The Controllership Series: The Controller's Role in Artificial Intelligence

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.

Basic Course Information

• Trends in AI
• Controller's role in AI
• Specific AI processes for Controllers
• Identify key AI cases
• AI and financial reporting
• AI and fraud cases
• AI and expense management
• AI and improved decision making
• AI and risk assessment
• AI and Supply chain management
• The seven pillars of a trustworthy framework

• Accounting
• Finance