Chief AI Officer and AI Digital Transformation
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This document discusses drivers of AI and digital transformation and the role of the Chief AI Officer (C.AI.O). It notes that by 2022, 75% of globally shipped working assets will have event-driven decision support systems. It asks what is needed to win in the next wave of value from AI and digital transformation, how to prepare data science teams to win, and how to build a value-driven AI playbook. It suggests the C.AI.O is the corporate role best able to find answers to these questions, as the C.AI.O can drive change, value, and competition through data, legal, and policy exploration to maximize appropriate AI use.
Chief AI Officer and AI Digital Transformation
- 1. AI DigitalTransformationDrivers AndThe Role Of The Chief AI Officer www.ChiefAIOfficer.org Prof. Giuseppe Mascarella, C.AI.O [email protected] FUTURE TECH: BEYOND 2020 SINGAPORE
- 2. 2020 AI Drivers Survey Results Value Amplify Confidential QUESTION:
- 3. AI Evolution Driver: Data Sources Evolution Source: www.microsoft.com
- 4. Data is out there and is free (Open data). It provides no competitive advantages. Finding patterns in data is the holy grail. • Oil in a barrel • Diesel in an engine
- 5. From Data Mining To AI Digital Transformation Value Amplify Confidential
- 6. By 2022, 75% of the working assets shipped globally, will have event driven decision support system Value Amplify Confidential
- 8. What does it take to be the winner inteyh next wave of value? How do I prepare my Data Science teams to be the winners? How do I build a value driven AI Playbook? AI and Digital Transformation is becoming a fierce battlefield for the next wave of value Which Corporate Role Needs To Find The Best Answers To These Questions?
- 9. C.I.O (I=Innovation) C.I.O (I=Information) Increase ROI/TCO of Technology Lowest TCO Per Prediction wit Best Speed and Accuracy Governance of use of IT/Cloud from LOB (Line Of Business) Architectures and New Business Models For the New World of Cognitive Opportunities Quality and Documentation of Company Data Algorithm Driven Data Strategy (Create/Access Open Data) Data Protection and Compliance to Industry Regulations C.AI.O Scientist Training Path to Legally Explore New Policy Changes for Maximum AI Use Value Competition Data Legal Drive Change (Artificial Intelligence)
- 10. Chief AI Officer Problem
- 11. Chief AI Officer Playbook | Part 2 Predictive Maintenance Stage 1: Reactive Stage 2: Informative Stage 3: Predictive Stage 4: Transformative Stage 5: Game Changer OUTCOMES Vision Schedule and manage using past operational and routine performance data Analyze conditions and make informed decisions Discover new insight, and predict likelihood and timeframe of failures Transform the experience with rea-time insight, actions and continuous feedback Shape new business models with digital ecosystem Strategic Intent • Define operational rhythm • Meet SLAs, compliance and warranty conditions • Orchestrate and leverage readily available reports and operational observations • Become purpose-driven with connected, complete, correct and connected data • Model asset-specific plans based on the asset condition • Easy access to insights on the whys and the trends • Manage the Voice of the Asset • Instrument the assets to provide real-time data on factors affecting asset condition • Predict and schedule maintenance for desired operations • Operate Asset as a Service by altering the asset behavior in real-time • Take corrective actions before a potential failure • Predict and perform maintenance based on the business impact • Launch digital services, leveraging design, data and delivery insight • Create new customer experiences and solutions, integrating partner assets • Monetize learning KPIs • Unplanned downtime • Regulatory compliance • Maintenance schedule, time and costs • Time between failures • Spare parts inventory • Annual budget • Asset utilization • Unexpected breakdowns • Capital and resource investment • Global reach • Revenue or throughput per asset • Customer loyalty • Outcome-based pricing • New markets • Cross-selling • Eco-system maturity CAPABILITIES: Data, Intelligence and Actions TECHNOLOGY APPROACH: Architecture Directions Value Amplify Confidential
- 12. Chief AI Officer Playbook | Part 2 Predictive Maintenance Stage 1: Reactive Stage 2: Informative Stage 3: Predictive Stage 4: Transformative Stage 5: Game Changer CAPABILITIES Data (Sources, time, quality, access) • Manufacturers reports • Asset features • Failures/repairs reports • Historical data from operational systems • Intermittent updates • Asset condition data • Correlated quality, ERP, and operational data • Scheduled data queries and data polling • Real-time, streaming data about asset conditions, environmental factors, and operating conditions • Multisite data aggregation • Data readiness for data science • Cognitive and feedback data • Business process / workflow • Organization data e.g. operator’s skills • Events, Smart sensing • Ecosystem data and services • External context (customer, consumer) • Real-time capability and data discovery Intelligence (Interpretations, analytics, insights, learnings) • Web-based reports, dashboards • Data visualization of historical and operational data • Self-service analytics • Asset condition monitoring and assessment • Statistical modeling • Trend analysis and forecasting • Predictions using data mining, modeling and algorithms across all data • Stream analytics • Rolling aggregates, analysis and recommendations • Insight at sensor and interface levels • Deep learning e.g. vibrations • Real-time predictions using current business context and operating conditions • Analyze current state behavior across ecosystem and identify opportunities • Evaluate health of data and algorithms and predict adjustments Actions (New or change in activities) • Inventory assets • Develop plans and schedule maintenance for assets based on past performance • Plan and schedule resources • Forecast and optimize schedule and inventory • Manage critical assets and business operations • Manage planned downtime • Manage resource productivity • Create knowledgebase • Check health while in use • Identify potential causes and time window, and take proactive actions • Generate alerts and propose best actions • Support remotely • Reliability engineering • Self-identify alternate paths for continuous operations • Heal the asset while in use • Create outcome-based business processes and customer experience • Make every interaction a source of revenue • Productize data, intelligence, algorithms, and business processes • Integrate partner services • Create BOTs APPROACH Architecture Directions • Systems of Records • Client/server or distributed architecture • Data marts • Reporting and analytics • Systems of Engagement • Service-oriented architecture • Integration • Data warehouses • Analytical modeling • Systems of Intelligence • Lambda architecture • NoSQL • Data lakes • Cloud • Systems of Learning • Neural network and FOG architecture • Cognitive services • In memory, edge analytics • Systems of Digital Markets • Microservices architecture • APIs
- 13. AIDigitalTransformationDrivers AndTheRoleOf The Chief AI Officer www.ChiefAIOfficer.org Prof. Giuseppe Mascarella, C.AI.O [email protected]