Enterprise Artificial Intelligence strategy
- 1. ENTERPRISE AI STRATEGY By Mukesh Sinha
- 2. Outline AI Industry Facts Organizational question 1 Enterprise Artificial Intelligence Strategy ( EAIS) End to End Implementation Strategy Life Cycle of Data Science 2 End To End Tool Chain Strategy 3 4 5 6
- 3. AI Industry Facts Value of AI is expected to rise $11.7B EQUITY FUNDING 367 EQUITY DEALS 1031 NEW INVESTORS 11 UNICORNS AI Quick Facts $ 10 B $ 40 B 2018 2020 No of companies developing INTELLIGENT TECHNOLOGY 2600 By 2025, the AI market will surpass $90 billion. 78% 68% 70% 77% 75% Increased operational efficiency Reduced False Positives Reduced Operational cost Enhancement in Employee productivity Regulatory compliance at lower cost Organizational Benefits across Operations, Sales and Customer service Potential AI use cases Financial Services Healthcare Retail References Telecom • Trading Strategies , Automated trading • Consumer behavior analysis • Fraud Detection • Reducing revenue churn • Product Recommendation • Forecasting , Tracking customer history • Reducing revenue churn • Forecasting • Managing Risk, Tracking customer history • Detecting small variations based on patients’ health data • Early identification of potential pandemics • Imaging diagnostics
- 4. • What are the potentials of introducing artificial intelligence to our company? • What are the concrete business use cases to implement AI ? • What kind of data that can be used for machine learning approach ? • Do we have strategy and available infrastructure to support E2E AI implementation ? • What impact have on employees, organization, processes and the eco-system? • What kind of skill set and resources required to implement successfull AI projects ? Stakeholder Infrastructure Resources • Which use cases to select for POC and how we can plan to implement POC ? • How much effort is required to realise the outlined use cases ? Feasibilty Assessment Future Roadmap • How can we increase sales and reduce costs ? • How to define the future roadmap of analytics pathways for cost optimization ? Organizational question
- 5. EAIS Customer Alignment • COE based on centralize AI engine to support across the business • AI team consists of Business, Data scientist and IT engineers • Implement AI Driven culture • Create a digitialy empowered workforce • Concrete Action plan to achieve AI goals • Continous Transformation • Identify the potential AI applications for better ROI • Select simple and key business use cases • Embrace cloud platforms and specialized hardware • Build semi structured historical data to perform POC • Invest on hardware system for integrating compute acceleration technologies like FPGAs, ASICs and GPUs • Perform POC for existing data set • Implement visulization to access the data • Leverage cloud based platform to minimize the cost Business case Data Center & Infrastructure Feasibility Assessment COE Roadmap & Next Step Customer Alignment Business case Roadmap & Next Step Center of Excellence Feasibility Assessment • AI readiness , Business problem identification • AI strategy aligned to business strategy 1 6 5 4 3 2 Data center and Infrastructure Enterprise Artificial Intelligence Strategy ( EAIS)
- 6. 1 Data cleaning Feature Engineering Model Engineering Training Data Experiment Testing Data Train Model Model Accuracy Model Evaluation Execution Applying ML Algorithm TestingTuning Deployment Dashboard Performance Reporting Publish web service Feature EngineeringData Lake Deliver Application Device Data Storages 2 3 456 SQL Database Flat Files Social Application Log End to End Implementation Strategy
- 7. Business Data Scientist IT Operation Determine Objective Problem Understanding Define success criteria Access Constraints Assess Data Data Understanding Obtain Data Explore Data Filter & Clean DataData Preparation Feature Engg Select Model Modeling Build Model Validate ModelResult Evaluation Explain Model Deploy Model Deployment Monitor & Maintain 5-10% 10-25% 20-40% 20-30% 5-10% 10-15% Subtasks Proportion EffortTasks Life Cycle of Data Science
- 8. Data Acquisition Data Transformation Model Engineering & Analytics Deployment & Visualitization Data Science & ML Distribution Platforms Data Ingestion Distributed File System Data WareHouse BI Tools App Services NoSQL Databases Tool Chain Strategy for Data Science SQL Database Unstructured Flat Files Social Application Log IOT Devices
- 9. THANK YOU