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Agile Mumbai 2022 - Abhishek Mishra | How to fail in your AI Endeavors

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Agile Mumbai 2022 - Abhishek Mishra | How to fail in your AI Endeavors

  1. 1. © 2021 Thoughtworks How to FAIL in AI Utterly, badly, consistently
  2. 2. © 2022 Thoughtworks About me 2 2011 Started as a dev in IB 2013 Joined a startup-like firm as UI dev 2017 Moved as ProMa to fintech 2019 Won a few awards in blogging, published my book 2022 Working on a cutting-edge data mesh implementation 2015 2021 Started ProMing award-nomina ted e-Learning product Joined Tw as Data ProMa
  3. 3. © 2022 Thoughtworks AI failures & why this deck 3 Bicameral doesn’t scale- CoE centers cannot effectively learn and apply data principles Lack of feedback loops- Insights have no clear way of making it back to the business General lack of authentic, shareable, high-quality data Fragmented space of data and AI, inundated with technlogy Vendor lock-in once a tool is finalized
  4. 4. © 2022 Thoughtworks But wait, no! 4 Sometimes, it’s hard to know where to start Lack of business readiness to be changed 4 Actual implementation is fraught with multiple vendors and hence, the politics that comes with it :) AI/ML is STILL a new world of unknown to many There is a TON of problems that occur way BEFORE the more talked about problems. These are cultural, human and avoidable
  5. 5. © 2021 Thoughtworks We will look at stories!
  6. 6. © 2021 Thoughtworks And some… learnings!
  7. 7. © 2021 Thoughtworks 7 Pre-cursor story- Establishing the pattern Problem statement(s): We want to: Be AI/ML driven Apply neural networks Build a data platform Create a data mesh Create a data science toolbox Do you see a problem?
  8. 8. © 2022 Thoughtworks | Confidential Story 1 It’s personal(ization) 8
  9. 9. © 2021 Thoughtworks 9 It’s personal(ization) A Fortune 100 healthcare-insurance company Partnerships with 500+ pharmacy chains in the US Owner of several patented products and formulae Client context
  10. 10. © 2021 Thoughtworks 10 It’s personal(ization) When customers visit our apps or websites, they see a very generic experience. We have good amount of data on the customer, but we are not putting it to use. Hence, we want to do personalization! To improve the quality of experience and to offer better products and services Client problem statement
  11. 11. © 2021 Thoughtworks 11 Let’s data science and AI the heck out of this! What we did It’s personal(ization)
  12. 12. © 2021 Thoughtworks 12 Let’s even present them journeys- to start with the “end” in mind What we did It’s personal(ization)
  13. 13. © 2021 Thoughtworks 13 Nothing happened!! The AI/ML engagement never took off Apart from a few PoCs, the customer never got around to actually implementing anything The result It’s personal(ization)
  14. 14. © 2021 Thoughtworks 14 It’s personal(ization) What do you think happened? Did they not have funds? Was tooling the challenge? Where did we go wrong?
  15. 15. © 2021 Thoughtworks 15 It’s personal(ization) COTS, COTS, COTS! The data isn’t in one place! Big plans = scary plans! The reveal
  16. 16. © 2022 Thoughtworks | Confidential Story 2 The strongest blizzard starts with a single Snowflake 16
  17. 17. © 2021 Thoughtworks 17 The strongest blizzard starts with a single Snowflake A chain of 100+ retail stores in South America Recent forays into FinTech Monopoly in the SA market Plans to have a centre in Turkey soon Client’s AI/ML engagement had been smoothly proceeding for ~1 year Client context
  18. 18. © 2021 Thoughtworks 18 The strongest blizzard starts with a single Snowflake Client says, “Mr. expert, your Presto is slowing us down It is getting in the way of being data-driven It is truly slow and not valuable Can you do something about it?” Problem statement
