10:00 AM - 11:14 AM Central
Here's a more detailed look at the controller's role in AI:
1. Data Integrity and Quality:
• Controllers are responsible for ensuring the quality and integrity of data used to train and inform AI models.
• They work to establish data governance frameworks, implement data quality initiatives, and ensure data lineage (tracking data from source to downstream systems).
2. Risk Management:
• Controllers assess potential risks associated with AI implementation, including inaccuracies, vulnerabilities, and ethical considerations.
• They help define and implement risk management frameworks for AI, ensuring that AI systems are used responsibly and ethically.
3. Compliance:
• Controllers stay informed about relevant regulations and ensure AI systems are compliant with those regulations.
• They monitor AI systems to ensure they are not violating any laws or policies.
4. Transparency and Auditability:
• Controllers work to ensure that AI decisions are transparent and auditable.
• They establish processes to review AI decisions and identify potential biases or errors.
5. Collaboration and Training:
• Controllers work with IT and business teams to understand AI's capabilities and limitations.
• They provide training to teams on how to use and manage AI tools effectively.
6. Value Creation:
• Controllers can leverage AI to identify areas for automation, improve efficiency, and gain real-time insights into financial operations.
• They can use AI to make more informed decisions and create more strategic recommendations for action.
7. Emerging Role:
• The role of the controller is evolving with the rise of AI, with controllers becoming more hands-on throughout the implementation process.
• They are increasingly seen as catalysts for AI-enabled transformation, helping to identify potential use cases and build confidence in the technology
This course will explore many of these topics.
Speakers:
Lynn Fountain
| 10/10/2025 |    |
