AI as a Business - Operationalizing AI for Sustainable Value Creation

Overview

Explore the challenges and strategies of building successful AI businesses with Daniel Langkilde, as he discusses data moats, vertical vs. general solutions, explainability in regulated industries, and precision/recall sensitivity.

Syllabus

• Introduction to AI in Business
Overview of AI technologies and applications
Importance of AI in modern business strategies
• Building AI-based Business Models
Identifying AI opportunities in various industries
Case studies of successful AI businesses
• Data Strategy and Competitive Advantage
Understanding data moats and their significance
Data collection, management, and governance
• Vertical vs. General AI Solutions
Differences between vertical-specific and general AI solutions
Approaches to choosing the right AI solution for your business
• AI and Business Integration
Strategies for integrating AI into current business processes
Change management and organizational readiness
• AI Explainability and Trust
Understanding the importance of explainability
Techniques for achieving explainability in AI systems
Regulatory considerations and compliance
• Precision and Recall in AI Systems
Balancing precision and recall based on business needs
Case studies on precision/recall trade-offs
• Challenges of Operationalizing AI
Common pitfalls in AI implementation
Strategies for overcoming operational challenges
• Measuring and Sustaining AI Value
Metrics for evaluating AI success
Continuous improvement and sustainability in AI systems
• Future of AI in Business
Emerging trends in AI technology and business applications
Preparing for future AI developments and opportunities
• Conclusion and Final Thoughts
Recap of key learnings
Q&A and open discussion on AI business strategies

Subjects

Computer Science