Revolutionizing Business Strategy with AI

AI is no longer just a tool; it’s a catalyst for transformation. The Wise-X Principle is a groundbreaking AI-driven framework designed to accelerate personal growth, business success, career advancement, financial growth and emotional growth . By integrating AI with human intelligence, this approach unlocks exponential results, ensuring that innovation and decision-making happen faster and more effectively than ever before.

This keynote session will explore how the Wise-X Principle enhances personal and professional development, supports wealth creation, and generates long-lasting intellectual property that can be passed down for generations.

You will learn:

• How integrating AI with human intelligenceleads to exponential, not just incremental, progress
• Key ways AI can optimizerevenue streams, market positioning, and innovation
• Strategies to stay ahead in an AI-powered economy by adapting and evolving continuously
• How AI-driven automation and insightscan streamline operations and boost productivity

Swati Shirname

Author

Generative AI for Leaders: Playbook for Innovation

While many organizations have initiated AI pilot projects, transitioning these initiatives into scalable, enterprise-wide solutions remains a challenge. This panel will explore strategies for effectively integrating AI into core business operations, ensuring alignment with organizational goals, and achieving measurable outcomes.​

Discussion points will include:

• Key considerations for moving from AI pilot programs to full-scale deployment

• Aligning AI initiatives with business objectives and securing stakeholder buy-in

• Overcoming common obstacles in enterprise-wide AI adoption

• Measuring the impact of AI on operational efficiency and decision-making

• Case studies highlighting successful AI integration across various departments

Moderator:

Prashant Natarajan

Chief AI Officer | Bestselling Author

Velatura | 'Demystifying AI for the Enterprise'

Panelists:

Ambhi Ganesan

Partner, Artificial Intelligence & Machine Learning

IBM

Shelby Heinecke, Ph.D.

Senior AI Research Manager

Saleforce

Shobana Ranganathan

Vice President, Enterprise Technology

The Walt Disney Company

Abstract coming soon!

As generative AI continues to evolve, the spotlight is shifting toward agentic AI systems—autonomous agents designed to make decisions, take actions, and adapt dynamically with minimal human input. While the potential is enormous, from streamlining operations to enabling entirely new business models, many organizations are still unsure whether agentic AI is the next breakthrough or the latest overhyped distraction.

This keynote will cut through the buzz and focus on the practical realities of agentic AI. What does it take to move beyond prototypes to production? What risks should leaders anticipate, and where are the early returns already visible? Drawing from real-world use cases and emerging deployment patterns, we’ll examine whether agentic AI is ready to deliver measurable value, or if it’s still more farce than force.

You will learn:

• How agentic AI differs from traditional AI systems and where it fits in enterprise architecture

• Key business functions where agentic AI is already driving efficiency, personalization, or automation

• The risks and governance concerns unique to autonomous agents and how to mitigate them

• Frameworks for evaluating readiness and prioritizing use cases for agentic AI adoption

• Lessons from early adopters on what works (and what to avoid) in deploying agentic systems at scale

Alok R. Chaturvedi, PhD

Professor of Management

Purdue University

As AI continues to integrate deeper into critical sectors, the potential security risks it poses have become an essential area of focus. Attacks aimed at compromising the integrity of machine learning models or manipulating their data inputs could result in significant disruptions, impacting everything from personal data security to financial and national security.

This keynote will explore recent threats, such as adversarial attacks and data poisoning, while examining the impact of AI’s vulnerability to novel exploits.

You will also learn:

• How to identify and mitigate adversarial attacks to maintain the accuracy and reliability of machine learning models
• Strategies for enhancing AI training data integrity, preventing data poisoning, and ensuring trustworthy model development
• The role of real-time monitoring and AI audit trails in detecting anomalies, improving transparency, and ensuring system accountability
• Practical approaches to embedding security measures throughout the AI lifecycle

Tolgay Kizilelma, PhD

Director of MS in Cybersecurity Program

Dominican University of California