't allowed to scrutinize and question the project details. If the team leader dismisses a single project due to its unviability, they would have no current work.

To avoid this situation, a portfolio should include multiple projects, ideally around three to six or even a dozen, which are prioritized by management. This approach allows the data science teams to critically evaluate each project, estimate the ROI, and decide which ones are worth pursuing.

The data science team could then suggest: "From the half dozen projects provided, we find that four are a good fit for our capabilities. Out of these six, we recommend starting with these two, as they have the highest potential for good ROI."

Keywords

• AI project
• profitability
• diversified analytics portfolio
• data science team
• top-down process
• scrutinize
• project viability
• ROI
• management priorities

FAQ

Q: Why should there be more than one AI project in the analytics portfolio?

A: Having multiple projects allows the data science team to critically evaluate each one, estimate potential ROI, and decide which ones are worth pursuing. This diversification helps ensure that resources are allocated to the most viable projects.

Q: What happens if a data science team is handed a single, top-down project?

A: If the single project is not viable, the data science team would be left without current work. This is why multiple projects should be maintained.

Q: How many projects should be included in an analytics portfolio?

A: Ideally, the portfolio should contain about three to six, or even up to a dozen, projects that are significant to management.

Q: How does the data science team contribute to project selection?

A: The team evaluates each project for ROI, scrutinizes the details, and recommends which projects to pursue based on their expertise and the project's potential profitability.

Q: What should management do if some projects are not immediately viable?

A: Management should be flexible and open to the data science team's recommendations. Out of several projects, teams can identify which ones have the highest potential and should be prioritized.