What should YouTube do with GenAI?

Clarifying questions
1.I'll assume this is focused on user functionality (as opposed to back office)


GenAI type approaches can be classified into 4 main categories:
1. foundation modelling using transformer architectures on structured and or unstructured data. Main use case: modelling predictive outcomes over vast data sets that have greater predictive power and gaining speed to market by by establishing a 1 to many modelling paradigm (ie. foundation model serves 100s of head models)
2. Knowledge management systems that support chatbots utilizing chatGPT type models to produce summary information in response to a query. Main use case:virtual agents either colleague or customer facing that can answer questions about products etc
3. Knowledge management systems that synthesize large volumes of data to support content generation sample use cases: filling gaps in product/content descriptions, investigating and research, personalization
4. Agentic systems that utilize the same GPT type LLM systems to produce a virtual agent type solution but also with the functionality of being able to effect an action sample use case: customer wants to update account information or update preferences in an app

To determine the optimal opportunity to leverage a GenAI solution at YouTube I'd take a effort vs impact approach and identify a list of potential POC use cases based on desired outcomes.

Potential outcomes:
User impact
1. improve ease of use
2. resolve customer issues
3. personalize content and search
4. streamline account set up and management

Business impact
1. increase new customers
2. Identify bot comments
3. Identify content that violates community rules
4. optimize ad placement

I'm sure there are many more but let's start here.

The potential use cases:
1. create virtual agent that can address user queries
2. create chatbot that can action simple customer issues
3. optimize search outcomes base on foundation model approaches
4. Make recommendation for content that is more relevant
5. create an agent that supports users to fill out profile on set up and optimize profile periodcially
6. identify target demographics for video and optimize ad content
7. investigate comments for bot activity and action removal
8. screen new content upload metadata for violation and flagging
9. create an automated admin agent for non-complex community surveying

Next identify for each use case the requirements for:
1. data
2. governance
3. infrastructure
4. compute (costs and size)
5. implementation
6. monitoring

Identify for each use case potential poc/MVP prioritization variables:
1. size of population in scope
2. size of total population opprtunity
3. impact on users
4. impact on interactions
5. automation impact

Select according to a global strategy - ie.:
1. goal to power 80% of searches with genai
2. goal to power 50% of recommendations with genai
3. goal to drive X% of in app interactions with genai
4. target Y% reduction in content gaps
5. Target Z% reduction in bot activity

The global strategy should set the guardrails for what outcomes to pursue and should be targeted enough to reduce proliferation of random MVPs across a wide landscape. It is desirable with new technology to compound learning which can only be done if targeting very deliberate directions.