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AI in Business: Opportunities & Challenges

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Tathagat Varma
Tathagat Varma

In this talk, I discuss how AI impacts the business, what are the challenges and what are the opportunities.

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AI IN BUSINESS: OPPORTUNITIES & CHALLENGES TATHAGATVARMA VP STRATEGY, WALMART GLOBAL TECH PHD SCHOLAR (EFPM), INDIAN SCHOOL OF BUSINESS (ISB)
DISCLAIMER THESE ARE MY PERSONALVIEWS!
PERSPECTIVES ON AI
PROJECTED ECONOMIC IMPACT OF $15 TRILLION BY 2030…!!! http://preview.thenewsmarket.com/Previews/PWC/DocumentAssets/476830.pdf
AI in Business: Opportunities & Challenges
VIRTUALLY ALL ASPECTS OF MOST BUSINESSES… https://info.algorithmia.com/2021
BUT,THERE’S ALSO A LOT OF HYPE!!! https://www.gartner.com/en/articles/the-4-trends-that-prevail-on-the-gartner-hype-cycle-for-ai-2021
…WITH ALARMINGLY HIGH FAILURE RATES! • Gartner: 85% of AI projects will fail and deliver erroneous outcomes through 2022. • MIT SMR: 70% of companies report minimal or no impact from AI.Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 report business gains from AI in the past three years. • VentureBeat: 87% of data science projects never make it into production.Tom Siebel says 99% of internal AI projects fail! • Andrew Ng (HBR): It is not unusual for teams to celebrate a successful proof of concept, only to realize that they still have another 12-24 months of work before the system can be deployed and maintained. https://research.aimultiple.com/ai-fail/
…AND LOW ROI & LONG PAYBACK PERIODS! The ROI for AI projects varies greatly, based on how much experience an organization has. Leaders showed an average of a 4.3% ROI for their projects, compared to only 0.2% for beginning companies. Payback periods also varied, with leaders reporting a typical payback period of 1.2 years and beginners at 1.6 years. https://www2.deloitte.com/us/en/insights/industry/technology/artificial-intelligence-roi.html
SO,WHAT IS AI? HINT:THERE IS NOTHING REALLY “INTELLIGENT” ABOUT IT!
WHAT IS “ARTIFICIAL INTELLIGENCE”, OR AI? Prof John McCarthy, Father of Artificial Intelligence: • Intelligence is the computational part of the ability to achieve goals in the world.Varying kinds and degrees of intelligence occur in people, many animals and some machines. • Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. http://jmc.stanford.edu/artificial-intelligence/what-is-ai/index.html
AI EVOLUTION TIMELINE… https://sitn.hms.harvard.edu/flash/2017/history-artificial-intelligence/
EVOLUTION OF AI, ML, DL https://www.stateofai2019.com/summary
MACHINE LEARNING • Term coined by Arthur Samuel in 1959 • Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. • Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects.These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. https://www.ibm.com/cloud/learn/machine-learning
POTENTIAL OF AI •AI is the new electricity – Andrew Ng •AI is more profound than fire or electricity – Sundar Pichai •People should stop training radiologists – Geoff Hinton, 2016, https://youtu.be/2HMPRXstSvQ
WHERE TO USE AI? If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future. – Andrew Ng https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now
