Chief AI Officer Summit schedule

•    Understanding how the Chief AI Officer drives AI adoption across all levels of the organization, ensuring AI is aligned with business goals and long-term vision
•    Defining the CAIO's role in establishing ethical guidelines, ensuring transparency, and managing the responsible deployment of AI technologies within the enterprise
•    How the CAIO facilitates collaboration between data science, IT, business leaders, and other stakeholders to create a unified approach to AI innovation and maximize its impact

•    A proven step-by-step framework to future-proof your AI strategy and adapt to changing landscapes.
•    Real-world examples of how principles and culture drive lasting success in leading enterprises.
•    Field-tested tools to build an actionable AI roadmap that delivers scalable and sustainable growth.
•    Transformative insights to unify your team and drive a vision of innovation, impact, and leadership.

•    How can AI initiatives be deeply embedded into core business strategies to support long-term growth and ensure alignment with overall business goals?
•    What leadership strategies are most effective in fostering innovation while integrating AI across different business functions?
•    How can organizations successfully balance AI-driven innovation with risk management to ensure sustainable transformation?
•    What key methods are successful companies using to scale AI while maximizing return on investment (ROI)?

Panelists:

Achuth Rao, Chief Product Officer, AI & Data, New York Life Insurance Company
Richa Singh, Head of Data and AI, Lexington Partners
Piyush Singhal, Head- Enterprise Data Engineering, Architecture & Analytics, Petco

•    How do you build and nurture AI skills across the enterprise? Ensuring the organization has the right talent for successful AI execution
•    Fostering a culture of continuous learning and skill development to stay ahead in the rapidly evolving AI landscape
•    Creating an AI-driven innovation culture by instilling the importance of AI for the future of the business

Innovation Slot 1: 
Innovation Slot 2: 

•    Why being slow to adopt AI can hinder a company’s ability to remain competitive in today’s rapidly evolving business landscape
•    Examples of AI tools and strategies that are revolutionizing processes, improving decision-making, and enhancing overall business agility
•    Addressing challenges like resistance to change, skill gaps, and the need for cross-departmental collaboration to successfully implement AI

•    What does the business want to accomplish, and which outcomes are the objectives?
•    Leveraging the power of AI to detect outliers and identify opportunities that may have been missed otherwise   
•    Combining AI with traditional business logic and business knowledge to improve efficiency  
•    Effectively delivering AI to address business needs

•    Assessing and optimizing the return on investment (ROI) for Generative AI projects is paramount as businesses increasingly invest in this transformative technology
•    Learning to measure the tangible and intangible ROI factors to justify and enhance generative AI projects
•    Discovering strategies for aligning Generative AI goals with business objectives and market demands.
•    Gaining insights into continuous monitoring, model refinement, and adaptation to maximize Generative AI project ROI

•    How can leaders cultivate a culture of responsibility and ethics within AI teams to minimize biases and enhance trust in AI technologies?
•    How organizations can integrate ethical considerations into the design and deployment of AI solutions, ensuring that innovation benefits all stakeholders without causing harm?
•    Exploring strategies for creating transparency in AI decision-making processes, including the use of audit trails and explainable AI to build public and regulatory trust

•    The applicational limitations of Data Science and AI
•    The modern history of Machine learning from a pedagogical perspective
•    The NYC Council’s journey with AI

• Defining the roles and responsibilities of the Chief Artificial Intelligence Officer
• Developing strategic alignment: ensuring AI initiatives align with overall strategic goals
• Bridging the gap between technical capabilities and organizational vision
• Developing a comprehensive roadmap for AI integration
• What specific expertise should a Chief Artificial Intelligence Officer possess: specialized knowledge in machine learning, natural language processing and more
• How to make informed decision making about AI technologies and their applications

• Developing your strategy together with senior leadership so as to not lose the initial excitement around your appointment
• Demonstrating the deep understanding of AI technology and strong business acumen
• Effective communication with technical and non-technical staff
• Developing budget and financial planning skill to ensure strong ROI of AI initiatives and investments
• Overcoming resistance to integrating AI into the organization culture: fostering a culture of innovation and data-drive decision making
• Staying compliant with evolving AI regulations
• Prioristing cybersecurity measures to protect AI assets and data

• Defining the roles of the Chief Artificial Intelligence Officer and the Chief Data Officer
• Understanding the relationship between the two roles
• Examining the AI specific tasks for the CAIO in relation to eh role of CDO management
• Managing legislation related to AI-specific data
• Addressing gaps in the organization skill set related to data

• Analyzing generative AI's influence on various sector's competitive dynamics, identifying key shifts and opportunities
• Developing proactive strategies to leverage generative AI for unique value propositions and market differentiation
• Establishing cross-sector dialogues to understand and adapt to the broader implications of generative AI transformations

By incorporating the approaches below, organizations can effectively embed AI responsibility into their practices and contribute to the development of trustworthy and ethically aligned AI systems.

• Ethical frameworks and guidelines
• Bias detection and mitigation
• Transparency and explainability