Generative AI Adoption Framework Slides

Generative AI adoption framework - 4 quardants - low risk item where quality of out of box LLm model is good, low risk item where quality need improvement, high risk item where better results are needed, high risk item where quality is very low. One should determine error rate and tolerance for error rate and accordingly implement generative AI.

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Additional Comments

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 shuold provide stages, best practices and how to evaluate risks and opportunties.

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

Slide also describe how to extend AI risk and consider all ris for generative AI

finally slide describe use caes that can be adopted with ease

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

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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From the blog

Build Dataproducts

How Dataknobs help in building data products

Enterprises are most successful when they treat data like a product. It enable to use data in multiple use cases. However data product should be designed differently compared to software product.

Be Data Centric and well governed

Generative AI is one of approach to build data product

Generative AI has enabled many transformative scenarios. We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products. We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner.

Be Data Centric and well governed

Generative AI is one of approach to build data product

Generative AI has enabled many transformative scenarios.
We combine generative AI, AI, automation, web scraping, ingesting dataset to build new data products.
We have expertise in generative AI, but for business benefit we define our goal to build data product in data centric manner.

Spotlight

Generative AI slides

Learn generative AI, LLM, foundation model

Learn generative AI - applications, LLM, architecture

See best practices for prompt engineering

Evaluate whether you should use out of box foundation model, fne tune or use in-context learning

Most important - be aware of concerns, issues, challenges, risk of genAI and LLM

See vendor comparison - Azure, OpenAI, GCP, Bard, Anthropic. Review framework for cost computation for LLM

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