Summary
This video emphasizes the critical importance of asking key questions before embarking on AI projects for agencies. Understanding the type and source of data to be used, user interactions with the system, and defining desired results are fundamental for project success. The exploration phase in the client lifecycle involves milestones like data sampling, input-output testing, and prototype development to ensure feasibility and client satisfaction. Techniques such as cosine similarity testing and graphical mockups aid in setting and managing client expectations throughout the AI project lifecycle.
Introduction
Introduction to the importance of asking key questions before taking on AI projects for an agency, based on the speaker's personal experience and lessons learned.
Key Questions: Data
The first key question to ask is about the data that will be used or manipulated in the AI project, understanding the type and source of data is crucial for project success.
Key Questions: Inputs
The second key question focuses on the expected inputs of the system, understanding how users interact with the system and what they expect as outputs is essential for designing the AI solution.
Key Questions: Outputs
The third key question is about the expected outputs of the system, defining the desired results helps in setting project goals and measuring success.
Exploration Phase
The process of exploration phase in the client life cycle, including milestones such as data sampling, input-output testing, and prototype development to ensure project feasibility and client satisfaction.
Expectation Management
Importance of setting and managing client expectations throughout the AI project lifecycle, using techniques like cosine similarity testing and graphical mockups to align on project outcomes.
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