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Deploying AI Applications in Enterprises

Deploying AI Applications in Enterprises: Challenges and Lessons Learnt discusses the growing use of AI and machine learning technologies in enterprises. It outlines some of the challenges of implementing AI applications, including not knowing where to start, overestimating short term benefits and underestimating long term impacts, and having too much or not enough relevant data. The document then provides examples of AI projects at PwC, focusing on areas like image analysis, speech recognition, and predictive maintenance. It concludes with recommendations for a successful AI strategy, including aligning with business objectives and demonstrating value through pilots before scaling.

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Enterprise AI•AI Applications Overview•
Deploying AI Applications in Enterprises: Challenges and Lessons Learnt AI Expo - Berlin Dr. Anand S. Rao – Global AI Lead, PwC Data& Analytics www.pwc.com/analytics June, 2017
PwC Artificial Intelligence – AI Expo 2 1 2 3 4 Future is Now Challenges Case Studies Next Steps Today’s discussion
PwC Artificial Intelligence – AI Expo 3Artificial Intelligence
PwC Artificial Intelligence – AI Expo 4 The growing sophistication of AI and machine learning technologies are transforming our lives – both as a consumer and as an enterprise ACT: Artificial Intelligence is equaling or surpassing humans in a number of other tasks – playing games, driving cars, recommendations (movies, books, finance, research) etc. THINK: AI is helping us do tasks faster, better and cheaper – Automated Intelligence; helping us make better decisions – Augmented Intelligence, or even taking over what we do – Autonomous Intelligence SENSE: Artificial Intelligence is becoming ubiquitous intelligence with the ability to see, hear, speak, smell, feel, understand gestures, interface with your brain, and dream
PwC AI covers a wide range of capabilities that spans the foundational, cognitive, system, and sensory layers 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 SYSTEM LAYER Statistics Econometrics Optimization Complexity Theory Computer Science Game Theory FOUNDATION LAYER What is Artificial Intelligence? Artificial Intelligence is defined as the theory and development of computer systems that perform tasks that normally require human intelligence such as speech recognition, decision making, visual perception, learning etc.. AI Areas (Not Exhaustive)
PwC Artificial Intelligence – AI Expo 6 1 2 3 4 Future is Now Challenges Case Studies Next Steps
PwC Challenges facing enterprises in implementing AI Too many starting points Not knowing where to start 01 Overestimating the short-term Underestimating the long term02 Too much data Not enough relevant data 03
PwC Automation Augmentation
PwC More than 1,600+ AI start-ups & still growing
PwC Alexa, where is Berlin Alexa, What was the answer to my previous question Berlin is the capital of Germany Sorry, I don’t know that
PwC 83% - Chevrolet SS 12% - Chevrolet Impala 5% - Chevrolet Cruze 1 Took less than 3 months for 1 FTE to build a DeepStyle model for cars based on GoogLeNet
PwC From Segmentation.network Microsoft’s COCO Image recognition Project • Used 328,000 photos from Flickr • Used Amazon’s Mechanical Turk to have humans identify each object, label them and draw a boundary • Total of 70,000 human hours to prepare the dataset
PwC Artificial Intelligence – AI Expo 13 1 2 3 4 Future is Now Challenges Case Studies Next Steps
PwC Artificial Intelligence – AI Expo PwC has worked with a number of clients across all sectors to solve challenging problems for them using AI Example Recent PwC AI Projects 1 Aerial Image Analytics Applies deep learning to images of property (e.g. cars or buildings) to automate risk and loss assessment 2 Smart Coolers Uses a connected network of sensors to derive valuable insights about their surroundings and activities of those within it 3 NLP for Cancer Research Uses natural language processing and question-answering systems to improve quality of patient care 4 Personal Mobility 5 Speech Recognition for Sales Applies a suite of deep learning algorithms to convert call center data into text, which can then be analyzed using NLP techniques 6 Financial Advisor Support Uses agent-based modeling to simulate household financial security under a range of scenarios to provide advice 7 Predictive Maintenance Detection and prediction of machine failures and tool fractures using statistical and machine learning techniques Uses agent-based modeling to run over 200k strategies to create a business unit focused on ride share and driverless cars
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PwC Artificial Intelligence – AI Expo Ongoing monitoring of construction activity using drone video analysis and deep learning Project: Capture and assess drone images from construction environment The Path to Value... OriginalCroppedImageModelOutput Extend image analysis to object detection and semantic segmentation Increase number of drone runs to collect image time-series Track volume of high value materials usage Reduce risks and enhance return on capital investment projects Add additional data types (e.g., thermal, etc.) Key: Background Trees Asphalt ConcreteCars Reinforcement 18 1
PwC Artificial Intelligence – AI Expo 19https://pwc.mediaspace.kaltura.com/media/entryId/1_cra40b7z
