Tuesday, March 19, 2024

g-f(2)2103 The 3Ps of AI Transformation: Problem-centric, People-first, Principle-driven


genioux Fact post by Fernando Machuca and ChatGPT


In the age of rapid technological advancement, many companies are embracing an "AI-first" strategy, prioritizing artificial intelligence as the ultimate strategic priority. However, this approach may be causing more problems than it solves. This article delves into the potential pitfalls of an AI-first strategy and proposes an alternative approach grounded in problem-solving, human empowerment, and ethical principles.

genioux GK Nugget:

"The problem with an AI-first strategy lies not within the “AI” but with the “first” aspect; it is about how organizational focus is directed." — Fernando Machuca and ChatGPT

genioux Foundational Fact:

An AI-first approach can be myopic, potentially leading us to overlook the true purpose of technology: to serve and enhance human endeavors. Instead, a balanced approach that prioritizes problem-solving, people, and principles can lead to more effective AI transformation initiatives.

10 genioux Facts:

  1. An AI-first strategy risks deploying AI solutions that do not address real organizational or customer problems, leading to solutions in search of problems or even creating new issues.
  2. AI solutions may not be universally beneficial and could hinder performance in certain tasks.
  3. Rushing to adopt AI applications without adequate IT infrastructure readiness can lead to inefficiencies and premature investments.
  4. Prioritizing AI over employees can exacerbate job insecurity fears and decrease employee buy-in, hindering successful transformation initiatives.
  5. Algorithmic management through AI can reduce employee motivation and erode organizational culture.
  6. Consumer reactance can be a significant hurdle when AI overtakes roles traditionally fulfilled by humans.
  7. Ethical dilemmas and legal ambiguities surrounding AI implementation require careful consideration to align projects with organizational values.
  8. A problem-centric approach prioritizes solving real organizational challenges over the deployment of flashy AI applications.
  9. A people-first approach focuses on empowering humans through AI, considering broader behavioral consequences and intrinsic motivation.
  10. A principle-driven approach ensures that AI deployment aligns with ethical principles such as fairness, bias mitigation, and privacy protection.


While the allure of an AI-first strategy is understandable, true success with AI transformation may lie in a more balanced and thoughtful approach. By prioritizing problem-solving, human empowerment, and ethical principles, organizations can harness the full potential of AI while avoiding potential pitfalls and achieving meaningful transformation.


The g-f GK Article

Oguz A. AcarIs Your AI-First Strategy Causing More Problems Than It’s Solving? Harvard Business Review, March 19, 2024.

Oguz A. Acar

Oguz A. Acar is a Chair in Marketing at King's Business School, King's College London¹. He is also a Research Affiliate at the Laboratory for Innovation Science at Harvard University, an Expert in Behavioural Sciences and Innovation at the World Economic Forum, and a Fellow of the Royal Society of Arts (RSA)¹. He was recently named one of the World’s Top 40 Business School Professors Under 40¹.

Acar's research focuses on behavioural innovation, drawing on behavioural science to understand the creation, evaluation, and adoption of innovative outputs, be it a novel idea, product, strategy, or technology¹². His research has been published in leading academic journals across different disciplines, including marketing, psychology, management, and innovation, as well as in executive-oriented outlets such as the Harvard Business Review¹².

He has received grants from several institutions, including the European Institute of Innovation & Technology and the British Academy¹. His work has been featured in popular business magazines and newspapers such as ABC, BBC, Financial Times, Forbes, Fortune, and Wired¹.

Acar has broad experience in developing and lecturing modules related to marketing and innovation to students and executives¹. He has also worked with various organizations in the forms of executive education, consultancy, and research collaboration¹. Prior to King's, he was an associate professor at Bayes Business School (formerly Cass), and a visiting scholar at Massachusetts Institute of Technology and Stern School of Business (New York University)¹. He completed his PhD from Rotterdam School of Management, Erasmus University¹². He also worked as a marketing professional in FMCG and financial services industries¹². He is an engineer by training¹².

Source: Conversation with Bing, 3/19/2024

(1) Oguz A Acar - King's College London. https://www.kcl.ac.uk/people/oguz-a-acar.

(2) Oguz A. Acar | Laboratory for Innovation Science at Harvard. https://lish.harvard.edu/people/oguz-acar.

(3) Stories by Oguz A. Acar | Scientific American. https://www.scientificamerican.com/author/oguz-a-acar/.

Classical Summary:

"Is Your AI-First Strategy Causing More Problems Than It’s Solving?" delves into the potential pitfalls of prioritizing artificial intelligence (AI) above all else in organizational strategy. Authored by Oguz A. Acar, the article critiques the prevalent "AI-first" approach, highlighting its myopic focus on technology over core organizational objectives, human empowerment, and ethical principles. By examining real-world examples and behavioral consequences, Acar argues for a more balanced and thoughtful approach to AI transformation, emphasizing problem-centric, people-first, and principle-driven strategies. The article concludes with a call to reevaluate priorities, suggesting that true success with AI lies in aligning technology with organizational goals, human values, and ethical considerations.

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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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