James Meng
Senior Fellow, SuperComputer Center, University of California, San Diego

TBD

Dinesh Thangaraju
Head of AWS Data Platform, Amazon Web Services

As organizations race to deploy AI agents across their enterprises, they're encountering a critical bottleneck: fragmented knowledge bases and siloed agent capabilities that prevent comprehensive, cross-domain insights. This presentation explores how to address this challenge through a federated approach to enterprise agentic AI infrastructure. This session examines three fundamental challenges facing enterprise AI adoption:

The Knowledge Fragmentation Problem: Organizations are independently building knowledge bases for specific use cases, leading to duplicated effort, inconsistent data parsing strategies, and context drift as source documents evolve. Each team creates their own chunking, embedding, and retrieval mechanisms without a unified architecture—resulting in AI agents that cannot access cross-functional domain knowledge or maintain accuracy at scale.

The Agent Collaboration Gap: Today's AI agents operate in isolation within their domains. When business leaders ask complex questions spanning customers, pricing, services, and operations, they receive fragmented answers requiring manual synthesis. Without standardized agent-to-agent communication mechanisms, authentication frameworks, or centralized agent registries, organizations cannot deliver the holistic insights that drive strategic decision-making.

The Governance and Innovation Paradox: Enterprises need to enable rapid experimentation while maintaining security, compliance, and user experience standards. Traditional centralized approaches stifle innovation; purely decentralized approaches create chaos. The challenge is building federated frameworks that guide discovery, assessment, incubation, and graduation of AI solutions without creating bottlenecks.

This presentation introduces a three-pillar architecture for enterprise agentic AI:

Unified Federated Knowledge Base: A bottom-up approach where domain teams create specialized knowledge bases that integrate into an organization-wide ecosystem through standardized ingestion pipelines, interfaces for vector databases and knowledge graphs, and evaluation frameworks for accuracy and relevance.

Cross-Domain Agent Collaboration: Technical mechanisms enabling AI agents to discover, authenticate, and communicate with each other—transforming isolated data points into comprehensive business intelligence that spans organizational boundaries.

Federated Innovation Framework: Secure sandbox environments with structured governance models that accelerate development time by 50% while maintaining enterprise standards for security and user experience.

Attendees will learn:

• Practical patterns for building federated knowledge architectures that eliminate redundant development efforts
• Technical approaches to agent-to-agent communication, including authentication, authorization, and service discovery
• Governance models that balance innovation velocity with enterprise security and compliance requirements
• Implementation strategies for co-owned initiatives requiring commitment across multiple data organizations

This session is essential for Chief Data Officers, enterprise architects, and data leaders navigating the transition from siloed AI experiments to scalable, federated agentic AI ecosystems that deliver measurable business value.

Elena Alikhachkina
Chief Data and AI Officer, TE Connectivity

TBD

Danette McGilvray
President and Principal Consultant, Granite Falls Consulting, Inc.

TBD

Dr. John R. Talburt
Distinguished Professor of Information Science, UA Little Rock

TBD

Robert Abate
Senior Advisor, CDOIQ

TBD

Stuart Madnick
Professor & Founding Director, Cybersecurity at MIT Sloan (CAMS)

TBD

Douglas Laney
Data, analytics and AI advisor, reseacher, and author

The autonomous organisation represents the next frontier in business transformation, moving beyond advanced analytics, piecemeal AI, and single-function automation. We are on the precipice of true organizational “self-driving” capabilities which will redefine business models and economies. While many enterprises still struggle with basic AI implementations, leading organisations are poised to introduce intelligent systems that can sense, decide, perform, interact, and adapt across entire business functions at digital speed.

Drawing parallels with the evolution of autonomous driving, Laney will define the seven levels of agentic AI – from early-stage chatbots to full business self-awareness and execution – and share new technology providers and business executives can prepare for this imminent inevitability.

Peter Aiken
Associate Professor, Virginia Commonwealth University
Founding Director, Anything Awesome

Everyone wants to use data to add value to their organizations.  The really important question is:  how can organizations achieve more effectively data practices?  This has been difficult to correct because, to quote Einstein:  The significant problems we face cannot be solved at the same level of thinking we were at when we created them.  Poor data education has led to naive understanding that, when combined with a technology-first, bias has prevented the vast majority of organizations from making tangible progress.  This, in spite of significant investments in hype such as big data science.  Before attempting data improvements, organizations must resolve flawed decision making about data issues.  The  briefly takes senior executives through a data awareness journey, transforming their thinking about data.  It provides an opportunity to better understand the kind of people and process decisions that will most speed-up your organization's ability to better leverage data.  Many examples illustrate the material.

Derek Strauss
Founder, CEO & Principal Consultant
Gavroshe

Transitioning the CDO from a Cost Center to a Profit Center

In the modern business landscape, organizations are inundated with vast amounts of data generated from customer interactions, operational processes, and external partnerships. Despite this abundance, many struggle to convert this data into actionable insights and tangible financial gains.

AI-driven Knowledge Governance is emerging as a transformative framework, utilizing the Data Liquidity Protocol (DLP), which revolutionizes data governance by embedding cryptographic proofs directly into data products. This ensures that every data product is verifiable, secure, compliant, and monetization ready.

DLP is unique because it binds five essential cryptographic proofs to every data product:

• Proof of Integrity: Ensures data provenance and quality.
• Proof of Ownership: Provides enforceable control over data assets.
• Proof of Security: Implements layered cryptographic protection.
• Proof of Compliance: Embeds privacy-preserving proofs to comply with regulations.
• Proof of Value: Measures ROI from every data use case.

By combining Knowledge Governance with DLP, organizations can automate compliance, manage risk, enhance data quality, unleash innovation, and unlock new revenue streams. This synergy allows organizations to transform raw data into valuable, marketable assets without exposing sensitive information or violating regulatory standards.