ULTRA-condensed knowledge
🎯Exceptional Golden Knowledge (GK) Container (g-f EGK)
🏆Golden Knowledge Mine
MIT SMR Foundation Fact: While the past decade has been about experimenting with applying AI, the next decade will reward those organizations that really manage to scale it.
MIT SMR Facts:
- The agile teams needed to kick-start artificial intelligence must give way to companywide structures in order to scale the technology across a business.
- Many enterprises are on a journey to gain broad value from data analytics and AI capabilities, but they are still far from the destination, often adrift among islands of experimentation.
- AI initiatives often begin with small, specialized teams exploring specific problems, but these decentralized efforts make a limited impact.
- The strongest lever for scaling usage and adoption of artificial intelligence is to demonstrate that using AI tools has a positive financial impact on the business.
genioux Fact: MIT SMR is a gold mine of exceptional knowledge. Amit Joshi, Ivy Buche, and Miguel Paredes Sadler produced an excellent golden knowledge (GK) container about a fundamental topic.
"genioux fact" condensed as an image
Extra-condensed knowledge
Extra condensed knowledge complements ultra condensed knowledge.
MIT SMR Facts:
- Companies in a wide range of sectors are making significant investments in AI — and are increasingly concerned with how to scale use of the technology to gain benefits from it across their organizations. Too many companies stall out on their AI journey and have difficulty getting past pilot projects or point solutions.
- Our research finds that companies fail to extract the potential business value from AI not for lack of technical expertise but rather due to structural and process issues.
- We took an in-depth look at the AI scaling journey of 10 market-leading legacy companies with three to eight years of AI implementation experience across diverse industries, including consumer packaged goods, pharmaceuticals, banking, insurance, security services, and automotive.
Condensed knowledge
The condensed knowledge complements the extra and ultra condensed knowledge.
g-f Meme GK
MIT SMR Facts:
- We found that AI projects in enterprises generally begin as what we call islands of experimentation (IOE) before coming together around a corporate center of excellence (COE). Only a small number then move to a sophisticated federation of expertise (FOE) model built on a centralized base of knowledge, systems, processes, and tools, and on decentralized embedded capabilities.
- This implies that enterprises with AI ambitions may need to make two potential leaps. Below, we explain why each leap is necessary and discuss how companies can facilitate them.
- The Limits of Experimentation. AI initiatives often begin with small, specialized teams exploring specific problems, but these decentralized IOEs make a limited impact.
- Focus on Standardization to Deliver at Scale. IOEs are almost by definition opportunistic and agile. Their key goals are speed and “good enough” solutions to the pressing needs of a BU or function.
- Locate COEs Where They Best Serve Current Objectives. The structural placement of the COE within the organization depends on the maturity, decision-making processes, and corporate dynamics of the enterprise.
- Address the Incentives-KPIs Mismatch. Aligning incentives and sharing risk between the COE and the BU is fundamental to scaling AI. Since the COE does not operate in a vacuum, setting KPIs only for the COE gives it responsibility without power — and then it becomes even easier for skeptical BUs to claim that the COE is not generating value.
- When the COE Is Not Enough. While COEs have been successful mechanisms used by many companies to gain more value from their AI capabilities, they can be insufficient for large, highly diversified companies with significant resources at their disposal.
- Close the Feedback Loop. The strongest lever for scaling usage and adoption of artificial intelligence is to demonstrate that using AI tools has a positive financial impact on the business. It requires that the organization establish a feedback-loop flow from the BU to the MLOps team and, ultimately, to the data scientists. This helps ensure that the two most common problems in model building — applicability and model drift — are addressed.
g-f Meme GK
REFERENCES
Amit Joshi, Ivy Buche, and Miguel Paredes Sadler, Moving Beyond Islands of Experimentation to AI Everywhere, MIT Sloan Management Review, MIT SMR, May 4, 2023.
ABOUT THE AUTHORS
Amit Joshi
Amit Joshi is Professor of AI, Analytics and Marketing Strategy. He specializes in helping organizations use artificial intelligence and develop their big data, analytics, and AI capabilities. An award-winning professor and researcher, he has extensive experience of AI and analytics-driven transformations in industries such as banking, fintech, retail, automotive, telecoms, and pharma.
