05_AI%20Strategy.pdf

AI Strategy AI for Innovation and Entrepreneurship Presentation | Date 15th, 2023 | Location 2023 Max Wiedemann, LEONINE Studios Max Wiedemann, born on December 22, 1977, in Munich, is a renowned German film producer and executive, celebrated for producing "The Lives of Others," which won the Os...

AI Strategy AI for Innovation and Entrepreneurship Presentation | Date 15th, 2023 | Location 2023 Max Wiedemann, LEONINE Studios Max Wiedemann, born on December 22, 1977, in Munich, is a renowned German film producer and executive, celebrated for producing "The Lives of Others," which won the Oscar for Best Foreign Language Film in 2007. As a cofounder of Wiedemann & Berg Film and TV, he has played a pivotal role in shaping the German film industry. In addition to his production achievements, Wiedemann is instrumental in business development and production at LEONINE Studios, holding the positions of Chief Business Development Officer and Chief Production Officer. His expertise spans both creative production and strategic business growth in the film and television sectors. ‹#› AI Strategy AI for Innovation and Entrepreneurship Presentation | Date 15th, 2023 | Location 2023 AI Strategy 13th November Module Overview The "AI Strategy" module is designed to provide students with a comprehensive understanding of the strategic implementation of AI within organizations. This session will delve into the challenges of applying AI, the varying levels of AI maturity, and the defining characteristics of an AI-First company. Students will learn how to formulate a visionary AI strategy and explore the various factors that enable successful AI integration within an organization. This module is a blend of strategic insights and practical knowledge aimed at leveraging AI to drive innovation and streamline operations within organizational settings. Why Attend This Session This session is invaluable for those who aspire to leverage AI to enhance organizational operations and lead innovation. It offers insights into the formulation of effective AI strategies and the realization of an AI-First approach within organizations. Attendees will gain a deeper understanding of AI maturity levels and the challenges inherent in AI application, equipping them with the knowledge to navigate and implement AI strategically. Whether you are a budding strategist, a business leader, or someone interested in the strategic dimensions of AI, this module will enrich your perspective and empower you to harness AI's transformative potential effectively. ‹#› AI Strategy Learning Objectives 1. Understanding of AI Application Challenges: Gain insights into the challenges associated with applying AI, enhancing the ability to navigate and address hurdles in AI implementation. 5. Exploration of AI Enabling Factors: Delve into the various enabling factors for AI within an organization, gaining knowledge to facilitate successful AI integration and innovation. . 2. Knowledge of AI Maturity Levels: Explore the concept of AI maturity levels, acquiring a nuanced understanding of the developmental stages of AI integration within organizations. 3. Insights into AI-First Companies: Learn what it means to be an AI-First company, developing a comprehensive understanding of the characteristics and approaches defining such organizations. 4. Formulation of Visionary AI Strategy: Understand how to set up a vision for AI, acquiring the skills to formulate impactful and forwardthinking AI strategies. ‹#› Agenda 1 Challenges of Applying AI 4 AI Vision 2 Zero Operational Cost 5 Enabling Factor 3 AI First ‹#› Challenges of Applying AI ‹#› ‹#› Brinker, S. (2016, November). Martec's Law: the greatest management challenge of the 21st century. Chief Martec. https://chiefmartec.com/2016/11/martecslaw-great-management-challenge-21st-century/ ‹#› Brinker, S. (2016, November). Martec's Law: the greatest management challenge of the 21st century. Chief Martec. https://chiefmartec.com/2016/11/martecslaw-great-management-challenge-21st-century/ Ritchie, H., & Roser, M. (n.d.). Moore's Law: The number of transistors on microchips doubles approximately every two years [Chart]. Our World in Data. https://ourworldindata.org ‹#› ‹#› ‹#› Kurzweil, R. (2005). The Singularity is Near: When Humans Transcend Biology. Viking. p. 67. ‹#› Post, T. (2022, November 1). The Paradox of Applying AI. Medium. Retrieved November 9, 2023, from https://medium.com/@tristan.s.b.post/the-paradox-of-applying-ai-c84eea8aecb5 ‹#› ‹#› ‹#› McKinsey Global Institute ‹#› Brinker, S. (2016, November). Martec's Law: the greatest management challenge of the 21st century. Chief Martec. https://chiefmartec.com/2016/11/martecslaw-great-management-challenge-21st-century/ ‹#› Brinker, S. (2016, November). Martec's Law: the greatest management challenge of the 21st century. Chief Martec. https://chiefmartec.com/2016/11/martecslaw-great-management-challenge-21st-century/ Social Brain Theory ‹#› Communication Complexity ‹#› ‹#› ‹#› Creative Destruction ‹#› Creative Destruction ‹#› Creative Destruction ‹#› Creative Destruction ‹#› ‹#› ‹#› ‹#› ‹#› ‹#› Cloud Technology Partners. (2017). Disrupted or Disrupter: Which One Will You Be? Zero Operational Cost ‹#› Reinventing with AI ‹#› ‹#› Fox, G. (n.d.). Uber Business Model - 1 Platform Attacking New Markets. Retrieved from https://www.garyfox.co/uber-business-model/ Zero Operational Cost The concept of zero operational cost refers to an idealized scenario where the operational expenses of certain processes or services are effectively negligible. It’s important to note that truly zero operational cost is largely theoretical, as there will almost always be some cost involved, whether it’s energy consumption, maintenance, or other overheads. However, the goal is to minimize these costs as much as possible. ‹#› minutes application second approval manual labour ‹#› The Value of Ant Financial Asset management: an activist’s playground. (2020, November 6). Financial Times. ‹#› AI Transcends Industries ‹#› AI First ‹#› VIDEO AI First AI First puts AI in the center of organisations business and operating model. AI First allows organizations to automate decision-making and in doing so transforming the way to capture value and increase