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The proof of AI is in the value created

The challenge of Data Science initiatives is to turn insights into value. However many initiatives never make it past the stage of analysis, ultimately only creating frustration for all involved stakeholders. Pierre and Koen explore what you can do to avoid this pitfall when you start up your next data science initiative.

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The proof of AI is in the value created

  1. 1. The proof of AI is the value created Pierre Marchand & Koen Wildemeersch Data Strategist & I&D Leader @ Capgemini
  2. 2. Apply AI to augment human capabilities and improve human performance, man-machine collaboration, Business results OUR MISSION AI … an umbrella term Oxford Dictionary: “Artificial Intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.”
  3. 3. Discover new ways of working Innovate at speed Enhance productivity at a lower cost Improve quality and predictability Decrease time-to-market Increase efficiency, agility, responsiveness and resilience WHERE ?
  4. 4. WHAT ? Service Orchestration Blockchain </> Algorithms Process Automation Neural Networks Robotic Process Automation Big Knowledge Chat Bots Internet of Things Big Data Closed Circuit TV Natural Language Processing Machine Learning Cognitive Computing
  5. 5. WHICH CASE ? Source : Turning AI into concrete value, Capgemini Digital Transformation Institute
  6. 6. What happened ? DESCRIPTIVE Why did it happen ? DIAGNOSTIC What will happen ? PREDICTIVE How to make it happen ? PRESCRIPTIVE USE CASES
  7. 7. What happened ? DESCRIPTIVE Why did it happen ? DIAGNOSTIC What will happen ? PREDICTIVE How to make it happen ? PRESCRIPTIVE
  8. 8. Stock synchronization issue One of Europe’s Largest fashion Retailers Business Challenges • Stock measured in the store differs significantly from what is in the Transaction Log. • Unknown where the root cause was: • Distribution centers? • In the Store? • Elsewhere? • As a consequence products were not in the store resulting in: • A loss in sales • Unhappy customers • Frustrated Staff What we did • Loaded transactions for a larger number of stores • Applied data mining to identify those transactions that were odd and link those • Analyzing the missed revenue to build a business case to change the processes that were not functioning well Result • Identified the issue – which was affecting all the stores in the Point of Sales Network. Correcting up to 17%. • Build business case based upon out-of-shelf/missed revenue • Fixed the processes at in the bookkeeping and clothing labelling • Also: Predicted when stock would reach ‘0’ in time
  9. 9. What happened ? DESCRIPTIVE Why did it happen ? DIAGNOSTIC What will happen ? PREDICTIVE How to make it happen ? PRESCRIPTIVE
  10. 10. Pattern recognition for repair of money transfer instructions European Settlement Services • Hundreds of thousands of money transfer instructions coming in daily • Thousands of instructions need repair before execution • Repair is ~50% automatic • Duplicates galore • 200+ variables • Structured case converted into unstructured case • Features from text mining results • Predictive model to identify probability of validation • 32% improvement in instruction automatic repair • 100% duplicates identified • Potential to shift 2 highly experienced FTEs to complex high value cases • New patterns put in production directly Business Challenges What we did Result
  11. 11. What happened ? DESCRIPTIVE Why did it happen ? DIAGNOSTIC What will happen ? PREDICTIVE How to make it happen ? PRESCRIPTIVE
  12. 12. Multinational Pharmaceutical Company Business Challenges • Improve sales forecast • Multinational scale • Very short time series for new projects/products • Very large number of time series What we did • Time Series and pattern recognition through clustering methods and Dynamic Time Wrapping • Optimization on algorithms ensemble, not individual ones Result • Improved forecasting quality by 12% • Beat niche competitor by huge margin • Potential to shift manual forecasting team (50) to complex high value cases Predicting sales using Time Series
  13. 13. Automated e-mail classification European Settlement Services Business Challenges • Large number of legal documents incoming daily • Each document has to be routed to correct department for processing • Right-Shore model balancing offshore & onshore • Offshore Data Science team not on client network • Existing 85% automation already excellent What we did • Dedicated VPN connectivity to offshore Data Science • Fine tuned OCR • Common environment across onshore & offshore • Large scale optimization Result • Increased dispatch accuracy from 85 to 93% • Reduce inter-department redirections by 10+% • Potential to shift up to 10 FTEs to complex high value cases
  14. 14. What happened ? DESCRIPTIVE Why did it happen ? DIAGNOSTIC What will happen ? PREDICTIVE How to make it happen ? PRESCRIPTIVE
  15. 15. Insurance data mining for finding contract non-renewals Major Belgium Insurance Company Business Challenges • Major Belgian Insurance Company that noticed that their brokers stopped renewing/upselling • Leading to a loss of revenue What we did • Applying SAS the broker data and Policy data were crossed. • A time series analysis was performed to identify broker behavior through time • Part of a larger project building a greenfield enterprise data warehouse Result • A clear understanding of why brokers stopped upselling/renewing as their commission decreased in time • Clear understanding of what needed to happen to correct, based on data. • Renewed renumeration model • Enabled Analytics of the future
  16. 16. Value Deployments Engaging the customer Boosting operations Generating insights 600+ AI Customer Deployments 350+ Automation & AI task force deployed to engagements 4,500+ Use Cases More than 130 vendor products evaluated 3,000+ Robots deployed 10,000+ Automation & AI Academy Experts 7,000 Automation Experts in RPA, Cognitive Technologies and AI 350+ AI Evangelists AI @ CAPGEMINI GROUP
  17. 17. How we see AI adding value to our customers Source: Capgemini Research Institute, State of AI survey, N=993 companies that are implementing AI, June 2017
  18. 18. OperateDeliverAdvise AI Consulting services: Assessment and Roadmap Definition AI Implementation Services to transform Business Operations Run AI Services: Operate & Maintain Strategy & Roadmap Creation Feasibility Assessment & Business Case Creation Developing Target Operational Model Project Planning & Prioritization Solution Design & Architecture Definition Solution Development Deployment Standardization & Reuse Monitoring & Support Maintenance & Enhancement Change Management Continuous Service Improvements AI FOR YOU E2E
  19. 19. This message contains information that may be privileged or confidential and is the property of the Capgemini Group. Copyright © 2018 Capgemini. All rights reserved. A global leader in consulting, technology services and digital transformation, Capgemini is at the forefront of innovation to address the entire breadth of clients’ opportunities in the evolving world of cloud, digital and platforms. Building on its strong 50-year heritage and deep industry-specific expertise, Capgemini enables organizations to realize their business ambitions through an array of services from strategy to operations. Capgemini is driven by the conviction that the business value of technology comes from and through people. It is a multicultural company of 200,000 team members in over 40 countries. The Group reported 2017 global revenues of EUR 12.8 billion. About Capgemini Learn more about us at