  19. 19. © 2021 Thoughtworks 19 The strongest blizzard starts with a single Snowflake Presto is an open source SQL query engine that's fast, reliable, and efficient at scale. Use Presto to run interactive/ad hoc queries at sub-second performance for your high volume apps. More context
  20. 20. © 2021 Thoughtworks 20 The architecture The strongest blizzard starts with a single Snowflake
  21. 21. © 2021 Thoughtworks 21 What did we do? The strongest blizzard starts with a single Snowflake A PoC to compare Presto and other data tools!
  22. 22. © 2021 Thoughtworks 22 What did we find? The strongest blizzard starts with a single Snowflake Presto was actually equally good if not better! Queries ran with the expected performance The AI/ML use cases were perfectly served with Presto
  23. 23. © 2021 Thoughtworks 23 The result The strongest blizzard starts with a single Snowflake Client killed the engagement Let’s replace Presto with Snowflake! And even then- their problems were not solved and we lost the business!
  24. 24. © 2021 Thoughtworks 24 The strongest blizzard starts with a single Snowflake Where did we go wrong? Can you guess what happened? Was our evaluation wrong? Was Snowflake actually better? Was Presto actually bad?
  25. 25. © 2021 Thoughtworks 25 The reveal The strongest blizzard starts with a single Snowflake Query writing Resources Influence
  26. 26. © 2022 Thoughtworks | Confidential Story 3 Can(not)cel 26
  27. 27. © 2021 Thoughtworks 27 Can(not)cel A very popular fitness chain based in the UK and Europe Serves millions of gym goers Awarded as most affordable fitness center five times in a row En route to Fortune 100 Client context
  28. 28. © 2021 Thoughtworks 28 Can(not)cel UK has put a law about cancellations! We have to allow people to digitally cancel Earlier, we made them jump through hoops But we cannot do that anymore! How can we enable a smooth cancellation process and predict the number of cancellations we will have using some models? Problem statement
  29. 29. © 2021 Thoughtworks 29 Can(not)cel We got to make some design and process recommendations! What did we do
  30. 30. © 2021 Thoughtworks 30 Can(not)cel Oh this is it! We will show them how great we are! We also started building models Present findings!
  31. 31. © 2021 Thoughtworks 31 Can(not)cel They wouldn’t bite it! The entire cancellation piece just lay stuck. The cancellation law became a nightmare to adhere to as a result The result
  32. 32. © 2021 Thoughtworks 32 Can(not)cel Where did we go wrong? Can you guess what happened? Did we not apply AI/ML at the beginning? Was doing user journeys bad? Was there not executive backing?
  33. 33. © 2021 Thoughtworks 33 Can(not)cel The “sleeping dogs” The reveal
  34. 34. © 2022 Thoughtworks | Confidential Story 4 Less is more 34
  35. 35. © 2021 Thoughtworks 35 Less is more An international NGO based out of Austria Works on organising disease control and eradication drives Used AI/ML models during Covid to predict the spread of the virus Client context
  36. 36. © 2021 Thoughtworks 36 Our analysts have difficulty in finding right datasets No ready sandbox to actually play with data No way to explore data Problem statement Less is more
  37. 37. © 2021 Thoughtworks 37 Let’s have a data science sandbox Introduce a lakehouse that the sandbox can talk to Enable CDC for large databases Run PoCs with multiple databases What did we do Less is more
  38. 38. © 2021 Thoughtworks 38 Data science sandbox works well We are able to access large datasets and enable AI/ML engineers to run their experiements Early promise! Less is more
  39. 39. © 2021 Thoughtworks 39 The platform was slow to be adopted Auditors found frequent issues with the models We heard reports of “confusion” The result Less is more
  40. 40. © 2021 Thoughtworks 40 Where did we go wrong? Can you guess what happened? Was the data not enough? Did we need to think about governance? Less is more
  41. 41. © 2021 Thoughtworks 41 The “reference” data sets The reveal Less is more
  42. 42. © 2022 Thoughtworks | Confidential Thank you

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