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Product owners are you ready to ride on AI wave

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Presentation deck used during Agile & DevOps day conference held in Pune (India) on 22nd June

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Product owners are you ready to ride on AI wave

  1. 1. 2 PRESENTERS RAGHAVENDRA MEHARWADE • Oxfam Trailwalker • Lead Functional Architect for Accenture myWizard®—Agile • Scrum Master/Agile SME/Coach • Agilist • Agile & AI Functional Architect • CSM, SAFe Agilist, Trained Kanban Practitioner ANUBHAV GUPTA Copyright © 2019 Accenture All rights reserved.
  2. 2. 3 Importance of AI For Product Owners, NOW? Journey Towards AI Aware Product Owner First Level Challenges Challenges 2.0 or Opportunities? Q&A TOPIC FLOW 1 2 3 4 5 Copyright © 2019 Accenture All rights reserved.
  3. 3. 4 IMPORTANCE OF AI FOR PRODUCT OWNERS, NOW? Copyright © 2019 Accenture All rights reserved.
  4. 4. 5 AI POTENTIAL VS CURRENT ADOPTION Copyright © 2019 Accenture All rights reserved. Source: https://www.forbes.com/sites/louiscolumbus/2018/04/30/sizing-the-market-value-of-artificial-intelligence/#5a13f00effe9
  5. 5. 6 Journey Towards AI Aware Product Owner Copyright © 2019 Accenture All rights reserved.
  6. 6. 7 1 2 3 4 5 6 7 8 9 Get Familiar With AI Identify Problems Prioritize Capability Assessment Data Sketch The Journey Start Small Build With Balance Assess and Refine YOUR FIRST STEP TOWARDS UNLIMITED OPPORTUNITIES Copyright © 2019 Accenture All rights reserved.
  7. 7. 8 AI ALLOWS SMART MACHINES TO EXTEND HUMAN CAPABILITIES SENSE COMPREHEND ACT LEARN Computing Vision Audio Processing Natural Language Processing Knowledge Representation Machine Learning Expert Systems Virtual Agent Identity Analytics Cognitive Robotics Speech Analytics Recommendation Systems Data Visualization WHAT IS AI? Perceive the world Analyze and understand Make informed decisions Improve performance Copyright © 2019 Accenture All rights reserved.
  8. 8. 9 WHAT IS MACHINE LEARNING (ML) Traditional Programming Data Program Processor Output Machine Learning Data Output Processor Program How it works? Copyright © 2019 Accenture All rights reserved.
  9. 9. 10 Machine Learning • The training set is labeled. • Classification and Regression • The training set is unlabeled. • Clustering • No labeled or unlabeled data set. • Algorithm learns to act in an env. to maximize reward. • Customer Segmentation • Weather Forecast • Spam Mail Detection • Speech Recognition • Self-driving Car • Chess Supervised Unsupervised Reinforcement TYPES OF MACHINE LEARNING Copyright © 2019 Accenture All rights reserved.
  10. 10. 11 FAMILIARIZATION WITH AI Follow AI Gurus – such as – Andrew NG, Jason Brownlee Specialization through online training providers such as coursera & udemy Machine Learning Foundations: A Case Study Approach Machine Learning Guide from Udemy: Learn Machine Learning Algorithms Formal classroom / online trainings AI technologies are changing rapidly AI Primer Guide Copyright © 2019 Accenture All rights reserved. Source: https://www.accenture.com/us-en/insights/artificial-intelligence/artificial-intelligence-explained-executives?c=glb_artificialintelexacttarget_10388747&n=emc_1018&emc=22324719:emc-102218 https://www.coursera.org/ https://www.udemy.com/
  11. 11. 12 FEATURE ENGINEERING… WHAT? Feature Engineering is also called variable engineering or attribute engineering. It is the selection of attributes in your data (such as columns in tabular data) that are most relevant to the predictive modeling problem you are working on. Copyright © 2019 Accenture All rights reserved.
