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CWIN17 san francisco-ai implementation-pub

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CWIN17 san francisco-ai implementation-pub

  1. 1. Artificial Intelligence in Business An Architect’s Viewpoint CWIN San Francisco Michael Martin December 7, 2017
  2. 2. Education Michael C Martin Enterprise Architect BS Computer Science Experience • Capgemini • IBM • Apple • GE • Coca-Cola • DCA / Attachmate Fun Facts • Outgrew Height Requirements for Airforce ROTC Scholarship • Started work for IBM at 19 • Been involved with IT since 1980 • Love solving puzzles and putting processes and systems in place that make a difference Projects • Ecommerce (QVC, Macys.com, NBA.com) • Content (DC Comics, Coca-Cola) • Infrastructure (Michael Kors) • Mobile (Quintiles, Cox Communications, Kohls.com, Coca- Cola)
  3. 3. Dimensions of Artificial Intelligence 10. Cognition 8. Deep learning 9. Image analysis 6. Knowledge engineering 7. Natural language generation 5. Machine learning 1. Robotics 2. Sensory perception 3. Speech recognition 4. Natural language processing Artificial Intelligence
  4. 4. Dimensions of Artificial Intelligence 10. Cognition 8. Deep learning 9. Image analysis 6. Knowledge engineering 7. Natural language generation 5. Machine learning 1. Robotics 2. Sensory perception 3. Speech recognition 4. Natural language processing Artificial Intelligence ConverseSee Predict Discover Move Converse
  5. 5. Interesting Intersections Data: • Prior: • Siloed content • Structured Content • Linear processes • Recent: • Data warehouses • Data “Lakes” • More opportunities to collect content • Now: • Unstructured Content • Streaming content • Constantly moving content • Opportunities for cross collaboration
  6. 6. Interesting Intersections Infrastructure: • Prior: • Internal Systems • Siloed Systems • Lack of integration or alignment • Recent: • Integration busses / Web Services • “Master Data Management” • Now: • Robust Cloud deployment tools • On-demand infrastructures • Federated environments Data Infrastructure
  7. 7. Interesting Intersections Tools and Platforms: • Prior: • Intra-system reporting tools • Summary reports • Green bar paper • Recent: • Business Intelligence Systems • Dashboards / KPI’s • Structured Data tooling • Departmental ad-hoc query tools • Now: • “executive ready” BI tools • Unstructured data tools • External Systems data aggregators • API services and contracts Data Infrastructure Tools
  8. 8. Interesting Intersections Data Infrastructure Tools AI Tools and Platforms: • Prior: • Intra-system reporting tools • Summary reports • Green bar paper • Recent: • Business Intelligence Systems • Dashboards / KPI’s • Structured Data tooling • Departmental ad-hoc query tools • Now: • “executive ready” BI tools • Unstructured data tools • External Systems data aggregators • API services and contracts
  9. 9. AI and the Diffusion of Innovation Curve Source: Wikipedia - Rogers Everett - Based on Rogers, E. (1962) Diffusion of innovations. Free Press, London, NY, USA. / https://en.wikipedia.org/wiki/Diffusion_of_innovations
  10. 10. The Five Senses of Artificial Intelligence Source: Capgemini, “The five senses of Artificial Intelligence: Christopher Stancombe”, May 2017
  11. 11. AI and Business Value Effectiveness Model Innovation Model Efficiency Model Expert Model Data Complexity Work Complexity Automate Augment
  12. 12. Examples where AI can assist Legal Research Medical Research Fraud Detection ComplianceCustomer SupportSocial Media Systems Operation Safety and Surveillance Expert Systems Processes where data is looked at in real time over a stream of content, structured data, or non-structured data.
  13. 13. How the AI projects start … Source: http://dilbert.com/strip/2016-06-20 EDIT 2017 12 12 Image removed in consideration of licensing terms. Please refer to the URL below for the referred to image
  14. 14. Thinking through the AI problem What are the bodies of content that I can use to help combine to come up with for analysis? Content Context Sentiment Intent What is the domain that the information relates to? What lens should the information be viewed? Are there external influences to take into account? What is the desired outcome / hypothesis that we can validate by doing analysis?
  15. 15. AI Implementation Roadmap Identify Information Clusters Derive Hypothesis Train Systems on Intent and Sentiment Create Model Apply Model Measure / Evaluate / Adjust Apply outcomes to improved and Redesigned business process flows
