Tuesday, March 26, 2024

g-f(2)2140 Unveiling the Data Dilemma: Is Your Organization Ready for Generative AI?

 


genioux Fact post by Fernando Machuca and ChatGPT



Introduction:


As generative AI gains traction across industries, there's a palpable excitement among executives about its transformative potential. However, a recent survey of chief data officers (CDOs) and data leaders reveals that while enthusiasm runs high, companies are facing significant challenges in preparing their data for the effective use of generative AI.



genioux GK Nugget:


"Despite the widespread excitement surrounding generative AI, many organizations are struggling to derive substantial economic value from its implementation due to unpreparedness of their data infrastructure." — Fernando Machuca and ChatGPT, March 26, 2024



genioux Foundational Fact:


A survey conducted in the latter half of 2023, encompassing 334 CDOs and data leaders, highlighted that although 80% agreed on generative AI's potential to transform their businesses, only 6% had a generative AI application in production deployment.



The 10 most relevant genioux Facts:




  1. While boards and senior management teams are enthusiastic about generative AI, the critical preparatory work rests on CDOs, data engineers, and knowledge curators.
  2. Companies are mobilizing to leverage generative AI, with educational workshops and explorations of potential use cases.
  3. Despite the excitement, most organizations have not yet seen significant economic value from their generative AI initiatives.
  4. The majority of companies are still in the experimental phase, with only a small fraction having deployed generative AI applications at scale.
  5. Customizing vendor models with proprietary data and preparing internal data are essential for deriving economic value from generative AI.
  6. Data quality emerges as a significant challenge, with 46% of respondents identifying it as the greatest obstacle to realizing generative AI's potential.
  7. Successful data preparation involves curation, cleaning, and integration of unstructured data, which requires considerable effort and investment.
  8. Prioritizing data domains and focusing on specific business areas can facilitate a smoother transition to generative AI implementation.
  9. While other data initiatives may seem more immediately tangible, delaying preparations for generative AI could hinder long-term transformation efforts.
  10. Despite competing priorities and organizational dynamics, the urgency to start preparing data for generative AI cannot be overstated.





Conclusion:


As companies navigate the complexities of integrating generative AI into their operations, the imperative lies in swiftly addressing data readiness challenges to unlock its transformative potential. Initiating data preparation efforts now can pave the way for sustained success in harnessing the power of generative AI.



REFERENCE

The g-f GK Article


Thomas H. Davenport and Priyanka TiwariIs Your Company’s Data Ready for Generative AI? Harvard Business ReviewMarch 26, 2024.


Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, a visiting scholar at the MIT Initiative on the Digital Economy, and a senior adviser to Deloitte’s AI practice. He is a coauthor of All-in on AI: How Smart Companies Win Big with Artificial Intelligence (Harvard Business Review Press, 2023).


Priyanka Tiwari is a product marketing leader at Amazon Web Services (AWS). She focuses on connected storytelling across AWS databases, analytics, and machine learning services and solutions.





Classical Summary:


The article discusses the readiness of companies for the adoption of generative AI, highlighting insights from a survey of 334 CDOs and data leaders. Despite widespread excitement about the potential of generative AI, organizations have not yet developed new data strategies or managed their data adequately to leverage this technology effectively. While many are experimenting with generative AI, only a small fraction have applications in production deployment. The article emphasizes the importance of preparing data for generative AI by focusing on data quality, integration, and curation. It also explores the challenges and opportunities faced by organizations in various business areas, such as customer operations, marketing, sales, and R&D. Ultimately, the article underscores the necessity for companies to start preparing their data now to unlock the transformative capabilities of generative AI in the future.



ABOUT  THE AUTHORS


Thomas H. Davenport


Thomas H. Davenport is an American academic and author specializing in analytics, business process innovation, knowledge management, and artificial intelligence. He currently holds the position of President’s Distinguished Professor in Information Technology and Management at Babson College, is a Fellow of the MIT Initiative on the Digital Economy, Co-founder of the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics. Davenport has authored or edited twenty books, including works on analytical competition, business process reengineering, and knowledge management. His influential book, Working Knowledge (coauthored with Larry Prusak), delves into knowledge management. Davenport has also contributed over one hundred articles to publications such as Harvard Business Review, MIT Sloan Management Review, and Financial Times. He has been recognized as one of the world's top consultants and analysts in business and technology. His work spans various domains, and he continues to shape the field of analytics and management¹².


Source: Conversation with Bing, 3/28/2024

(1) Thomas H. Davenport - Wikipedia. https://en.wikipedia.org/wiki/Thomas_H._Davenport.

(2) Analytics 3.0 - Harvard Business Review. https://hbr.org/2013/12/analytics-30.

(3) Beyond Automation - Harvard Business Review. https://bing.com/search?q=Thomas+H.+Davenport+summary.

(4) Beyond Automation - Harvard Business Review. https://hbr.org/2015/06/beyond-automation.

(5) Keep Up with Your Quants - Harvard Business Review. https://hbr.org/2013/07/keep-up-with-your-quants.



Priyanka Tiwari


Priyanka Tiwari is the Product Marketing Lead - AWS Data and ML at Amazon Web Services. She specializes in product and content marketing, bringing enterprise software products to market. Prior to joining Amazon Web Services, Priyanka led product marketing for IT operations tools at SmartBear Software¹.


Source: Conversation with Bing, 3/28/2024

(1) Priyanka Tiwari - Product Marketing Lead - AWS Data and ML @ Amazon Web .... https://www.crunchbase.com/person/priyanka-tiwari.

(2) Is Your Company’s Data Ready for Generative AI? - Harvard Business Review. https://hbr.org/2024/03/is-your-companys-data-ready-for-generative-ai.

(3) 8 Strategies for Chief Data Officers to Create — and Demonstrate — Value. https://hbr.org/2023/01/8-strategies-for-chief-data-officers-to-create-and-demonstrate-value.



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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References


genioux facts”: The online program on "MASTERING THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)2140, Fernando Machuca and Bard (Gemini)March 26, 2024, Genioux.com Corporation.
 
The genioux facts program has established a robust foundation of over 2139 Big Picture of the Digital Age posts [g-f(2)1 - g-f(2)2139].



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