  12. 12. 13 WHY?... TO AVOID AI FAILURES Copyright © 2019 Accenture All rights reserved. Share & discuss real world examples
  13. 13. 14 LEVEL 1 CHALLENGES Copyright © 2019 Accenture All rights reserved.
  14. 14. 15 DATA THREAT! Supervised Un-supervised New data Copyright © 2019 Accenture All rights reserved.
  15. 15. 16 UNCONTROLLED LEARNING Copyright © 2019 Accenture All rights reserved. Share & discuss real world examples
  16. 16. 17 MODEL SECURITY Copyright © 2019 Accenture All rights reserved. Share & discuss real world examples
  17. 17. 18 MODEL & ITS OUTCOME ARCHIVAL APPLY FOR LOAN Hi, How can I assist you? ~~~~~~~~ ~~~~~~ ~~~~~~~ ~~~~~ ~~~~~~ ~~~~~ ~~~~~~ ~~~~~~~~ ~~~~~~ ~~~~~~~ Your application is rejected WHAT? WHY? BUT… Copyright © 2019 Accenture All rights reserved.
  18. 18. 19 CHALLENGES 2.0 OR OPPORTUNITIES Copyright © 2019 Accenture All rights reserved.
  19. 19. 20 EXPLAINABLE AI I understand I understand I know I know I know I know NON-EXPLAINABLE AI why why not when you succeed when you fail when to trust you why you errored Explainability PredictionAccuracy Copyright © 2019 Accenture All rights reserved.
  20. 20. 21 RESPONSIBLE AI UNBIASED ROBUST ADJUSTABLE GOVERNED ETHICAL & LEGAL EXPLAINABLE TRANSPARENT HUMAN-LED Copyright © 2019 Accenture All rights reserved.
  21. 21. 22 A DAY IN LIFE OF PRODUCT OWNER 09.00 am 09.30 am 11.00 am 12.30 pm 02.30 pm 03.30 pm 05.00 pm Daily stand up Product Backlog update Connect with Team Assess release & sprint progress. Prepare for connect with Head of Product Owners Connect with Business stakeholders Product Backlog refinement for next sprint Join daily EOD defect triage call (Team includes data scientists) (Validation business process maturity) (Present PoV on AI Vs Automation) Confirm & secure data repository, perform feature engineering Identify potential data source, define expected data quality (volume, quality, fairness) (AI) Copyright © 2019 Accenture All rights reserved.
  22. 22. 23 & Copyright © 2019 Accenture All rights reserved.
  23. 23. 24 REFERENCES • https://medium.com/datadriveninvestor/how-to-build-ai-into-your-product-ae6b54e020c0 • https://in.pcmag.com/tableau-desktop/111539/10-steps-to-adopting-artificial-intelligence-in-your-business • https://dzone.com/articles/3-ai-fails-and-why-they-happened • https://medium.com/syncedreview/2018-in-review-10-ai-failures-c18faadf5983 Copyright © 2019 Accenture All rights reserved.
  24. 24. 25 This Presentation has been published for information and illustrative purposes only and is not intended to serve as advice of any nature whatsoever. The information contained and the references made in this Presentation is in good faith and neither Accenture nor any its directors, agents or employees give any warranty of accuracy (whether expressed or implied), nor accepts any liability as a result of reliance upon the content including (but not limited) information, advice, statement or opinion contained in this Presentation. This Presentation also contains certain information available in public domain, created and maintained by private and public organizations. Accenture does not control or guarantee the accuracy, relevance, timelines or completeness of such information. Accenture does not warrant or solicit any kind of act or omission based on this Presentation. The Presentation is the property of Accenture and its affiliates and Accenture be the holder of the copyright or any intellectual property over the Presentation. No part of this document may be reproduced in any manner without the written permission of Accenture. Opinions expressed herein are subject to change without notice. DISCLAIMER

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