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Augmented Intelligence for EmTech May 2016 (Anand-final-Without Video) Presentation

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Augmented Intelligence for EmTech May 2016 (Anand-final-Without Video) Presentation

  1. 1. Dr. Anand S. Rao Partner, Innovation Lead, PwC Data & Analytics Augmented Intelligence: The art of making complex business decisions 05.23.16 www.pwc.com/digitalandtech
  2. 2. PwC 2
  3. 3. PwC AI as AAAI 3 Assisted Intelligence  Nature of tasks don’t change  Tasks are automated  Humans don’t learn  Machines learn Augmented Intelligence  Nature of tasks change  Humans inform Machines  Machines inform humans Autonomous Intelligence  Nature of tasks change  Decisions are automated  Machines learn continuously Man-Machine Intelligence Continuum
  4. 4. PwC Case study—Market Adoption Model for Personal Mobility 4 01 Market entry strategies (e.g., personal mobility) are complex business decisions with uncertain, incomplete, and sparse data and multiple stakeholders
  5. 5. PwC An agent-based simulation model captured the entire ecosystem of players, decisions, and assumptions 5 Consumer Sites Point of Departure (POD) Program Adoption Usage Reservation Car Service Provider
  6. 6. PwC The model was calibrated to different market conditions of consumer acceptance To account for randomness experienced in dynamic systems, each strategy for each city was conducted 10 times Over 200,000 strategic scenarios were simulated to explore the ‘least regret’ strategy to enter and dominate markets 6 Approximately 200k simulations were conducted over the course of the analysis ~6,000 Selected simulations Select cities Strategies Market conditions Environ- mental random- ness Cities selected in the previous analysis, using a Demographic model and the Demand Estimator, were used in the analysis Different strategies were tested, varying, among others:  Price  Aggressiveness of Entry  Marketing  Customer Service
  7. 7. PwC Over the course of 18 months, the combination of human and artificial intelligence led to a superior understanding of the dynamics of the personal mobility ecosystem modeled by an agent-based system 7  AI: Agent-based model that captured dynamics of driver behavior, technology advances, demand and supply dynamics, business models, investments, returns, advertising spend, etc.  Decision Maker: Decisions on cities to enter, advertising spend, business model to adopt, investments required, market share targets, learning curve effects, etc. Augmented Intelligence  Basic understanding of Personal Mobility ecosystem  Good understanding of auto buyers, drivers, and competitive environment Human Intelligence  No understanding of underlying dynamics of customer adoption, technology changes and regulatory impacts Artificial Intelligence
  8. 8. PwC Case study—Digital Advice Models 8 02 Financial Services firms are moving along the spectrum of AAAI in developing financial advice and portfolio management solutions
  9. 9. PwC PwC has developed a proprietary synthetic population with household level financial statements for 330 million US individuals using multiple data sources 9 Household financial statement 01 Balance sheet – Assets & Liabilities 02 Income statement – Income & Expenses 03 Demographics / Family Structure 04 Behavioral Preferences
  10. 10. PwC Cradle-to- Grave Simulations Scenario- based Planning PwC’s $ecureTM is a cognitive digital-advisor built as an agent-based simulation model that projects complex financial decisions of households from cradle-to-grave 10 Behavioral Economics & Simulation 3 4 5 $ecureTM Synthetic US Population /Household Holistic Household View 2 1
  11. 11. PwC Personalized cradle-to- grave simulation of 330 million consumer agents informs an ongoing holistic financial planning and execution process between the Advisor, Consumer, and the $ecureTM platform 11 • AI: Agent-based model that captured dynamics of economy, market returns, individual investor needs, behavior, health shocks, specific product characteristics, product actions, etc. • Consumer: Decisions on how to satisfy goals, savings vs spending, when to retire, risk appetite, etc. • Advisor: Asset class selection, portfolio optimization, holistic advice, fiduciary role Augmented Intelligence  Consumer: Low-to-medium knowledge of investing  Advisor: Medium-to-high level of knowledge & expertise Human Intelligence  No understanding of underlying dynamics of economy, market, or investor behavior Artificial Intelligence
  12. 12. PwC’s Digital Services Augmented Intelligence Agent-based simulation offers a viable approach for enterprises to capture the underlying structure and behavior of decision-makers Initially the agents embody the human insights, but as the simulation unfolds the emergent behavior informs the humans on the importance of the structure/behavior of the ecosystem As the system continuously reacts to stimuli from the external world it learns and adapts to changing circumstances and its own errors Humans and machines are in a symbiotic relationship where each is continuously improving based on the interaction with the other embodying true augmented intelligence 12
  13. 13. PwC For More Information Contact: Dr. Anand S. Rao anand.s.rao@pwc.com Twitter: @AnandSRao LinkedIn: www.linkedin.com/in/anandsrao To stay connected on PwC's latest emerging technology insights, please subscribe at: http://pwc.to/ETinsights 13 © 2016 PwC. All rights reserved. PwC refers to the US member firm or one of its subsidiaries or affiliates, and may sometimes refer to the PwC network. Each member firm is a separate legal entity. Please see www.pwc.com/structure for further details.

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