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Human Collective Intelligence: the future of corporate innovation

How AI can improve decision-making in organizations by leveraging knowledge and opinions of experts.

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Human Collective Intelligence: the future of corporate innovation

  1. 1. September 2019 Human Collective Intelligence The Future of Corporate Innovation
  2. 2. 1 Transforming Wisdom of Customer Crowds: The Challenge 1999: Lego Direct was created to be a global direct-to-consumer “corporate startup” encompassing, LEGO Shop at Home and other LEGO entities 2001: LEGO creates first consumer Dialogue Portal 2002: Message boards are co-developed Objectives § Listen and respond to customers § Understand the consumer mindset to guide development § Build and improve brand loyalty § Make consumer part of the company § Find the relationship margin and not just the profit margin Result: More than 250K registered users in the first year
  3. 3. 2 Transforming Wisdom of Customer Crowds Question to Customers: “What would you like to experience when …” Top Ranked Response: “A long engaging experience of building” Product Idea: The LEGO Star Wars Imperial Star Destroyer - The largest and most expensive LEGO set ever offered. Initial Internal Reaction: “You’ll never sell something like that.” Results: Most profitable product launch in company history, gross margin dollars >10x company average
  4. 4. 3 Learning from Conversations with Customers: 2001 - 2008
  5. 5. 4 Learnings from Innovating with Collective Intelligence § Rating as listening company up from 18% to 72% § Uncover unmet market needs to generate product ideas § Reduced time to market § Cost-effective product testing § Solicited targeted, rapid insights on key shows § Uncovered shifting attitudes over days/weeks § Made changes before viewers changed behaviors Engaging customers as partners in innovation creates radical positive change
  6. 6. 5 A New Generation of Human-First AI § Move beyond feedback management and knowledge models § Acquire knowledge through assessment strategies § Prove its power: started with predicting startup success § Integrate the best of knowledge system and AI technologies
  7. 7. 6 Three Foundational Technologies Required § Computational models of expertise (Symbolic AI – first wave) § Automation of knowledge acquisition via collective intelligence § Bayesian Inference that learns from data
  8. 8. 7 What We Do CrowdSmart is a pioneer in the use of human- first artificial intelligence designed to improve decision-making in organizations, by leveraging the diverse knowledge and opinionsof a community of experts.
  9. 9. 8 CrowdSmart Human-Powered AI Overview CI Learning Algorithm + NLP Bayesian Belief Network Provides transparency and thematic explanations for startup scoring Expert Input Experts assess startups guided by the knowledge acquisition system CrowdSmart Knowledge Model Bayesian belief network is influenced by 4+ years of assessing and measuring startups Bayesian Classifier Platform Outputs › Decision: Invest/Pass › Probability of success › Breakdown of decision factors › Expert influence ranking
  10. 10. 9 The Future of Corporate Innovation § Strategic investment in startups and new initiatives with accuracy and precision § Collaborative innovation with customer and expert communities to create transformative new offerings § Identification of thought leaders from within § Create a cycle of accumulating accurate learning
  11. 11. 10 Collective Prediction of Seed Stage Startup Scores or ratings with supporting reasons allow for predictions with explanations
  12. 12. 11 Breaking Out of Your Own Rut § Reach beyond the old way of doing things § Leverage scientific and technology-based approach to expanding options § Create a system for learning fast § Integrate internal team views with external views
  13. 13. 12 Why Modelling the Collective Mind Matters § Identify precise implications of an investment § Understand the facts, knowledge and influencers behind a decision or investment § Predict the results of the model and act § Create a cycle of accumulating accurate learning
  14. 14. 13 Building the Future on Human-First AI § Discover new market opportunities § Leverage the best minds to refine and predict outcomes § Rapidly learn your next move
  15. 15. 14 Appendix (The Science)
  16. 16. Diversity of expert opinion is the key to increasing prediction accuracy 15 Team collective prediction error = Average individual error – Prediction diversity Two proven collective intelligence prediction accuracy facts Collective Intelligence Science Diversity of perspective, experience, education, and cultural background of evaluators reduces collective team prediction error The collective team prediction error will always be less than the average individual error provided the team has prediction diversity1 2 The math of accurate predictions
  17. 17. 16 Essential Technology for Collective Intelligence 1. Discover new ideas from open ended question 2. Allow for evolution of ideas from group interactions 3. Learn a statistically significant ranking 1. Rapidly learns priorities 2. Process must be stable and reproducible 3. Correctly give high rankings to important ideas 4. Correctly give low rankings to unimportant ideas Requirements Results
  18. 18. Bayesian Learning Basics The science and math for generating accurate individual predictions 17 Bayesian Principle Ex: Evaluator Evaluation Belief Mathematical Formula Beliefs are expressed as a probability distribution I believe this startup has only moderate odds of an ROI p(H) = Prior odds Given evidence, how likely is it to change beliefs Feedback from expert team and founder interactions are compelling p(E|H) = Likelihood (odds change) Update beliefs based on evidence I am convinced that this startup has a high odds of an ROI p(H|E) = Posterior odds p(D|H)= p(H|D)p(D) p(H)