Let's Adopt Generative AI in Enterprise - Best Practices and Challenges

Overview

Gain insights into enterprise AI adoption strategies, challenges, and best practices through PagerDuty's journey implementing generative AI technologies across teams and infrastructure.

Syllabus

• Introduction to Generative AI in Enterprises
Definition and examples of generative AI
Overview of enterprise adoption trends
• The Business Case for Generative AI
Assessing the potential ROI
Identifying areas of impact within an organization
• PagerDuty's Journey with Generative AI
Initial motivations for adoption
Key milestones and timelines
Outcomes and achievements
• Implementation Strategies
Building a cross-functional AI team
Integrating AI into existing workflows and infrastructure
Selecting the right generative AI tools and platforms
• Challenges in Generative AI Adoption
Data privacy and security concerns
Balancing AI automation with human oversight
Ensuring scalability and flexibility
• Best Practices for Generative AI Deployment
Developing a robust AI governance framework
Continuous monitoring and model evaluation
Promoting a culture of innovation and learning
• Case Studies and Real-World Examples
Successful deployments across different industries
Lessons learned and key takeaways
• Ethical and Responsible AI
Addressing bias and fairness in AI models
Transparency and explainability
• Future Trends and Innovations in Generative AI
Emerging technologies and their potential impact
Preparing for the future of AI in enterprise
• Course Conclusion and Key Insights
Recap of best practices and challenges
Final discussion on the future of generative AI in enterprises
• Additional Resources and Further Reading
Recommended books, articles, and case studies
Online communities and forums for ongoing learning

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Subjects

Data Science