Monday, March 4, 2024

g-f(2)2042 Unveiling the Hidden Power of Predictive AI: Why It Outperforms Generative AI


genioux Fact post by Fernando Machuca and ChatGPT


In the realm of artificial intelligence (AI), companies face the critical decision of whether to prioritize generative AI, which creates content like writing and images, or predictive AI, which targets tasks such as ad targeting and risk management. The article "3 Ways Predictive AI Delivers More Value Than Generative AI" explores why predictive AI often delivers more value than generative AI, despite the latter's allure.

genioux GK Nugget:

"Predictive AI offers substantial bottom-line improvements to enterprise efficiencies, making it a compelling choice for companies seeking impactful AI solutions for their operational challenges." — Fernando Machuca and ChatGPT

genioux Foundational Fact:

Predictive AI and generative AI serve distinct purposes, with predictive AI streamlining existing large-scale operations, while generative AI aims to perform tasks traditionally handled by humans.

10 genioux Facts:

  1. Predictive AI frequently delivers higher returns than generative AI by optimizing systematic enterprise processes and improving customer experiences.
  2. Predictive AI can operate autonomously, driving decisions without human intervention, unlike generative AI, which often requires human review and supervision.
  3. Predictive AI is generally more cost-effective and imposes a smaller footprint than generative AI, with lighter-weight machine learning models.
  4. The predictive AI market is projected to reach $64 billion by 2025, far surpassing the spend on generative AI.
  5. Applications of predictive AI span various industries, including marketing, finance, healthcare, and supply chain management.
  6. Companies can achieve significant cost savings and revenue growth through predictive AI implementations, such as fraud detection and targeted marketing campaigns.
  7. Generative AI excels in generating initial drafts of content but typically requires human oversight for quality assurance.
  8. The adoption of both predictive and generative AI should be considered complementary rather than mutually exclusive, with each serving specific operational needs.
  9. Organizations should focus on deploying the most suitable AI technology for each operational problem to maximize value generation.
  10. Bridging the technology/business gap is essential for successful AI deployment, emphasizing the interdisciplinary nature of AI implementation.


While generative AI may capture attention with its novelty, predictive AI often substantially benefits enterprises by optimizing existing processes and driving operational efficiencies. Companies should leverage the strengths of both predictive and generative AI to address diverse operational challenges effectively.


The g-f GK Article

Eric Siegel3 Ways Predictive AI Delivers More Value Than Generative AIForbes, March 4, 2024.

Eric Siegel

Eric Siegel, Ph.D., is a renowned AI consultant, bestselling author, and former professor at Columbia University²³⁴⁵. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI World³. He is also the author of "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die, Revised and Updated Edition" and the founder of the Predictive Analytics World conference series⁴.

Siegel has made significant contributions to the field of machine learning and has helped numerous companies deploy machine learning solutions²³⁵. His work focuses on the practical application of machine learning and predictive analytics, and he has written extensively on these topics²³⁴⁵.

In addition to his consulting work, Siegel has also been a spokesperson for publishing brands and has coached executives, founders, solopreneurs, and small business owners in the art of interviewing and media strategy². His expertise and insights have made him a sought-after speaker and consultant in the field of AI and machine learning²³⁴⁵.

Source: Conversation with Bing, 3/5/2024

(1) Eric Siegel: books, biography, latest update.

(2) Eric Siegel - Forbes.

(3) Stories by Eric Siegel | Scientific American.

(4) Eric Siegel's Home Page: Machine Learning Events, Training, Book.

(5) Erich Segal - Wikipedia.

ChatGPT's Summary:

The Forbes article "3 Ways Predictive AI Delivers More Value Than Generative AI" explores the comparative advantages of predictive AI over generative AI in delivering tangible value to enterprises. Author Eric Siegel, an AI consultant and former Columbia University professor, emphasizes that while generative AI may appear more innovative and captivating, predictive AI often yields greater bottom-line improvements due to its focus on streamlining established operational processes. Siegel presents three key reasons why predictive AI outperforms generative AI:

  1. Higher Returns: Predictive AI enhances large-scale, systematic processes within enterprises, offering substantial returns and improving customer experiences. By optimizing operations such as package delivery planning, fraud detection, and marketing campaigns, predictive AI can deliver significant cost savings and revenue growth.
  2. Autonomy: Unlike generative AI, which often requires human intervention to review outputs, predictive AI can operate autonomously for many deployments. This autonomy allows predictive AI to drive millions of operational decisions daily without human involvement, leading to increased efficiency and scalability.
  3. Cost-Effectiveness: Predictive AI typically requires lighter-weight machine learning models compared to the resource-intensive models used in generative AI. This difference in complexity translates to lower costs and energy consumption for predictive AI projects, making them more accessible and sustainable for enterprises.

Siegel concludes by advocating for a balanced approach that leverages both predictive and generative AI based on the specific needs and objectives of each operational problem. While acknowledging the value of generative AI in tasks like content generation and code improvement, he emphasizes that predictive AI remains the cornerstone of enterprise machine learning, offering substantial untapped potential for organizations seeking operational optimization and growth.

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