g-f(2)1940 Unlocking AI's True Value: Shifting Focus from Technical Metrics to Business Outcomes

 

genioux Fact post by Fernando Machuca and ChatGPT

Introduction:

In the realm of artificial intelligence (AI), particularly in the domain of machine learning (ML), the focus has long been on technical metrics rather than business value. However, this approach often leads to project failures, highlighting the crucial need for prioritizing business metrics in evaluating AI initiatives.

genioux GK Nugget:

"Most AI/machine learning projects prioritize technical metrics over business metrics, leading to a significant gap between perceived and actual value delivery." — Fernando Machuca and ChatGPT

genioux Foundational Fact:

To prevent project failures and maximize the value of AI initiatives, leaders must emphasize the measurement of business metrics over technical ones, ensuring alignment with organizational objectives and desired outcomes.

10 Relevant genioux Facts:

1. AI encompasses various technologies, but for organizations, machine learning (ML) is often the core technology driving AI initiatives.
2. ML has the potential to enhance diverse business processes, including predictive analytics, fraud detection, and risk management.
3. Despite the potential for business improvement, many ML projects primarily report on technical metrics such as precision and recall.
4. Technical metrics provide insights into model performance but fail to quantify the direct business value generated by ML initiatives.
5. Business metrics, such as revenue, profit, and cost savings, offer a more comprehensive understanding of ML's impact on organizational objectives.
6. Data scientists often overlook business metrics in favor of technical ones due to training and tool limitations.
7. The absence of business metrics reporting undermines stakeholders' ability to assess the true value and potential impact of ML projects.
8. ML projects that prioritize technical metrics over business metrics often fail to deliver expected returns on investment.
9. Incorporating business metrics, such as misclassification costs, enables a more accurate assessment of ML model performance and value.
10. By emphasizing business metrics in ML project evaluation, organizations can make better-informed decisions and maximize the return on AI investments.

Conclusion:

In the journey towards AI project success, leaders must shift their focus from technical to business metrics, ensuring that ML initiatives align with strategic objectives and deliver tangible value to the organization. This shift requires collaboration between data scientists and business leaders to bridge the gap between technical execution and business outcomes, ultimately driving better decision-making and realizing the full potential of AI technologies.

REFERENCE

The GK Article

Eric Siegel, What Leaders Should Know About Measuring AI Project Value, MIT Sloan Management Review, February 7, 2024.

Eric Siegel

Eric Siegel, Ph.D., is a leading consultant and former professor at Columbia University and the University of Virginia Darden School of Business⁷⁸. He is renowned for his expertise in machine learning and predictive analytics¹²³⁴.

At Columbia University, he taught graduate-level courses in machine learning and artificial intelligence, and won the engineering school's award for teaching⁴. He was also affiliated with the natural language processing group¹.

At the University of Virginia Darden School of Business, he served as the inaugural Bodily Bicentennial Professor in Analytics for the 2022-2023 academic year⁷⁸⁹. This visiting position allowed him to collaborate and share insights with the Darden and broader University of Virginia community⁸.

Siegel is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI World¹. He is also the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery”¹.

He has authored the bestselling book "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die", which has been used in courses at hundreds of universities, as well as "The AI Playbook: Mastering the Rare Art of Machine Learning Deployment"¹²³⁴.

Before his academic career, Siegel was the founder and principal of a software company with a globally diverse client list²³..

Source: Conversation with Bing, 2/7/2024

(1) Data Analytics | UVA Darden School of Business. https://www.darden.virginia.edu/faculty-research/data-analytics.

(2) ERIC SIEGEL, PH.D. - University of Virginia Darden School of Business. https://wwwprod3.darden.virginia.edu/sites/default/files/2022-07/Eric%20Siegel%20CV_2022.pdf.

(3) Eric Siegel, Ph.D. - Columbia University. http://www1.cs.columbia.edu/~evs/.

(4) Eric Siegel, machine learning keynote speaker and author. https://www.machinelearningkeynote.com/.

(5) Eric Siegel, machine learning keynote speaker and author. https://www.machinelearningkeynote.com/speaking-keynotes.

(6) Eric Siegal Speaker - Simply Life India Speakers Bureau. https://www.simplylifeindia.com/eric-siegal.html.

(7) UVA Darden Welcomes 12 New Professors Ahead of ... - Darden Report Online. https://news.darden.virginia.edu/2022/08/23/12-new-professors-2022-23-academic-year/.

(8) . https://bing.com/search?q=Eric+Siegel+University+of+Virginia+Darden+School+of+Business.

(9) Darden School of Business. https://www.darden.virginia.edu/.

(10) undefined. https://bing.com/search?q=.

(11) Getty Images. https://www.gettyimages.com/detail/photo/darden-school-of-business-university-of-virginia-royalty-free-image/1003225412.

Copilot's Summary

The article "What Leaders Should Know About Measuring AI Project Value" by Eric Siegel, published in the MIT Sloan Management Review, discusses the importance of focusing on business metrics rather than technical metrics when evaluating the value of AI projects¹.

The author argues that most AI/machine learning projects report only on technical metrics, such as precision, recall, and lift, which do not provide a direct reading on the absolute business value of a model¹. These metrics only tell us the relative performance of a predictive model in comparison to a baseline such as random guessing¹.

Siegel emphasizes that the focus should be on business metrics, such as revenue, profit, savings, and number of customers acquired¹. These metrics gauge the fundamental notions of success, relate directly to business objectives, and reveal the true value of the imperfect predictions machine learning delivers¹.

Unfortunately, data scientists often omit business metrics from reports and discussions, despite their importance¹. The author suggests that this practice needs to change to prevent project failures and to build a much-needed bridge between business and data science teams¹.

Source: Conversation with Bing, 2/7/2024

(1) What Leaders Should Know About Measuring AI Project Value. https://sloanreview.mit.edu/article/what-leaders-should-know-about-measuring-ai-project-value/.

(2) MIT Sloan Management Review & Report - Tribune Content Agency. https://tribunecontentagency.com/article/what-leaders-should-know-about-measuring-ai-project-value/.

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References

“genioux facts”: The online program on "MASTERING THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)1940, Fernando Machuca and ChatGPT, February 7, 2024, Genioux.com Corporation.

 

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