Cracking the Code: Scaling AI from Vision to Value

• Cabernet Sauvignon: Justin | Paso Robles 2012
• Bordeaux: Chateau Loudenne | Medoc 2018
• Pinot Grigio: Santa Margherita | Valdadige 2022
• 44 Classic Manhattan: Colonel E.H. Taylor Jr. Small Batch Bourbon, Carpano Antica Vermouth, Angostura bitters, and a Luxardo cherry
• Coastal Rita: Clase Azul Tequila, Rhum Clément Creole Shrubb Liqueur, agave nectar, and fresh pressed lime

Choice of Salad:

• Chopped Iceberg Wedge
• Mixed Greens
• Caesar Salad

Choice of Entree:

• Filet 8oz
• Bone In Ribeye 22oz
• New York Strip 16oz
• Cold Water Norwegian Salmon Fillet
• Custom Vegan Options upon request

Choice of Desserts:

• New York Cheesecake
• Chocolate Sin Cake
• Signature Warm Butter Cake

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• Focused Dialogue:

The moderator will lead the conversation through topics derived from pre-survey responses, ensuring a targeted and relevant discussion.

• Structured Environment:

To maintain the session's integrity and focus, side discussions will be minimized.

• Exclusive Setting:

The roundtable will take place in a private room, ensuring confidentiality and an undisturbed environment.

• Feedback Loop:

Post-event, participants' quotes will be shared with them for approval before any public dissemination, upholding our commitment to privacy, authenticity, and transparency.

• Quantifying ROI from AI Infrastructure Investments:

Exploring metrics such as operational efficiency, cost savings, and speed to market to measure AI ROI while addressing challenges in aligning infrastructure spending with business goals.

• Unlocking Scalability with Advanced Solutions:

Discussing how to overcome compute bottlenecks, storage limitations, and inefficiencies in legacy systems to scale AI enterprise-wide and leverage foundation models for rapid innovation.

• Addressing Pilot Failures & Scaling Challenges:

Examining why 91% of AI projects fail to move from pilot to production and identifying strategies to align technical capabilities with business priorities for scaling success.

• Balancing Innovation and Stakeholder Expectations:

Understanding how to navigate organizational resistance, secure stakeholder buy-in, and prioritize infrastructure upgrades that align with long-term business objectives.

• Lessons Learned from AI Implementation:

Capturing insights from failed or delayed AI initiatives, emphasizing the importance of early stakeholder alignment, realistic expectations, and balancing innovation with operational feasibility.