  16. 16. AI Reference Architecture – Logical View Big Data Store Data Transformation Analytical Processing Storage and Integration Production Systems Extract Transactions Campaign + Inbound Xactions Customer Product Master Unstructured Data Access Data Transform Learning Scoring Mining Modeling / Inputs Results Store Integration Queries / Analytics Post Processing Systems of Consumption API Contracts Web / Mobile / Dashboards CRM / Customer Experience Curation / Modeling / Queries Business Intelligence
  17. 17. Logical view of AI systems hosted on Amazon Servers
  18. 18. Microsoft Provided Algorithm Cheat Sheet Source : https://docs.microsoft.com/en-us/azure/machine-learning/studio/algorithm-cheat-sheet
  19. 19. Capgemini Smart Analytics™ Platform Rapid prototyping of Cognitive and other AI use cases behind your firewall To enable better social customer service To proactively provide information of prospect to financial advisors To enable predictive intelligence to customer data To provide relevant next best offers To analyze voice of customer in near to real time To enable location based offering Connecting all customer touchpoints https://www.capgemini.com/insights-data/smart-platform-delivering-insights-for-your-business
  20. 20. IBM Watson plugin for Wordpress – Sentiment analysis Source: https://thecustomizewindows.com/2017/06/wordpress-plugin-to-analyze-post-emotion-with-ai-ibm-watson/
  21. 21. Macy's tests artificial intelligence as a way to improve sales Source : http://www.latimes.com/business/la-fi-agenda-macys-20160725-snap-story.html
  22. 22. Many potential tools exist now
  23. 23. Source: https://www.how2shout.com/tools/opensource-tools-for-artificial-intelligence-ai.html Open Source Solution Candidates Caffe – ( http://caffe.berkeleyvision.org/ ) TensorFlow – ( https://www.tensorflow.org/ ) DeepLearning4J – ( http://deeplearning4j.org/ ) H20.ai – ( https://www.h2o.ai/ ) Mlib – ( http://spark.apache.org/mllib/ ) Mahout – ( http://mahout.apache.org/ ) Distributed Machine Learning Toolkit – ( http://www.dmtk.io/ ) NuPIC – ( http://numenta.com/ ) OpenNN – (http://www.opennn.net/ ) Oryx 2 – (http://oryx.io/ ) AT&T Acumos – (https://www.acumos.org/ ) Microsoft CNTK – (https://www.microsoft.com/en-us/cognitive- toolkit/ )
  24. 24. Interesting Directions and Non-Functional Perspectives Distributed Machine Learning Privacy, Ownership, Nexus of Data Federation, Licensing, Partnerships Curation, Ownership Increasing Public Awareness à Regulation? Lifecycle Management, Financial Commitment
  25. 25. Do not underestimate resources/time to manage (find, source, clean, govern) datasets for AI projects AI projects will require a culture change, and sometimes real surprises. Driving the corresponding transformation will require high level of sponsorship and willpower to drive change. Communicate and educate at each step taken during the project. You need to build trust with the machine! The AI era is now! AI is a disruption factor. It is also a learning curve. The sooner you get on it, the better you can manage your own destiny as a business. A successful AI project can only be built with an integrated Business/IT team. The best use cases are problems that sometimes exist since forever, but never found a real answer. … but data & AI techniques are only means to an end, not the end itself! Think Big, Start Small : 80 % of our AI projects show relevant and actionable results in 3 months
  26. 26. Closing thoughts from the Architect PRO: • Potential to realize underused data, provide a competitive edge in your market • Opportunities for those who embrace and help derive business value from new processes CON: • High probability that there will need to be changes in your processes and systems to accommodate new information flows and analysis opportunities. • Some jobs may be affected more than others Watch: Learn: Unless your business is in the business of AI / ML (startup, moonshot program), hang back a little to see how the tools and processes will mature over the next few years. However, you should keep a close eye on this, possibly run some pilot projects to have a short term goal in mind and to see what opportunities there are to integrate this into your systems — this is not a question of if — it’s a question of when this will be commonplace and table stakes.
  27. 27. Capgemini: AI – The successful Implementers Toolkit https://www.capgemini.com/consulting/resources/ai-the-successful-implementers- toolkit/ IBM Watson: Everything You Ever Wanted to Know https://www.fool.com/investing/2017/08/30/ibm-watson-everything-you-ever-wanted- to-know.aspx Resources
  28. 28. 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 2016 global revenues of EUR 12.5 billion. About Capgemini Learn more about us at www.capgemini.com This message contains information that may be privileged or confidential and is the property of the Capgemini Group. Copyright © 2017 Capgemini. All rights reserved. People matter, results count.

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