Data Quality & Model Complexity

• Ensuring diverse, high-quality training data.
   

• Balancing model complexity for interpretability.

IPR & Data Ownership

• Defining and protecting intellectual property rights.
   

• Clarifying ownership of training data.

Human-AI Collaboration 

• Enhancing collaboration for optimal performance.
   

• Ensuring user trust and acceptance.

Integration & Scalability

• Seamlessly integrating AI/ML into workflows.
   

• Ensuring scalable models for production demands.

Interpretable AI & Explainability

• Enhancing model interpretability for stakeholders.
   

• Providing explanations for model predictions.

Continuous Learning 

• Designing adaptable systems for changing environments.
   

• Implementing mechanisms for continuous model improvement.

Data Security & Access Control

• Protecting sensitive information and ensuring compliance.
   

• Implementing robust access controls for data protection.

Ethical Considerations

• Addressing biases and ensuring fairness.
   

• Developing guidelines for ethical AI use.

Quantifying Value & Communication

• Demonstrating business value and ROI.
   

• Effectively communicating AI benefits and limitations.