The AI Value Playbook: Making AI Work in the Real World

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Overview

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In this course, you'll learn how to leverage AI to drive business value and strategy. Gain insights into AI's role in business operations and how to implement it effectively. You'll be guided through real-world applications, industry perspectives, and actionable strategies to maximize AI’s impact. What sets this course apart is its focus on practical, business-driven results, ensuring that you understand how to implement AI in your organization. Designed for non-technical business leaders, this course is perfect for those seeking to unlock the potential of AI in their companies.

Syllabus

Introduction

In this section, we explore practical AI strategies for business growth, focusing on data-driven decision frameworks and scalable integration models for non-technical leaders.

Overview of AI Concepts and Technology Stack

In this section, we explore core AI concepts, types like machine learning and generative AI, and the AI technology stack to understand their business applications and limitations.

Sam Liang, CEO of Otter.ai

In this section, we explore real-time speech recognition and conversational AI applications for meeting optimization and collaborative knowledge sharing in enterprise environments.

Amr Awadallah, Founder and CEO at Vectara

In this section, we explore integrating generative AI with proprietary data to enhance business operations, focusing on reducing hallucination, bias, and ensuring compliance for reliable AI deployment.

Philipp Heltewig, Co-Founder and CEO at Cognigy

In this section, we explore AI integration in customer service, focusing on ROI, human-AI collaboration, and strategic deployment for improved satisfaction and operational efficiency.

Miao Song, Chief Information Officer at GLP

In this section, we explore data governance, integrated platforms, and business value alignment to drive sustainable growth through analytics and strategic data management.

Ruben Ortega, General Partner at Enjoy the Work

In this video, we introduces Ruben Ortega, a General Partner at Enjoy the Work, and highlights his professional experience.

Joshua Rubin, Principal AI Scientist at Fiddler AI

In this section, we explore AI observability and explainability for enterprise applications, emphasizing measurable metrics, transparent systems, and responsible AI frameworks to ensure trust and compliance.

Nadine Thomson, Global Chief Technology Officer at GroupM (WPP)

In this section, we explore integrating AI and data into business strategies to enhance efficiency and revenue through stakeholder mapping and data-driven frameworks.

Sarvarth Misra, Co-Founder and CEO of ContractPodAi

In this section, we explore AI-driven contract management solutions, analyze legal tech challenges, and design modular systems to enhance efficiency and innovation in legal workflows.

Edward Fine, AI and Data Science Consultant Technologist and instructor

In this section, we explore data-driven decision-making, customer behavior analysis using SQL, and designing low-stakes data products to build trust and achieve measurable outcomes.

Sanjeevan Bala, Group Chief Data and AI Officer at ITV

In this section, we explore the last mile approach to align data strategies with business value, analyze value chain opportunities, and implement decentralized team structures for measurable outcomes.

Nathalie Gaveau, AI Tech Entrepreneur and Board Member

In this section, we explore strategic AI implementation through phased pilots, customer segmentation, and data-driven decision-making to drive business improvements and stakeholder buy-in.

Phil Harvey, Applied AI Architect

In this section, we explore synthetic data generation, causal ML integration, and knowledge-based AI architectures, emphasizing their practical applications in industrial and financial domains.

Elizabeth Ajayi, Director, Intelligent Industry at Capgemini Invent

In this section, we explore AI opportunities in high-volume processes, strategies for responsible adoption, and challenges in implementation. Key concepts include automation, ethical integration, and collaboration for impactful solutions.

Louis DiCesari, Head of Data, Analytics and AI at Levi Strauss & Co.

In this section, we explore aligning data strategies with stakeholder needs, using 2x2 matrices for prioritization, and balancing data quality for faster impact.

Vickey Rodrigues, CTO/CDO in Insuretech, Payments, and Healthtech

In this section, we explore capability-based project execution, data integration, and designing insurance products through real-world health data insights and incremental delivery strategies.

Sean McDonald, Former Global Chief Innovation Officer at McCann Worldgroup

In this section, we explore integrating data for cross-functional insights, leveraging AI in creative strategies, and designing frameworks for data transparency and collaboration.

Julie Gray, Head of Data and Internal Systems at Agilio

In this section, we explore implementing CRM systems, analyzing data for insights, and designing unified data structures to enhance clarity and business outcomes.

Peter Jackson, Chief Data and Technology Officer at Outra

In this section, we explore data-driven decision-making, aligning strategies with business outcomes, and designing scalable governance frameworks for transformational impact.

Mark Beckwith, Director of Data Governance and Architecture at the Financial Times

In this section, we explore data governance frameworks and engagement metrics to drive business value, emphasizing structured data practices and their role in strategic decision-making and reader engagement.

Kshira Saagar, Data Science at Wolt and Doordash

In this section, we explore data-driven decision-making, business problem-solving with data science, and designing scalable solutions for operational efficiency in diverse industries.

Joe Romata, Global Head of Customer Experience at a Multinational Energy Company

In this section, we explore user-centric design, customer journey analysis, and omnichannel strategies to enhance engagement and drive data-informed business outcomes.

Tomasz Ullman, Former Global Head of Data Science and Strategy at Ford Pro

In this section, we explore probabilistic modeling in data science, communication of AI limitations to leadership, and real-world applications in mobility solutions.

Oz Krakowski, Chief Business Development Officer at Deepdub

In this section, we examine AI-driven localization workflows, scalable dubbing solutions, and the economic impact of reducing barriers to global content access.

Case Study: LLMs and RAG Enable Hyper-Personalized Education for Healthcare Technicians

In this section, we examine how RAG and LLMs enhance personalized learning for healthcare technicians, focusing on context-aware systems and tailored educational applications.

Case Study: AI Personalization Increases Engagement in Nascent Tech Communities

In this section, we explore AI personalization in non-English digital communities, focusing on inclusivity and relevance.

Case Study: AI-Powered Virtual Agent Augments Service Efficiency

In this section, we explore AI-powered virtual agents that enhance service efficiency. Key concepts include secure deployment, context-dependent solutions, and scalable enterprise strategies.

Case Study: Generative AI Creates a Paradigm Shift in Innovation Processes

In this section, we explore how generative AI transforms innovation through data-driven creativity and business integration.

Case Study: Unlocking Profit Potential Leveraging Enterprise Data for Customer Profitability

In this section, we explore using enterprise data to enhance customer profitability through stakeholder engagement, incremental data management, and actionable insights for sustainable business growth.

Case Study: Minimizing Customer Churn with AI

In this section, we explore AI-driven strategies for predicting and reducing customer churn through data analysis and actionable insights.

Case Study: Enhancing Marketing Strategies Through the Power of LLMs

In this section, we explore how language models like ChatGPT improve audience insights and marketing strategies through real-world case studies.

Case Study: Multimodal LLMs Redefine Software Development and Customer Innovation

In this section, we examine how multimodal LLMs enhance software development and customer innovation through AI integration, focusing on internal and external applications for efficiency and creativity.

Where We've Got To and What's Next

In this section, we explore AI strategies for operational efficiency, value creation, and scalable frameworks across industries, emphasizing measurable outcomes and strategic alignment.