ULTRA-condensed knowledge

Opportunity, Achieving return on AI projects, MIT SMR

Some companies have achieved economic return on their AI investments.
Their strategies for finding value include establishing close relationships between the data group and interested business units, selecting projects with tangible value and a clear path to production, lining up trust from key stakeholders in advance of development, building reusable AI products, selectively employing “proof of concept” projects, and establishing a management pipeline or funnel leading projects toward production implementation.

Lesson learned, Bringing the benefits of AI into a company

Bringing the benefits of artificial intelligence into a company requires good working relationships between the data team and the business units — and a clear focus on tangible value.

Opportunity, Six Strategies Toward Value

AI projects typically are led by the company’s data science group, and that group is tasked with both executing the projects and taking responsibility for their achievements. These six strategies can help guide the data science team toward a greater chance of success in these cross-unit projects.

Focus on partnerships with AI-friendly business units.
Select projects with tangible values and a clear path to production.
Foster stakeholder trust and sponsorship in advance of development.
Build reusable AI products to drive scale.
Use PoCs (A proof of concept) selectively, but create a path to implementation.
Manage the project pipeline toward full implementation.

Genioux knowledge fact condensed as an image

Condensed knowledge

Classical CONTEXT of the Digital Age

CONTEXT the Golden Knowledge Path ("GKPath") digital highway

"genioux facts", the online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, builds The Golden Knowledge Path (GKPath) to accelerate everyone's success in the digital age.

The inverted mini pyramid of knowledge that is essential to travel on the Golden Knowledge Path ("GKPath") highway.

g-f(2)256 The Big Picture of the Digital Age (5/2/2021), geniouxfacts, The fundamental knowledge mini-pyramid for traveling on the "GKPath" highway.

g-f(2)261 The Big Picture of the Digital Age (5/5/2021), geniouxfacts, You are unique. Grow without limits in the digital age.
g-f(2)257 The Big Picture of the Digital Age (5/3/2021), geniouxfacts, At any moment you take the fastest path (GKPath) to your greatness.
g-f(2)254 The Big Picture of the Digital Age (5/1/2021), geniouxfacts, The Golden Knowledge Path (GKPath) to accelerate everyone's success.
g-f(2)260 The Big Picture of the Digital Age (5/4/2021), geniouxfacts, Alerts, Warnings and Relevant Lessons Learned Traveling at High Speed on GKPath.

The big picture of the digital age
Opportunities those travelling at high speed on GKPath

Opportunity, Achieving return on AI projects, MIT SMR

Some companies have achieved economic return on their AI investments.
Their strategies for finding value include establishing close relationships between the data group and interested business units, selecting projects with tangible value and a clear path to production, lining up trust from key stakeholders in advance of development, building reusable AI products, selectively employing “proof of concept” projects, and establishing a management pipeline or funnel leading projects toward production implementation.

Lesson learned, Bringing the benefits of AI into a company

Bringing the benefits of artificial intelligence into a company requires good working relationships between the data team and the business units — and a clear focus on tangible value.

Opportunity, Six Strategies Toward Value

AI projects typically are led by the company’s data science group, and that group is tasked with both executing the projects and taking responsibility for their achievements. These six strategies can help guide the data science team toward a greater chance of success in these cross-unit projects.

Focus on partnerships with AI-friendly business units.
Select projects with tangible values and a clear path to production.
Foster stakeholder trust and sponsorship in advance of development.
Build reusable AI products to drive scale.
Use PoCs (A proof of concept) selectively, but create a path to implementation.
Manage the project pipeline toward full implementation.

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 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

Fernando Machuca

References

Achieving Return on AI Projects, Thomas H. Davenport and Ren Zhang, July 20, 2021, MIT Sloan Management Review, MIT SMR.

ABOUT THE AUTHORS

Thomas H. Davenport (@tdav) is the President’s Distinguished Professor of Information Technology and Management at Babson College, a visiting professor at Oxford’s Saïd Business School, and a fellow of the MIT Initiative on the Digital Economy. Ren Zhang is the chief data scientist for BMO Financial Group, a member of the Business of Data’s global advisory board, and a mentor to the Creative Destruction Lab.

Thomas H. Davenport

Extracted from Wikipedia

Thomas Hayes "Tom" Davenport, Jr. (born October 17, 1954) is an American academic and author specializing in analytics, business process innovation, knowledge management, and artificial intelligence. He is currently the President’s Distinguished Professor in Information Technology and Management at Babson College, a Fellow of the MIT Initiative on the Digital Economy, Co-founder of the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics.

Davenport has written, coauthored, or edited twenty books, including the first books on analytical competition, business process reengineering and achieving value from enterprise systems, and the best seller, Working Knowledge (with Larry Prusak) (Davenport & Prusak 2000), on knowledge management. He has written more than one hundred articles for such publications as Harvard Business Review, MIT Sloan Management Review, California Management Review, the Financial Times, and many other publications. Davenport has also been a columnist for The Wall Street Journal, CIO, InformationWeek, and Forbes magazines.

Ren Zhang

Chief Data Scientist

BMO Financial Group

Ren is the Chief Data Scientist for BMO Financial, in charge of driving adoption of AI and Machine Learning capabilities to automate processes and deliver better predictive decisions that will help the businesses drive accelerated revenue, cost productivity and customer outcomes across the enterprise. Ren has over 15 years experience as a senior AI leader within various financial organizations. Prior to BMO Financial, she worked at Prudential Financial, where she was Vice-President and Head of Data Science. Prior to Prudential, Ren was Executive Director of Data Science and Innovation at Commonwealth Bank of Australia. And at American Express, she held progressively senior leader roles, ranging from credit risk management, to loyalty analytics, to fraud risk strategy, and to risk capabilities, with her last being Vice-President, Risk and Information Management of Enterprise Growth. Ren holds a PhD in Statistics from The Wharton School at the University of Pennsylvania.