their competitive advantage ‹#› AI Journey ‹#› AI Maturity Model ‹#› The Challenges of Each Stage ‹#› The Challanges of Each Stage ‹#› Maturity and Value ‹#› AI Vision ‹#› ‹#› Positioning vs Strategy vs Vision ‹#› ‹#› Pillars of AI Ambition ‹#› ‹#› ‹#› Enabling Factors ‹#› Join at slido.com #1782462 ⓘ Start presenting to display the joining instructions on this slide. What do you think are enabling factors for applying AI in an organization? ⓘ Start presenting to display the poll results on this slide. ‹#› Foster adaptable, innovative culture by promoting continuous learning, risk-taking, collaboration, datadriven decisions, and AI awareness across all levels. ‹#› Culture eats Strategy for Breakfast Peter Drucker Economist and Author Schein's Organizational Culture Model ‹#› Schein's Organizational Culture Model ‹#› Schein's Organizational Culture Model ‹#› AI Adoption Hurdles ‹#› AI Adoption Hurdles ‹#› ‹#› Foster adaptable, innovative culture by promoting continuous learning, risk-taking, collaboration, datadriven decisions, and AI awareness across all levels. Establish integrated structure by creating AI-focused teams, defining roles, and enhancing communication for effective AI integration. ‹#› ‹#› Center of Excellence (CoE) ‹#› Placing the CoE ‹#› Placing the CoE ‹#› Optimizing Team Structure ‹#› Maturity and the CoE ‹#› Foster adaptable, innovative culture by promoting continuous learning, risk-taking, collaboration, datadriven decisions, and AI awareness across all levels. Establish integrated structure by creating AI-focused teams, defining roles, and enhancing communication for effective AI integration. Invest in diverse skills, from AI tech to business acumen, by attracting, developing, and retaining talent, including upskilling existing staff. ‹#› Roles of an AI Team Technical Roles Business Roles Supporting Roles ‹#› Acquiring Talent Technical Roles Business Roles Supporting Roles ‹#› Engage with the evolving AI landscape through collaborations with vendors, research, forums, and opensource tools, enriching organizational knowledge. ‹#› AI Ecosystem Partners Collaboration in AI ‹#› Engage with the evolving AI landscape through collaborations with vendors, research, forums, and opensource tools, enriching organizational knowledge. Optimize data practices by ensuring efficient collection, storage, governance, and fostering data literacy among staff. ‹#› Data is a strategic asset for every organization The world's most valuable resource is no longer oil, but data. ‹#› Data Excellence for AI Success ‹#› Engage with the evolving AI landscape through collaborations with vendors, research, forums, and opensource tools, enriching organizational knowledge. Optimize data practices by ensuring efficient collection, storage, governance, and fostering data literacy among staff. Develop robust AI infrastructure with suitable hardware, software, platforms, data management, privacy, and security measures. ‹#› The AI Factory Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI: Strategy and leadership when algorithms and networks run the world. Harvard Business Review Press. ‹#› ‹#› Dhinakaran, A. (2020, August 11). ML Infrastructure Tools for Production (Part 1). Towards Data Science. The AI Strategy House ‹#› ‹#› ‹#› Horst Urlberger, Microsoft Horst Urlberger, as part of the Microsoft Business Group Germany is responsible for the Microsoft Azure Data & AI Go-To-Market in Germany. He leads the strategic introduction and implementation of Microsoft Azure Data & AI solutions in the local market. Earlier to this, he led Data modernization within the Azure Business group and introduced the strategic landing of Azure VMware Solutions & Azure Virtual Desktop in Germany. ‹#› AI Use Case: Ideation, Evaluation, Priorization 20th November Module Overview The "AI Use Case: Ideation, Evaluation, Prioritization" module is meticulously designed to guide students through the multifaceted process of identifying, evaluating, and prioritizing AI use cases. This session will explore the diverse capabilities of AI, offering techniques to ideate potential AI applications. Students will learn how to critically assess and prioritize AI use cases, focusing on value delivery and ease of implementation. Additionally, discussions on 'make or buy' strategies will provide insights into resource allocation for optimal outcomes. This module is a comprehensive guide to strategically navigating the landscape of AI use case development, whether for startup validation or organizational implementation. Why Attend This Session This session is crucial for anyone looking to validate AI startup ideas or decide which AI use cases to develop and allocate budget to within an organization. It provides practical knowledge and techniques for ideating, evaluating, and prioritizing AI use cases effectively, ensuring the selection of the most valuable and feasible options. Attendees will gain insights into making informed decisions on AI application, whether it’s about validating the viability of startup ideas or optimizing resource allocation for AI development in organizations. This module empowers students to strategically leverage AI capabilities for maximum impact and value creation. ‹#› AI Use Case: Ideation, Evaluation, Priorization Learning Objectives 1. Understanding of AI Capabilities: Explore the diverse capabilities of AI, gaining insights into what AI enables us to achieve and how it can be leveraged for various applications. 2. Skills in AI Use Case Ideation: Learn techniques for ideating AI use cases, enhancing the ability to conceptualize potential AI applications and solutions effectively. 5. Strategic Decision-Making in AI Application: Develop the skills to make strategic decisions on AI use case development and budget allocation, whether for validating startup ideas or for organizational implementation. . 3. Proficiency in Evaluating and Prioritizing AI Use Cases: Acquire the knowledge to critically assess and prioritize AI use cases, focusing on high-value and feasible options for optimal outcomes. 4. Insights into 'Make or Buy' Strategy: Understand the principles of 'make or buy' strategy in AI, gaining the ability to make informed decisions on resource allocation and AI development. ‹#›