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Building an AI Council That Drives GenAI Adoption and Innovation

Building an AI Council That Drives GenAI Adoption and Innovation

51mBeginner2025-03-11

Authors

Jim Sterne

Jim Sterne

Author and thought leader in the field of AI for marketing

Course details

In this course, learn how you can drive AI adoption and innovation in your organization—no technical expertise required. Instructor Jim Sterne shares best practices for establishing and running an effective AI council. Learn to assemble a team that represents key stakeholders across the org, how to develop ethical guidelines for AI use, and how to implement effective governance structures that drive AI adoption across the company. Bring in generative AI in a controlled way that carefully encourages innovation. This course is a playbook for establishing an internal AI Council to track the rampant advances in generative AI, provide direction on policies, guidelines, and training, and act as a clearing house for best practices and collective learnings.

Learning objectives
Describe the five essential roles required for an effective AI council, including key responsibilities and reporting relationships.
Identify the three core elements for an effective AI council proposal.
Explain the relevance of cultural acceptance and risk tolerance in a company’s AI readiness.
List the six mandatory components of an AI ethics framework.

Skills covered

Business StrategyArtificial Intelligence for BusinessBusiness Analysis and StrategyLeadership and ManagementOne-Off

Concepts

0. Introduction

  • 01 - Creating a generative AI council
  • 02 - What to know before starting this course

1. Understanding the Need

  • 03 - The need for an AI council

2. AI Council Members

  • 04 - AI council members

3. Situational Awareness

  • 05 - Assessing your company's culture, vision, and risk tolerance
  • 06 - Keep abreast of new and emerging AI technologies

4. Compliance and Security

  • 07 - Work with the legal department to understand regulatory requirements
  • 08 - Work with IT to implement robust data protection measures
  • 09 - Establish ethical guidelines

5. Skills and Training

  • 10 - Assess current capabilities and identify skill gaps
  • 11 - Develop training programs for employees at all levels
  • 12 - Gather leading adopters to glean and publish learnings

6. Implementation and Integration

  • 13 - A phased roadmap for AI implementation
  • 14 - Measuring the success of AI initiatives
  • 15 - Continuous improvement of AI strategies and practices

Conclusion

  • 16 - Next steps on the path to AI success

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