LLMs, Gen AI and Stakeholder Buy-in

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=sE8aAAIlhiw


Duration: 28:06
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Matt Fornito emphasizes the importance of effectively communicating the value of data science to executives and stakeholders. He introduces the concept of a maturity framework and identifies key personas within an organization. The speaker discusses challenges and strategies for adoption, collaboration with different roles, and implementation. Matt also emphasizes the importance of creating a data-driven transformation culture within organizations.

Topics:
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Importance of effectively communicating the value of data science
* Address concerns and educate executives and stakeholders about the benefits and opportunities of data science
* Use a maturity framework to assess an organization's level of data-driven decision-making
* Build relationships with key personas within an organization
Challenges and strategies for adoption
* Executives may have reservations about privacy and security implications of using large language models (LLMs) and proprietary data
* Provide education and training opportunities to foster trust and understanding
* Collaborate with CDOs and CTOs to overcome hardware constraints and cost considerations, and understand what's needed to transform data pipelines
* Data engineers play a crucial role in ensuring reliability, explainability, privacy, and security of Gen AI models
Implementation
* Identify business cases where Gen AI can have a significant impact
* Collaborate with various departments to prioritize use cases and develop a roadmap
* Generate small wins to build trust and stakeholder buy-in
* Productionize models, assess their value, and scale solutions successfully
Creating a data-driven transformation culture
* Provide training and workshops to educate employees about generative AI
* Align AI initiatives with business and organizational goals
* Assess talent acquisition and consider bringing in new talent or external consultants
* Encourage a culture of experimentation and adaptability
* Demonstrate the financial impact of AI initiatives through ROI discussions



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Tags:
deep learning
machine learning