SO,WHY NOW? After decades of start/stops, finally AI seems to be at the cusp of its (third) resurgence.
AI IS THE NEW SOFTWARE! • Machine learning was a huge leap from programmed instructions and if-then statements that merely simulated the very human process of thinking and making decisions. • With machine learning, the machine no longer needs to be explicitly programmed to complete a task; it can pour through massive data sets and create its own understanding. It can learn from the data and create its own model, one that represents the different rules to explain relationships among data and use those rules to draw conclusions and make decisions and predictions. • A machine learning algorithm is a mathematical function that enables the machine to identify relationships among inputs and outputs.The programmer’s role has shifted from one of writing explicit instructions to creating and choosing the right algorithms. Rose, Doug.Artificial Intelligence for Business:WhatYou Need to Know about Machine Learning and Neural Networks.
HOW IS AI DIFFERENT FROM SOFTWARE? Traditional Software AI Software Reasoning Deductive Inductive Inputs Data + Program Data + Output Logic Manually pre-programmed to perform a specific task on a given dataset Programmed to automatically keep learning rules from a given dataset Output Output Models, Rules Learning Learns one-time from the programmer “Learns” constantly being the data Resource Code Data Solutions Deterministic Probabilistic Output Consistently remains the same Can improve with usage (or degrade over time) Business model One-time development efforts, followed by multiple sales, and small maintenance effort (optional) Each project is one-off, and needs full lifecycle management mandatorily
FAILURE MODES OF AI • Brittleness • Embedded Bias • Catastrophic Forgetting • Explainability • Quantifying Uncertainty • Common Sense • Math Source: https://spectrum.ieee.org/ai-failures
CHALLENGES • Unavailability of Skills &Talent • Technology is quite good, but still maturing • Data is often siloed or not available, or poor quality, etc. • Business case, and alignment of strategy and business model to an AI-firm • Societal concerns like ethics, privacy, transparency, bias, surveillance, etc. • Governance issues such as data management, regulations, legal frameworks, etc. • …..and many many more…!!!
BUSINESS BENEFITS OF AI https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
AI-POWERED ORGANIZATION https://hbr.org/2019/07/building-the-ai-powered-organization
RECAP • Today’s AI is not really “AI”! At most, it is what we call as “Machine Learning” (ML). But, let’s stick with AI for now J • In the broadest sense, it is a form of a “General Purpose Technology” (GPT). Just like fire, electricity or computers. • AI has huge potential to solve problems that could benefit from high-speed and at-scale data- based decision-making for routine and repetitive tasks , especially for “noisy data” conditions. • Perhaps the first trillion-dollar economy that won’t require natural resources, financial capital, or human labor. • Challenges with adoption notwithstanding, there are huge opportunities for businesses to leverage AI to boost productivity and deliver financial returns.
FINALLY…