PwC Artificial Intelligence – AI Expo Improving sales force effectiveness using audio deep learning Project: Speech-to-text and Natural Language Understanding The Path to Value... Use deep learning algorithms to understand emotion and content of voice calls Combine audio learning with natural language processing & other structured data Improve customer satisfaction, increase cross-sales, and sales practices Fundamentally redesign customer sales and customer service functions Integrate with call center and customer relationship management systems 20 Angry/Aggressive Calming/Soothing 4
PwC Artificial Intelligence – AI Expo Strategic analysis of ‘Personal Mobility’ and Autonomous Vehicle Adoption using collaborative agents and simulation Project: Simulate adoption of personal mobility solutions The Path to Value... Simulate a million ‘consumer’ agents and their purchase choices based on causal reasoning Run over 200K go-to- market scenarios to prescribe the right city, pricing, and # of vehicles Personal mobility as a service disrupting the transportation sector Incorporate real-time sensor data from city and vehicles Vehicle Fleets (Driverless, Electric, Sharing) Simulating demand, charging and utilization by geography Modeling demand for vehicle miles travelled 21 5
PwC Smart Manufacturing Analytics Platform to enhance the manufacturing process 22 Technology Data Analytics Predictive Maintenance Predict tool defects and machine downtimes before they happen to take proactive maintenance actions. Real-time Monitoring Process production sensor data in real-time from machines to the global reliability portal. Improved Product Quality Increase product quality and reduce waste and costs by analyzing sensor data in the production process. Quality Rating Platform Standardize, simplify and automate the regular reporting process of supplier quality data. Location-Based Solutions Record, analyze and visualize position and movements of any resources in real time to improve shop floor and logistics efficiency Cloud Platform & Services Big Data Services Visualization Tools Collaboration & Workflows Risk factors Downtimes, bottlenecks, quality etc. Plant and supplier KPIs OEE, on-time delivery, output volume, etc. Production data Utilization, quality, setup time, etc. Sensor data Temperature, vibration, pressure, etc. Statistical Modeling Machine Learning Simulation Optimization Global Information and Reliability Portal Get a 360°view over all plants, KPI’s, maintenance alerts and real-time production data. 7
PwC Manufacturing Analytics Solutions Friedrichshafen AG – Machine Learning & Predictive Maintenance Very good, valuable, and relevant insights have been gained using Machine Learning – many of the findings are new and unexpected and give concrete leads for further investigation. Dr. Bernhard Rank Senior Manager Gear Quality ZF Friedrichshafen AG “ Microsoft Azure cloud services Improved production efficiency. Agile process model. Client Challenge • Today, the client is a quality leader in the production of gearboxes and wants to maintain this position by exploring and implementing for promising machine learning approaches to increase production quality and efficiency. • The production process includes complex tasks and different types of machines. Overall Equipment Effectiveness (OEE) is affected by unexpected tool fracture and undue maintenance tasks. • Sensor data from the production process is available, but current analytic capabilities limit the usage of large datasets, resulting in less insights. Statistical methods & ML techniques. Project Outcome • Deep insights concerning machine failures and tool fracture have been gained and a detection and prediction model have been created. • The detection algorithm is able to detect 99% of all tool fractures in real-time. • Boundary values for sensors can be adjusted dynamically, making time consuming manual updates unnecessary. 23 7
PwC Artificial Intelligence – AI Expo Five key success factors to derive maximum benefits from Artificial Intelligence, big data & analytics Start from business decisions Demonstrate value through pilots before scaling Fail forward – test and learn culture Address ‘big data’ – don’t forget ‘lean’ data Blend intuition and data- driven insights
PwC Artificial Intelligence – AI Expo 25 1 2 3 4 Future is Now Challenges Case Studies Next Steps
PwC Artificial Intelligence – AI Expo Four key areas of Artificial Intelligence have matured and are being increasingly adopted by vendors and enterprises 26 • Supervised machine learning • Unsupervised machine learning • Convolutional neural nets, LSTM • Generative Adversarial Networks • Deep learning for text, voice, images, video • Robotic process automation • Cognitive/Intelligent process automation • Process mining and learning • Chatbots • Virtual assistants Artificial Intelligence • Information retrieval • Question & Answering systems • Language generation • Speech-to-text and text-to-speech • Machine translation • Agent-based modelling • Social simulation • Reinforcement learning • Management cockpits • Collaborative systems
PwC Artificial Intelligence – AI Expo Four Steps to establishing AI within your Enterprise 27 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 Business-Aligned AI Strategy AI capability model Institutionalizing AI AI Governance & Change Step 1 Step 2 Step 3 Step 4
PwC Artificial Intelligence – AI Expo https://pwc.mediaspace.kaltura.com/media/t/1_yee8khtl
For more information: Anand S. Rao (anand.s.rao@pwc.com) Twitter: @AnandSRao © 2017 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.. Images from Google Deep Dream