Skip to main content
UploadLanguage (EN)Support
BusinessMobileSocial MediaMarketingTechnologyArt & PhotosCareerDesignEducationPresentations & Public SpeakingGovernment & NonprofitHealthcareInternetLawLeadership & ManagementAutomotiveEngineeringSoftwareRecruiting & HRRetailSalesServicesScienceSmall Business & EntrepreneurshipFoodEnvironmentEconomy & FinanceData & AnalyticsInvestor RelationsSportsSpiritualNews & PoliticsTravelSelf ImprovementReal EstateEntertainment & HumorHealth & MedicineDevices & HardwareLifestyle
Uploaded byAnandSRao1962
PPTX, PDF880 views

Advanced AI Applications In Enterprises

This document discusses advanced applications of AI in enterprises. It begins with an overview of AI and its abilities in sensing, thinking and acting. It then discusses four lenses for viewing enterprise AI: the business lens focusing on metrics and value chains, the intelligence lens on automated to autonomous intelligence, the data lens on structured vs. unstructured data, and the technology lens on techniques and tools. Several case studies are presented on using AI in pharmaceuticals, construction, automotive and financial advising. The document concludes with building and evolving an enterprise AI capability through developing an AI strategy and roadmap.

Business◦
Related topics:
Enterprise AI•
PwC AI Lab | 1 Advanced Applications of AI in Enterprises Global Big Data Conference – Santa Clara (Jan 17-19 2018) Dr. Anand S. Rao Global Artificial Intelligence Lead
PwC AI Lab | 2 Today’s discussion Enterprise AI Through Four Lenses Enterprise AI Case Studies Building & Evolving your AI Capability 01 02 03
PwC AI Lab | 3 01 Enterprise AI Through Four Lenses
PwC AI Lab | 4 AI as Sense-Think-Act Sense Artificial Intelligence is becoming ubiquitous intelligence with the ability to see, hear, speak, smell, feel, understand gestures, interface with your brain, and dream Think AI is helping us do tasks faster, better and cheaper – Automated Intelligence; helping us make better decisions – Assisted & Augmented Intelligence, or even taking over what we do – Autonomous Intelligence Act Artificial Intelligence is equaling or surpassing humans in a number of other tasks – playing games, driving cars, recommendations (movies, books, finance, research), etc.
PwC AI Lab | 5 Statistics Econometrics Optimization Complexity Theory Computer Science Game Theory FOUNDATION LAYER Sense Think Act • Robotic process automation • Deep question & answering • Machine translation • Collaborative systems • Adaptive systems • Knowledge & representation • Planning & scheduling • Reasoning • Machine Learning • Deep Learning • Natural language • Audio & speech • Machine vision • Navigation • Visualization AI that can sense… AI that can think… AI that can act… Hear See Speak Feel Understand Perceive PlanAssist Physical Creative Cognitive Reactive More Formally…
PwC AI Lab | 6 Business Lens Metrics & Value Chain Intelligence Lens Automated, Assisted, Augmented & Autonomous Data Lens Structured vs Unstructured Available vs Augmented Technology Lens Techniques, Tools & Platforms Four Lenses of Artificial Intelligence
PwC AI Lab | 7 Business Lens: Metrics & Value Chain Operations & Development Product Development Service & Support Operations Outbound Logistics Sales & Distribution Customers & Marketing Strategy & Growth Supply Chain & Procurement Finance, HR, Planning Inbound Logistics How will we ensure our product supply is meeting demand? VP, Supply Chain 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 increase efficiency and effectiveness of our operations? Director, Operations How can we get a better return on our talent, capital, and assets? Director, Finance & HR • Market Share • Customer Experience • Acquisition Rate • Innovation Rate • Operational Efficiency • Customer Satisfaction • Talent Retention • Inventory Turn Over 300+ AI Use Cases Across 8 Sectors – Sizing the Prize
PwC AI Lab | 8 Intelligence Lens: Four Types of Enterprise AI 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 +
PwC AI Lab | 9 Data Lens: Four Types of Data Structured AvailableAugmented Unstructured
PwC AI Lab | 10 What is Artificial Intelligence? Artificial Intelligence can be defined as the theory and development of systems that can continuously sense its environment, think, make decisions, and take actions that influence the environment to achieve its goals. Technology Lens: AI Techniques Machine Vision Natural Language Audio & Speech Navigation Visualization SENSORY LAYER Knowledge Representation Reasoning Planning & Scheduling Machine Learning Deep Learning COGNITIVE LAYER Robotic Process Automation Deep Question & Answering Machine Translation Collaborative Systems Adaptive Systems BEHAVIORAL LAYER Statistics Econometrics Optimization Complexity Theory Computer Science Game Theory FOUNDATIONAL LAYER
PwC AI Lab | 11 02 Enterprise AI Case Studies
PwC AI Lab | 12 Case 1: Global Pharmaceutical Case 2: Construction Company Case 3: Automotive Manufacturer Case 4: Digital Advisor
PwC AI Lab | 13 Global Pharmaceuticals Extracting adverse drug interaction from clinician notes, social media, and medical literature to enhance productivity and effectiveness (96% accuracy)
PwC AI Lab | 14 Adverse Event Pipeline using NLP Toolkit
PwC AI Lab | 15 Named Entity Recognition using NLP Example Case Text: Entity Type 0 Muscle Location 1 Soreness Event 2 Thigh Location Entity Type 0 Diarrhea Event NLP Engine 2) Patient suffered from diarrhea once or twice this month and worried the symptom resulted from injecting Forteo. 1) Patient felt muscle soreness around the injection site of his thigh. Key Information Output
