Generative AI adoption framework | Product Services and Data Product

Generative AI - Adoption-framework describe how dependancy on universal data and tolerance for risk will drive adoption. On ther hand where custom dataset are needed and tolerance for error is low - adoption will be hard. The framework describe opportunities for product, services and data-product companies.
GENAI ADOPTION FRAMEWORK
GENAI ADOPTION FRAMEWORK
        
FOR PRODUCT COMPANIES
FOR PRODUCT COMPANIES
        
FOR ENTERPRISES
FOR ENTERPRISES
        


Generative AI Adoption Framework

GenAI Security Framework
  • Low risk and Universal data
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  • High risk and Domain specific data
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    For Medium to Long Term

    Leaders should think thru and answer following questions:


  • Whether inference should be on cloud or on-prem for generative AI. Generative AI compute is specialized and expenive. Depending on the need, speclization, cose - one need to determine where inference should be run
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  • What training platform to use? If you are training model on your own on custom data determine which ML training platform to use.
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    Why consider Adoption Framework for GenAI in Enterprises
  • Adoption framwork help companies determine areas where generative AI adoption can happen quickly. Areas that are less mission crticial and where out of box generative AI works well with low error rate are good candidate to onboard generative AI.
    •
  • Framework need to be structured. It should provide stages, best practices and how to evaluate risks and opportunties.
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  • Slides describe 5 stages of gnerative AI adoption phases.

    		•
  • It describe areas where out of box model can be used without issues. It describe areas where out of box model will not work and there is opportunity to innovate
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  • Slide also describe how to extend AI risk and consider all ris for generative AI
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  • Finally slide describe use caes that can be adopted with ease
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  • Mission critical areas where out of box model does not work - should be adopted at last stage. Companies should evaluate whether training on domain specific data will help. If building such model produce good result, it will provide competitive advantage
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    Additional cpoints for adoption framework
    Factors to Consider for Generative AI Adoption
    • Data Availability: Assess the availability and quality of data required for training the generative AI model.
    • Computational Resources: Evaluate the computational resources needed to train and deploy the generative AI model.
    • Expertise and Skills: Determine the level of expertise and skills required to develop and maintain the generative AI model.
    • Ethical Considerations: Consider the ethical implications and potential biases associated with the generative AI model's outputs.
    • Legal and Regulatory Compliance: Ensure compliance with relevant laws and regulations when using generative AI.
    • Business Objectives: Align the adoption of generative AI with the organization's strategic goals and objectives.
    • Costs and Return on Investment: Evaluate the costs associated with implementing and maintaining generative AI, and assess the potential return on investment.
    • Security and Privacy: Address security and privacy concerns related to the data used and generated by the generative AI model.
    • User Acceptance: Consider the acceptance and usability of generative AI outputs by end-users or customers.

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    Adoption-framework    Ai-assistant-lifecycle    Ai-finance    Algorithms    Challenges-on-prem-generative    Considerations    Detail-notes    Digital-human    Elephant-and-monkey-generated    Elephant-monkey-story   

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