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Automation, Analytics, and Artificial Intelligence - Panel
- 1. 11.29.18 Automation, Analytics & Artificial Intelligence: Opportunities, Risks, and Challenges Dr Anand S. Rao, Global AI Lead, PwC
- 2. PwC’s Digital Services Our world is rapidly changing... 64% of executives believe technology will disrupt how they do business in next 5 years. —PwC Source: PwC’s 21st Annual Global CEO Survey (2018) 2
- 3. Enterprises are realizing the value from digitization to AI along two distinct but related paths, to enhance productivity, increase profits and enhance experience 3 Digitization Artificial Intelligence Productivity Experience Profits Simplification Standardization Automation Decision-making Personalization Analytics Revenues AutomationPath AnalyticsPath
- 4. Automation Path: Enterprises are moving from BPA to IPA to fully exploit AI, enhance productivity and reduce costs of operation 4 Macros and Scripts Rules-based automation within a specific application (e.g., Excel) to provide users with a way to automate a repeatable process with highly structured data Business Process Automation (BPA) Reengineering existing business processes by using software, integrating systems, and restructuring labor to optimize workflows and minimize costs Robotic Process Automation (RPA) Alias: Robotic Desktop Automation (RDA) Automating labor-intensive, repetitive activities across multiple systems and interfaces by training and/or programming third-party software to replicate a user’s workflow Operates at the presentation layer without the need to change existing systems Intelligent Process Automation (IPA) Aliases: Cognitive Computing, Smart Workflows Combining RPA with artificial intelligence technologies to identify patterns, learn over time, and optimize workflows Through “supervised” and “unsupervised” learning, algorithms make predictions and provide insights on recognized patterns Algorithmic Business Industrialized use of complex mathematical algorithms to drive improved business decisions or process automation for competitive differentiation How do RPA and IPA differ? RPA directly mimics human behavior IPA learns how to become more efficient ProgramInput Output ProgramInput Learning Output
- 5. Analytics Path: Enterprises are moving from descriptive analytics to cognitive analytics to fully exploit AI, enhance experience and improve margins 5 Describe, summarize and analyze historical data Recommend ‘right’ or optimal actions or decisions Monitor, decide, and act autonomously or semi-autonomously Predict future outcomes based on facts from the past and simulations Descriptive Predictive Prescriptive Cognitive Identify causes of trends and outcomes Diagnostic (What happened?) (Why it happened?) (What could happen?) (What should be done?) (How do we adapt to change?)
- 6. Four ways that AI is used in enterprises: No human in the loopHuman in the loop Hardwired / specific systems Adaptive systems Automated Intelligence 1 Assisted Intelligence 2 Augmented Intelligence 3 Autonomous Intelligence 4 + 6
- 7. Statistics Econometrics Optimization Complexity Theory Computer Science Game Theory FOUNDATION LAYER 7 AI that can act… ▪ Robotic process automation ▪ Deep question & answering ▪ Machine translation ▪ Collaborative systems ▪ Adaptive systems AI that can sense… ▪ Natural language ▪ Audio & speech ▪ Machine vision ▪ Navigation ▪ Visualization AI is defined as the theory and development of systems that sense the environment, make decisions, and act that would normally require human intelligence. Hear See Speak Feel AI that can think… ▪ Knowledge & representation ▪ Planning & scheduling ▪ Reasoning ▪ Machine Learning ▪ Deep Learning Physical Creative Cognitive Reactive Understand Perceive Plan Assist
- 8. AI will contribute to substantial gains in productivity and consumption. 8 Are you ready to exploit the opportunities from AI & overcome the challenges? Global GDP Impact of AI through 2030 GlobalGDPupliftduetoAI ($intrillions) 2030 IMPACT: $15.7T Consumption Contribution: 60% Source: PwC Global Artificial Intelligence Study, 2017-2018 Productivity Contribution: 40%
