Friday, March 8, 2024

g-f(2)2055 Beyond the Golden Egg: Why Machine Learning Belongs in the Business Nest

 


genioux Fact post by Fernando Machuca and Claude


Introduction


In this webinar, "How to Succeed With Predictive AI", machine learning expert Eric Siegel explains why many enterprise machine learning projects fail at deployment and outlines a collaborative end-to-end paradigm to bridge the gap between business and technical stakeholders.



genioux GK Nugget


"Machine learning's untapped potential lies not in its technical sophistication, but in organizations treating it as a business endeavor first by deeply involving stakeholders in defining objectives, metrics, and deployment plans from inception." — Fernando Machuca and Claude



genioux Foundational Fact


While data scientists focus on the "golden egg" of model creation, the greater challenge is operationalizing predictions to drive decisions, which requires business teams to gain a semi-technical understanding of key project parameters.



Top 10 genioux Facts



  1. Most enterprise machine learning projects fail to reach deployment and generate value.
  2. Deployment stalls because business stakeholders get cold feet when not involved in project details.
  3. The crucial factors are what's predicted, how well it performs on business metrics, and how predictions inform decisions.
  4. Chasing accuracy metrics alone is misguided; translating to KPIs like profit and ROI is key.
  5. Probabilities are systematically acted on over many cases, but headlines often misinterpret them.
  6. Each predictive use case is defined by a granular prediction goal and prescribed action based on scores.
  7. Machine learning is celebrated more for its technical achievements than actual business launches.
  8. An end-to-end paradigm with deep collaboration is needed, not just isolated technical steps.
  9. Business leaders must ramp up on semi-technical project aspects, akin to driving versus auto mechanics.
  10. Machine learning should be reframed as an operations improvement initiative, not a technical project.




Conclusion


To overcome the gap between machine learning's potential and its deployment realities, organizations must embrace it as a collaborative business endeavor, empowering stakeholders to shape key project parameters and success measures through a shared semi-technical understanding.



REFERENCE

The g-f GK Webinar 


How to Succeed With Predictive AIMIT Sloan Management Review, YouTube channel, March 7, 2024.


Too many machine learning projects fail at deployment. The primary reason? They’re viewed as technology rather than business projects. And organizations often fail to foster a connection between business and technology functions. In this webinar, predictive analytics expert Eric Siegel, author of "The AI Playbook: Mastering the Rare Art of Machine Learning Deployment," explains what business stakeholders must do to succeed with AI.



Eric Siegel


Eric Siegel, the author of "The AI Playbook: Mastering the Rare Art of Machine Learning Deployment," presents a gold-standard, six-step practice for ushering machine learning projects from conception to deployment¹. He illustrates the practice with stories of success and failure, including revealing case studies from UPS, FICO, and prominent dot-coms¹. The book emphasizes that machine learning is the world's most important general-purpose technology, but it's notoriously difficult to launch¹. Siegel's book serves both business and data professionals by providing a strategic framework and upskilling business professionals with semi-technical background knowledge¹. This approach enables business and data professionals to collaborate deeply, jointly establishing what machine learning is called upon to predict, how well it predicts, and how its predictions are acted upon to improve operations¹.



Source: Conversation with Bing, 3/8/2024

(1) The AI Playbook: Mastering the Rare Art of Machine Learning Deployment .... https://www.amazon.com/AI-Playbook-Mastering-Deployment-Management/dp/0262048906.

(2) The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. https://direct.mit.edu/books/monograph/5729/The-AI-PlaybookMastering-the-Rare-Art-of-Machine.

(3) The AI Playbook: Mastering the Rare Art of Machine Learning Deployment .... https://www.goodreads.com/book/show/150065140-the-ai-playbook.

(4) The AI Playbook by Eric Siegel - Penguin Books New Zealand. https://www.penguin.co.nz/books/the-ai-playbook-9780262048903.

(5) undefined. https://doi.org/10.7551/mitpress/15059.001.0001.





Claude's Summary:


In this MIT Sloan Management Review webinar, machine learning expert Eric Siegel addresses the pervasive problem of enterprise machine learning projects failing to reach deployment and generate value. He argues that this disconnect stems from treating machine learning as a purely technical endeavor, rather than a collaborative business initiative that requires deep stakeholder involvement from inception to integration.


Siegel emphasizes that while data scientists focus on creating the "golden egg" of predictive models, the real challenge lies in operationalizing those predictions to drive business decisions. This requires business teams to gain a semi-technical understanding of key project parameters, including the specific prediction goal, performance on business-relevant metrics, and how scores will inform actions.


He cautions against chasing accuracy metrics alone, stressing the importance of translating model performance to KPIs like profit and ROI. Siegel also delves into the nuances of probabilities, noting that while they are systematically acted on over many cases, headlines often misinterpret their meaning.


To bridge the deployment gap, Siegel advocates for an end-to-end paradigm that fosters deep collaboration between technical and business stakeholders. He likens the necessary semi-technical understanding for business leaders to the knowledge required for driving a car, as opposed to auto mechanics.


Siegel ultimately argues for reframing machine learning projects as operations improvement initiatives, rather than isolated technical endeavors. By empowering business stakeholders to shape key project parameters and success measures, organizations can unlock the true potential of predictive AI.


The webinar concludes with a call to action for organizations to embrace machine learning as a collaborative business endeavor, fostering a shared semi-technical understanding to drive deployment and value creation. Only by bridging the gap between technical prowess and business realities can enterprises truly succeed with predictive AI.



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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)2055, Fernando Machuca and ClaudeMarch 8, 2024, Genioux.com Corporation.
 
The genioux facts program has established a robust foundation of over 2054 Big Picture of the Digital Age posts [g-f(2)1 - g-f(2)2054].



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