Joshi believes that no organization’s digital transformation is complete until they really understand their data and how to upscale their analytical capabilities. He is currently focusing on how organizations can ensure that AI implementation occurs strategically and at scale rather than in small islands of excellence.
He has delivered customized programs for several companies including UBS, Sonova, Johnson & Johnson, Guardian Life, Mars Petcare, Securitas, Bank Danamon, Bayer, Ooredoo, Siam Commercial Bank, Abu Dhabi School of Governance, Hanover RE, and Migros. He also advises start-ups on their strategies.
Ivy Buche
Ivy Buche is Associate Director of Business Transformation Initiative at IMD. She works with faculty on organization transformation projects for large companies.
Miguel Paredes Sadler
Miguel Paredes Sadler is vice president of AI and data science at Albertsons Companies.
Some relevant characteristics of this "genioux fact"
- BREAKING KNOWLEDGE
- Category 2: The Big Picture of the Digital Age
- [genioux fact deduced or extracted from MIT SMR]
- This is a “genioux fact fast solution.”
- Tag Opportunities to those travelling at high speed on GKPath
- Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).
- Type of validity of the "genioux fact".
- Inherited from sources + Supported by the knowledge of one or more experts.
- Authors of the genioux fact
References
“genioux facts”: The online programme on "MASTERING THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)1116, Fernando Machuca, May 5, 2023, Genioux.com Corporation.
“genioux facts”: The online programme on "MASTERING THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)1116, Fernando Machuca, May 5, 2023, Genioux.com Corporation.
ABOUT THE AUTHORS
PhD with awarded honors in computer science in France
Fernando is the director of "genioux facts". He is the entrepreneur, researcher and professor who has a disruptive proposal in The Digital Age to improve the world and reduce poverty + ignorance + violence. A critical piece of the solution puzzle is "genioux facts". The Innovation Value of "genioux facts" is exceptional for individuals, companies and any kind of organization.
PhD with awarded honors in computer science in France
Fernando is the director of "genioux facts". He is the entrepreneur, researcher and professor who has a disruptive proposal in The Digital Age to improve the world and reduce poverty + ignorance + violence. A critical piece of the solution puzzle is "genioux facts". The Innovation Value of "genioux facts" is exceptional for individuals, companies and any kind of organization.
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- g-f(2)1084 THE BIG PICTURE OF THE DIGITAL AGE (4/24/2023), HBR, Driving Organizational Change — Without Abandoning Tradition
- g-f(2)1083 THE BIG PICTURE OF THE DIGITAL AGE (4/24/2023), geniouxfacts, PDT: In the current state Bard and Bing Chatbot are GREAT!
- g-f(2)1082 THE BIG PICTURE OF THE DIGITAL AGE (4/23/2023), geniouxfacts, Personal Digital Transformation (g-f PDT): The Key to Success in the Digital Age
- g-f(2)1081 THE BIG PICTURE OF THE DIGITAL AGE (4/23/2023), HBR + Deloitte, Do You Really Know The Financial Impacts of Your Digital Transformation?
- g-f(2)1080 THE BIG PICTURE OF THE DIGITAL AGE (4/23/2023), Fast Company, How AI could power advertising’s next creative revolution
- g-f(2)1079 THE BIG PICTURE OF THE DIGITAL AGE (4/23/2023), Insead Knowledge, Leading Change in Turbulence: Case of Discovery Inc.
- g-f(2)1078 THE BIG PICTURE OF THE DIGITAL AGE (4/22/2023), MIT SMR, A Better Way to Pilot Emerging Technologies
- g-f(2)1077 THE BIG PICTURE OF THE DIGITAL AGE (4/22/2023), HBR, When Scenario Planning Fails
- g-f(2)1076 THE BIG PICTURE OF THE DIGITAL AGE (4/22/2023), geniouxfacts, The AI Revolution and the Transformation Revolution
- g-f(2)1075 THE BIG PICTURE OF THE DIGITAL AGE (4/21/2023), geniouxfacts, Bard and Bing Chatbot wrote poems to make the world a better place
- g-f(2)1074 THE BIG PICTURE OF THE DIGITAL AGE (4/21/2023), geniouxfacts, genioux Fact: Bard is a poet!
- g-f(2)1073 THE BIG PICTURE OF THE DIGITAL AGE (4/21/2023), geniouxfacts, genioux Fact: Bing Chatbot is a poet!
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