g-f(2)2595 Scaling Gen AI: Overcoming the Challenges

 

genioux Fact post by Fernando Machuca and Copilot

Introduction: 

Welcome to our latest genioux Fact post, where we delve into the McKinsey article “A data leader’s technical guide to scaling gen AI”. This article provides valuable insights into the challenges and solutions for scaling generative AI (gen AI) in organizations, a crucial aspect of digital transformation in the g-f New World.

genioux GK Nugget: 

"Effective scaling of gen AI requires improved data quality, better data products, and key data-management considerations." — Fernando Machuca and Copilot, July 8,2024.

genioux Foundational Fact: 

Scaling gen AI in organizations is a complex task. The article highlights that 70% of top performers face difficulties integrating data into AI models due to issues with data quality, defining processes for data governance, and having sufficient training data. To overcome these challenges, organizations need to improve their source data, use gen AI to build better data products, and consider key data-management practices that enable reuse and accelerate the development of data solutions.

The 10 Most Relevant genioux Facts:

1. Scaling gen AI is a challenge faced by 70% of top performers.
2. Issues with data quality, data governance processes, and training data are common obstacles.
3. Poor data can lead to poor outcomes, costly fixes, cyber breaches, and loss of user trust.
4. Improving and expanding source data is a key action for scaling gen AI.
5. Gen AI can be used to build better data products on modernized data platforms.
6. Key data-management considerations enable reuse and accelerate data solution development.
7. Managing data remains a main barrier to value creation from gen AI.
8. Evolving data capabilities are important to support gen AI cases at scale.
9. Gen AI can be used to improve data practices.
10. The article provides a technical guide for data leaders to scale gen AI.

Conclusion: 

In conclusion, the McKinsey article “A data leader’s technical guide to scaling gen AI” offers valuable insights into the challenges and solutions for scaling gen AI in organizations. Understanding these insights is crucial for any organization aiming to effectively leverage gen AI in the g-f New World. By improving data quality, building better data products, and considering key data-management practices, organizations can overcome the challenges and successfully scale gen AI. Stay tuned for more genioux Facts as we continue to explore the g-f New World together!

REFERENCES

The g-f GK Context

Asin Tavakoli, Carlo Giovine, Joe Caserta, Jorge Machado, Kayvaun Rowshankish, Jon Boorstein, and Nathan Westby, A data leader’s technical guide to scaling gen AI, McKinsey, McKinsey Digital, July 8, 2024.

ABOUT THE AUTHORS

This article is a collaborative effort by Asin Tavakoli, Carlo Giovine, Joe Caserta, Jorge Machado, and Kayvaun Rowshankish, with Jon Boorstein and Nathan Westby, representing views from McKinsey Digital and QuantumBlack, AI by McKinsey.

Classical Summary of the Article:

The McKinsey article "A data leader’s technical guide to scaling gen AI" discusses the challenges and solutions for scaling generative AI (gen AI) in organizations¹.

According to the article, 70% of top performers have experienced difficulties integrating data into AI models due to issues with data quality, defining processes for data governance, and having sufficient training data¹. The article suggests three actions to help move from gen AI pilots to scaling data solutions¹:

1. Improve Your Data: The risks and costs of feeding poor data into gen AI models are high, ranging from poor outcomes, costly fixes, and cyber breaches to a loss of user trust in the outputs¹. Organizations should improve and expand their source data¹.
2. Use Gen AI to Build Better Data Products: Organizations can use gen AI to build better data products with their modernized data platforms¹.
3. Key Data-Management Considerations: The article explores key data-management considerations that enable reuse and accelerate the development of data solutions¹.

The article emphasizes that managing data remains one of the main barriers to value creation from gen AI¹. It also highlights the importance of evolving data capabilities to support gen AI cases at scale and using gen AI to improve data practices¹.

Source: Conversation with Copilot, 7/9/2024

(1) A data leader’s technical guide to scaling gen AI | McKinsey. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/a-data-leaders-technical-guide-to-scaling-gen-ai.

(2) How AI Can Change the Way Your Company Gets Work Done. https://hbr.org/2024/07/how-ai-can-change-the-way-your-company-gets-work-done.

The categorization and citation of the genioux Fact post

Categorization

This genioux Fact post is classified as Bombshell Knowledge which means: The game-changer that reshapes your perspective, leaving you exclaiming, "Wow, I had no idea!"

Type: Bombshell Knowledge, Free Speech

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“genioux facts”: The online program on "MASTERING THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)2595, Fernando Machuca and ChatGPT, July 8, 2024, Genioux.com Corporation.

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