Why should you consider a phased delivery plan for your generative Al solution?

Homework Help: Questions and Answers: Why should you consider a phased delivery plan for your generative Al solution?

a) To enable you to gather feedback and identify issues before releasing the solution more broadly
b) To eliminate the need to identify, measure, and mitigate potential harms
c) To enable you to charge more for the solution

Answer:

Let’s analysis each given options step by step to determine the correct answer (option).

Given Options: Step by Step Answering

a) To enable you to gather feedback and identify issues before releasing the solution more broadly

• A phased delivery plan allows you to release the AI solution to a smaller group first. This helps you gather valuable feedback from early users and identify any potential issues or bugs. You can address these before rolling out the solution on a larger scale. This is a common approach in software development to ensure higher quality and user satisfaction.

b) To eliminate the need to identify, measure, and mitigate potential harms

• This option suggests that a phased delivery plan could remove the responsibility to identify or mitigate harms. However, this is incorrect. Even with a phased approach, identifying, measuring, and mitigating potential harms is essential. Ignoring this could lead to significant risks when the solution is eventually rolled out more broadly.

c) To enable you to charge more for the solution

• This is also incorrect. While phased delivery might offer some business advantages, such as creating anticipation or adding features over time, the primary purpose of a phased approach is not to increase the price. Charging more is not typically a direct reason to use a phased delivery plan.

Final Answer

Based on the above analysis, the correct answer is:

a) To enable you to gather feedback and identify issues before releasing the solution more broadly

This approach is a best practice to improve the quality and reliability of the final AI solution by learning from real-world use cases and addressing any issues before a full-scale launch.

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