More Related Content

AI in Business: Opportunities & Challenges

  • 1. AI IN BUSINESS: OPPORTUNITIES & CHALLENGES TATHAGATVARMA VP STRATEGY, WALMART GLOBAL TECH PHD SCHOLAR (EFPM), INDIAN SCHOOL OF BUSINESS (ISB)
  • 2. DISCLAIMER THESE ARE MY PERSONALVIEWS!
  • 4. PROJECTED ECONOMIC IMPACT OF $15 TRILLION BY 2030…!!! http://preview.thenewsmarket.com/Previews/PWC/DocumentAssets/476830.pdf
  • 7. BUT,THERE’S ALSO A LOT OF HYPE!!! https://www.gartner.com/en/articles/the-4-trends-that-prevail-on-the-gartner-hype-cycle-for-ai-2021
  • 8. …WITH ALARMINGLY HIGH FAILURE RATES! • Gartner: 85% of AI projects will fail and deliver erroneous outcomes through 2022. • MIT SMR: 70% of companies report minimal or no impact from AI.Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 report business gains from AI in the past three years. • VentureBeat: 87% of data science projects never make it into production.Tom Siebel says 99% of internal AI projects fail! • Andrew Ng (HBR): It is not unusual for teams to celebrate a successful proof of concept, only to realize that they still have another 12-24 months of work before the system can be deployed and maintained. https://research.aimultiple.com/ai-fail/
  • 9. …AND LOW ROI & LONG PAYBACK PERIODS! The ROI for AI projects varies greatly, based on how much experience an organization has. Leaders showed an average of a 4.3% ROI for their projects, compared to only 0.2% for beginning companies. Payback periods also varied, with leaders reporting a typical payback period of 1.2 years and beginners at 1.6 years. https://www2.deloitte.com/us/en/insights/industry/technology/artificial-intelligence-roi.html
  • 10. SO,WHAT IS AI? HINT:THERE IS NOTHING REALLY “INTELLIGENT” ABOUT IT!
  • 11. WHAT IS “ARTIFICIAL INTELLIGENCE”, OR AI? Prof John McCarthy, Father of Artificial Intelligence: • Intelligence is the computational part of the ability to achieve goals in the world.Varying kinds and degrees of intelligence occur in people, many animals and some machines. • Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. http://jmc.stanford.edu/artificial-intelligence/what-is-ai/index.html
  • 13. EVOLUTION OF AI, ML, DL https://www.stateofai2019.com/summary
  • 14. MACHINE LEARNING • Term coined by Arthur Samuel in 1959 • Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. • Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects.These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. https://www.ibm.com/cloud/learn/machine-learning
  • 15. POTENTIAL OF AI •AI is the new electricity – Andrew Ng •AI is more profound than fire or electricity – Sundar Pichai •People should stop training radiologists – Geoff Hinton, 2016, https://youtu.be/2HMPRXstSvQ
  • 16. WHERE TO USE AI? If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future. – Andrew Ng https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now
  • 17. SO,WHY NOW? After decades of start/stops, finally AI seems to be at the cusp of its (third) resurgence.
  • 18. AI IS THE NEW SOFTWARE! • Machine learning was a huge leap from programmed instructions and if-then statements that merely simulated the very human process of thinking and making decisions. • With machine learning, the machine no longer needs to be explicitly programmed to complete a task; it can pour through massive data sets and create its own understanding. It can learn from the data and create its own model, one that represents the different rules to explain relationships among data and use those rules to draw conclusions and make decisions and predictions. • A machine learning algorithm is a mathematical function that enables the machine to identify relationships among inputs and outputs.The programmer’s role has shifted from one of writing explicit instructions to creating and choosing the right algorithms. Rose, Doug.Artificial Intelligence for Business:WhatYou Need to Know about Machine Learning and Neural Networks.
  • 19. HOW IS AI DIFFERENT FROM SOFTWARE? Traditional Software AI Software Reasoning Deductive Inductive Inputs Data + Program Data + Output Logic Manually pre-programmed to perform a specific task on a given dataset Programmed to automatically keep learning rules from a given dataset Output Output Models, Rules Learning Learns one-time from the programmer “Learns” constantly being the data Resource Code Data Solutions Deterministic Probabilistic Output Consistently remains the same Can improve with usage (or degrade over time) Business model One-time development efforts, followed by multiple sales, and small maintenance effort (optional) Each project is one-off, and needs full lifecycle management mandatorily
  • 20. FAILURE MODES OF AI • Brittleness • Embedded Bias • Catastrophic Forgetting • Explainability • Quantifying Uncertainty • Common Sense • Math Source: https://spectrum.ieee.org/ai-failures
  • 21. CHALLENGES • Unavailability of Skills &Talent • Technology is quite good, but still maturing • Data is often siloed or not available, or poor quality, etc. • Business case, and alignment of strategy and business model to an AI-firm • Societal concerns like ethics, privacy, transparency, bias, surveillance, etc. • Governance issues such as data management, regulations, legal frameworks, etc. • …..and many many more…!!!
  • 24. RECAP • Today’s AI is not really “AI”! At most, it is what we call as “Machine Learning” (ML). But, let’s stick with AI for now J • In the broadest sense, it is a form of a “General Purpose Technology” (GPT). Just like fire, electricity or computers. • AI has huge potential to solve problems that could benefit from high-speed and at-scale data- based decision-making for routine and repetitive tasks , especially for “noisy data” conditions. • Perhaps the first trillion-dollar economy that won’t require natural resources, financial capital, or human labor. • Challenges with adoption notwithstanding, there are huge opportunities for businesses to leverage AI to boost productivity and deliver financial returns.