PwC AI Lab | 16 Deep Learning of Latent Relationships Word2Vec is able to show the relationship between Sneezing and Anti- histamine.
PwC AI Lab | 17 AI in Healthcare
PwC AI Lab | 18 Construction Company Monitoring of construction site completion status and asset tracking using deep learning decreased operational costs by 60%
PwC AI Lab | 19 Deep Learning from Video Images
PwC AI Lab | 20 Deep Learning with Drones PwC – Drones and AI
PwC AI Lab | 21 Global Auto Manufacturer Gamification of Strategy resulted in the Auto Manufacturer evaluating over 200,000 go-to-market scenarios to launch rideshare and autonomous vehicle multi- billion dollar business unit Won the Best Enterprise AI Alconics Award at AI Summit
PwC AI Lab | 22 Deep Causal Reasoning with Millions of Digital Twins Transport Choice Car Share Other Commute Service 1 Service 2 Service n P(Car Sharing) P(Alternatives) Car Share Characteristic Evaluation 2-Way ( Zip Car) Free float (Car2Go) Peer to Peer (Relay Rides) Service Offering Types Errand Weekend Digital Twins of Consumers, model purchase choices based on socio- demographics, transport choice, city topology, and economics
PwC AI Lab | 23 Synthetic Trip Data to Augment Commute Behavior
PwC AI Lab | 24 Over 200,000 Go-to-Market Scenarios Simulated ~6000 Total Simulations Select Cities Strategies Random Seeds Market Conditions 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 To account for randomness experienced in dynamic systems, each strategy for each city was conducted 10 times The model was calibrated to different market conditions of consumer acceptance
PwC AI Lab | 25 Business Lens Disruptive Business Model “Selling Cars” to “Mobility as a Service” Intelligence Lens Augmented Intelligence Continuous Learning Data Lens Structured, Unstructured and Augmented Data Technology Lens Agent-Based Model & Reinforcement Learning Four Lenses applied to the Case Study
PwC AI Lab | 26 Digital Advisor 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 PwC AI Lab | 26
PwC AI Lab | 27 03 Building & Evolving Your AI Capability
PwC AI Lab | 28 Benefiting from AI requires separating myths from facts Myth 1: Artificial Intelligence is a distinct monolithic area of study Fact 1: Artificial Intelligence is an interdisciplinary area with many distinct sub-fields Myth 2: All types of problems can be solved by a single AI solution (e.g., ….insert your favorite solution) Fact 2: Different types of problems require different type of AI techniques and solutions to be used Myth 3: Machine Learning automatically (magically) learns from data without any human intervention Fact 3: Machine Learning requires a laborious process of acquiring and cleansing large amounts of data, selecting, training, and guiding the algorithm Searching, Querying & Conversing Describing, Classifying, Understanding & Visualizing Diagnosing, Discovering & Reasoning Trending, Forecasting, Projecting & Predicting Simulating, Learning, Optimizing, & Adapting Recognizing, Sensing, and Recommending AI Uses
PwC AI Lab | 29 Artificial Intelligence • Robotic process automation • Cognitive/Intelligent process automation • Process mining and learning • Chatbots • Virtual assistants • Information retrieval • Question & Answering systems • Language generation • Speech-to-text and text-to-speech • Machine translation • Agent-based modelling • Social simulation/ Digital Twins • Reinforcement learning • Management cockpits • Collaborative systems • Supervised machine learning • Unsupervised machine learning • Convolutional neural nets, LSTM • Generative Adversarial Networks • Deep learning for text, voice, images, video Adaptive Roadmap Areas of focus for Enterprise AI
PwC AI Lab | 30 Adaptive Roadmap Getting started with Enterprise AI Business-Aligned AI Strategy AI capability model Institutionalizing AI AI Governance & Change Step 1 Step 2 Step 3 Step 4 Step 1: Develop an AI Strategy that is aligned with your overall business vision and objectives. Step 2: Develop an AI capability across the enterprise in the relevant assisted, augmented and autonomous intelligence. Step 3: Institutionalize the portfolio of successful AI capabilities by embedding analytics in core processes, adopting cloud and open– source platforms Step 4: Ensure appropriate governance of all forms of intelligence by educating and changing customer and employee perception of AI
PwC AI Lab | 31 PwC’s Digital Services Six success factors to derive maximum benefits from artificial intelligence Start from business decisions 01 Demonstrate value through pilots before scaling 02 Blend intuition and data-driven insights 03 Address ‘big data’ – don’t forget ‘lean’ data 04 Fail forward – test and learn culture 05 Focus on Responsible AI from the start 06
PwC AI Lab | 32 Augmented Intelligence
PwC AI Lab | 33 PwC’s Digital Services Thank you. © 2018 PwC. All rights reserved. Not for further distribution without the permission of PwC. “PwC” refers to the network of member firms of PricewaterhouseCoopers International Limited (PwCIL), or, as the context requires, individual member firms of the PwC network. Each member firm is a separate legal entity and does not act as agent of PwCIL or any other member firm. PwCIL does not provide any services to clients. PwCIL is not responsible or liable for the acts or omissions of any of its member firms nor can it control the exercise of their professional judgment or bind them in any way. No member firm is responsible or liable for the acts or omissions of any other member firm nor can it control the exercise of another member firm’s professional judgment or bind another member firm or PwCIL in any way. Dr. Anand S. Rao PwC Global Artificial Intelligence Lead anand.s.rao@pwc.com @AnandSRao