- 9. Operations & DevelopmentOutbound Logistics Insurers carry out a huge number of activities and make countless decisions across the value chain that are being optimized or disrupted by AI 9 Product Development Service & Support Underwriting & Operations Sales & Distribution Customers & Marketing Strategy & Growth Risk, Finance, Capital Claims Inbound Logistics How do we efficiently manage our capital and get better ROE? Director, Finance How can we engage with our customers to enhance their experience? Director, Marketing How can we grow our market share and which markets to enter, exit or expand? Director, Strategy How do we innovate and introduce new products and services? Director, Products How do we increase customer satisfaction and retain more customers? Director, Service How can we reach more customers and price our products to increase sales? Director, Sales How can we streamline underwriting and enhance efficiencies of our operations? Director, Underwriting, Operations How can we balance losses, claims experience and claims costs? Director, Claims • Market Share • Customer Experience • Acquisition Rate • Innovation Rate • Operational Efficiency • Customer Satisfaction • Expense Ratio • Claims Ratio Over 300+ AI Use Cases Across 8 Sectors – Sizing the Prize
- 10. 10 Robo-advisor for financial wellness What’s the situation? RIIA wanted to highlight the household balance sheet and new ways of planning for retirement income, but was stuck with traditional portfolio optimization models What we did… We built consumer and household level ‘digital twins’ using synthetic datasets and agent-based simulation What were the benefits? Gamification of Strategy resulted in the development of a digital advisor that simulates household level (128 million) financial data into the future to enhance financial wellness Customer Experience
- 11. Modeling a futuristic robo- taxi ridesharing fleet. 11 TEMPLATE B SAMPLE “Autonomous vehicles are among the client’s highest priorities. The modeling has had a significant impact on how they think and plan.” ANAND RAO PwC Artificial Intelligence Leader What’s the situation? GM recognized the need to invest in alternative transportation services, but needed to understand what services could be profitable and how they should be operated. What we did… We built a dynamic simulation to identify drivers of adoption and optimal operational structures. What were the benefits? We performed a scenario analysis with more than 200,000 go-to-market scenarios to identify target markets, as well as launch and operations strategies. Designing New Markets
- 12. PwC 12 Balance the opportunities with the significant risks that need to be assessed, mitigated and managed Control • Risk of AI going ‘rogue’ (e.g., Tay Chatbot) • Inability to control malevolent AI • Swarm drones Security • Cyber intrusion risks • Privacy risks • Open source software risks • Digital, Physical, Political security Societal • Risk of Autonomous Weapons proliferation • Risk of ‘intelligence divide’ Ethical • ‘Lack of Values’ risk • Value Alignment risk • Goal Alignment risk Economic • Job displacement risks • ‘Winner-takes-all’ concentration of power risk • Liability risk Performance • Risk of Errors • Risk of Bias • Risk of Opaqueness • Risk of stability of performance • Lack of feedback process Risk Robust & Safe AI Beneficial AI Responsible AI
- 13. Six success factors to help you derive maximum benefits from AI. Start from business decisions No.1 Demonstrate value through pilots before scaling No.2 Blend intuition and data-driven insights No.3 Address ‘big data’ – don’t forget ‘lean’ data No.4 Fail forward – test and learn culture No.5 Focus on Responsible AI from the start No.6 13
- 14. PwC’s Digital Services Automation, Analytics, and Artificial Intelligence are rewiring how we work, think, and live. Whether you lead or follow, start exploring. 14
Editor's Notes
- What’s the situation? Adjusting to shifts in consumer preferences, a large auto manufacturer determined the need to invest in autonomous ridesharing fleets. They needed help defining how many vehicles were needed, where key infrastructure such as charging and parking should be installed, what rules should govern the system, and how customer demand would impact system efficiency. What we did… We used simulation modeling to craft a synthetic trip list to understand where customers travel and when. We created a simple interface and through risk-free testing experimented with control variables, such as parking garages and charging stations, displayed on a map. How did it do? The dynamic model was used to perform a scenario analysis with more than 200,000 go-to-market scenarios to determine optimal levels of infrastructure and vehicles and identify key economic drivers. The solution is flexible enough to perform analysis of vehicles in new markets or with new variables.