Artificial Intelligence - the next generation of automation

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1. Bimal Tripathi Oct 2017 Bimal Tripathi Sr Director Brocade Communications Trends and Opportunities In Information Technology 2. Bimal Tripathi Oct 2017 Enterprises Artificial Intelligence TOPICS Enterprise adoption Demystifying AI Business Case for Enterprise AI Strategy Enterprise AI Roadmap Ethics 3. Bimal Tripathi Oct 2017 Enterprise adoption Enterprises do not deal with smart cars, natural language processing, Computer vision and photo tagging but would benefit from AI by using cumulative enterprise digital experience to Grow revenue Improve products Enhance Customer Engagement Optimize operating efficiencies 4. Bimal Tripathi Oct 2017 2 Demystifying AI - How it works? Computer learns from data 1 3 Recognizes patterns in the historic data higher dimensions and larger data volumes Builds models (statistical rules ) to predict Applied the rules on new data to make decisions 5. Bimal Tripathi Oct 2017 Historic Data Pattern Rule 48.5 + 0.05 * Revenue Example 1: Supervised Learning Regression What is the right deal size for a customer given Revenue $6,040 M? 6. Bimal Tripathi Oct 2017 Will App crash? CPU Utilization : 70% Memory Utilization : 85% Historic Data Pattern Rule A decision tree Example 2: Supervised Learning Decision Tree Predict Service disruption - is the service availability at risk? 7. Bimal Tripathi Oct 2017 Historic Data Rule a neural network Example 3: Supervised Deep Learning (Neural Networks) Lead quality prediction what leads should internal sales reps work on? 8. Bimal Tripathi Oct 2017 Personalized offers and messaging Example 4: Unsupervised Learning Customer Segmentation group look alike customer for the next campaign CUSTOMER Data CLUSTERED DATA 9. Bimal Tripathi Oct 2017 Web Traffic Response time Example 5: Unsupervised Learning Anomaly detection Real or False Alarm? 10. Bimal Tripathi Oct 2017 Business case for Enterprise AI Strategy Last 3 decades of business innovation ERP -> Internet -> Analytics -> Virtualization -> Cloud -> Mobile Is AI the next frontier? Strategic Goals Grow revenue Enhance Customer Engagement Improve products Optimize operating efficiencies Execution Success Metrics Process Owners Governance Infrastructure Data Science Lab Data Architecture At scale Computing HDFS/SPARK/PYTHON 11. Bimal Tripathi Oct 2017 Sales/CRM Product Operations Marketing & eCommerce Information Technology People Operations Enterprise Artificial Intelligence Use case for Enterprise functions Bot and assistants Opportunity ranking Pricing Customer churn Support readiness Sentiment Product Failure Inventory Mgmt Forecasting Anomaly detection Lead scoring Recommendations Promotions Segmentation Dynamic pricing Buyer intent Cyber Security Help Desk automation Data Quality System availability Recruiting Engagement Attrition Data sources: In-house applications ERP, Web, CRM, products Data Warehouse/Data Lake/Big Data Clusters 3rd party data providers 12. Bimal Tripathi Oct 2017 2016 BROCADE COMMUNICATIONS SYSTEMS, INC. Brocade Confidential. 12 PERFORMANCE DATA COLLECTED CALL HOME DATA LOGS SW AGENTS PRODUCT DISRUPTION EVENTS RETURNS SERVICE REQUESTS AGGREGATE, CLEAN ENRICH TO FORM SUPERVISED LEARNING TRAINING DATA BUILD/TRAIN MACHINE LEARNING MODEL PRODUCTS IN FIELD SERVICES IN CLOUD ALERT CUSTOMER TAKE CORRECTIVE ACTION BEFORE CUSTOMER CALLS Solve your customers problems before they call you Predict product failures and Service disruptions using Supervised Learning COLLECT NEW PERFORMANCE DATA 1 2 3 4 5 PREDICT PREDUCT DISRUPTION NEW DATA 6 7 8 4 13. Bimal Tripathi Oct 2017 PREPARED FOR AN ENTERPRISE AI STRATEGY? 1- Align and Plan 2- POC & Simulation 3- Design & Build 4- Track & Monitor Establish ML goal Build domain knowledge and engage SME Build hypotheses and experiment Determine the sources of data Define success criteria and performance metrics Find strongest predictors of event Select model and run train/test validation Collect and enrich data for model building Iterate and pick best model Design deployment architecture for scale Select Machine Learning Technologies Identify data sources and needed process changes Deploy new data taps Institute systemic data Quality validations Deploy for production usage Monitor Prediction accuracy & adjust Model parameters Study Performance Trends Root cause and correct performance detractors Assess cycle time to results throughput SLAs Conduct capacity review and technology upgrades Sample tools and technology: Dashboards Visualization Tools Monitoring tools Sample tools and technology: Database, Excel R, Python, SAS, RapidMiner, TensorFlow, H2O, Spark Mlib, Amazon AML Sample tools and technology: Dashboards Visualization tools Roadmap to implementing Enterprise Machine Learning Sample tools and technology: Data repository (RDBMS, HDFS) Python, Java, Spark, R Infrastructure: AWS, Google, SFDC, Oracle, SAP, IBM, MSFT 14. Bimal Tripathi Oct 2017 Ethics of Artificial Intelligence Bots versus humans Social Economics Employee privacy Customer profiling 15. Bimal Tripathi Oct 2017


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