The session will examine how ontologies, knowledge graphs, and world models will enable organizations to aggregate context, accelerate learning, improve decision-making, and continuously evolve their understanding of the business. While building a company brain will require significant investment, it will increasingly become table stakes for organizations seeking to scale AI and maintain competitive advantage in an increasingly complex world.
Enterprise generative AI investments often stall because adoption metrics are mistaken for business impact. Token spend, seat counts and pilot activity may show usage, but they do not prove value if AI cannot be reliably connected to correctness-critical workflows.
This session examines why many AI initiatives fail to produce measurable returns and argues that the underlying issue is often semantic, not model-related. The session will show how ambiguous retrieval, inconsistent definitions and weak integration patterns can prevent AI from being trusted in operational settings. It will also explain where formalized semantics can make a material difference by clarifying meaning, improving retrieval quality, supporting governance and creating a more reliable foundation for automation.
Attendees will leave with a practical field test for deciding when semantic infrastructure earns its cost and when a lighter approach is sufficient. The session is designed to help leaders avoid overengineering while recognizing the moments when semantics becomes essential to accuracy, trust and business value. Participants will gain a clearer framework for connecting AI investments to outcomes rather than activity.
AI adoption is no longer limited to approved platforms or formally sponsored projects. It is appearing across SaaS applications, developer tools, productivity workflows and informal business processes, often faster than security, compliance and governance teams can track.
This session examines the gap between where organizations believe AI is being used and where it is actually influencing work. The session will explore what AI usage looks like in a real enterprise environment, why traditional visibility controls often miss it and how unmanaged adoption can create risks around data exposure, intellectual property, compliance, model dependency and operational accountability. Rather than treating the issue as a reason to slow innovation, the session focuses on practical ways to discover usage, assess exposure and introduce control.
Attendees will learn how to start building an inventory of AI touchpoints, evaluate risk by workflow and data type, and apply governance in a way that supports responsible adoption. The session will provide a pragmatic path for improving visibility and control without blocking the productivity gains that are driving AI use across the business.
Taxonomies, ontologies and knowledge management increasingly overlap as organizations seek better ways to organize, connect and apply enterprise knowledge. This educational session explains how knowledge organization systems support knowledge management, why they remain relevant in the age of AI and how they can help turn scattered information into usable business context. The session will define the roles of taxonomies and ontologies, showing how they relate, differ and extend one another. It will then connect those structures to the core components of knowledge management, including knowledge creation, capture, organization, retrieval, sharing, reuse and governance. Attendees will learn how taxonomies support the knowledge management lifecycle by improving consistency, findability, navigation and content organization. The session will also show how ontologies extend these benefits through richer relationships, enterprise knowledge graphs and semantic layers that can support analytics, discovery and AI-enabled applications. Participants will leave with a practical understanding of where taxonomies and ontologies fit within KM strategy, how they strengthen enterprise knowledge programs and how semantic foundations can improve both human and machine use of organizational knowledge.
Taxonomy Consultant, Hedden Information Management
Heather Hedden is a taxonomy consultant who has been working in the field of taxonomies and information management for 30 years and more recently has also gotten into ontology design. In addition to working as an independent consultant, Heather has been employed in taxonomist roles... Read More →
Wednesday September 16, 2026 9:15am - 10:15am EDT Lake Thoreau Track 4Hyatt Regency Reston, VA
As organizations race to deploy AI agents across critical business processes, many discover that AI is only as trustworthy as the data and context behind it. In this session, Datavid will explore how organizations can evolve from traditional operational data hubs to a true "Company Brain"—a semantic layer that connects data, knowledge, business rules, and organizational context into a unified foundation for AI.
Attendees will learn how semantic technologies, knowledge graphs, and governed metadata help AI agents move beyond simple data retrieval to deliver explainable, trustworthy, and business-aware outcomes. Through real-world examples, this session will demonstrate how enterprises can reduce hallucinations, improve transparency, and provide the context AI agents need to make informed decisions—turning fragmented information into a strategic advantage for the AI era.
Healthcare providers delivering in-home urgent care must make timely, accurate decisions using patient information that is often fragmented across electronic health records, clinical notes, referral documents, operational systems, and real-time field data. Converting this scattered mix of structured and unstructured information into trusted, actionable decisions is critical for improving patient outcomes while maximizing limited clinical resources.
We will present a case-study about a next-generation, AI-powered platform that transforms mobile integrated healthcare by unifying an intelligent human in the loop triage agent, AI-generated clinical notes, accurate volume forecasting, and an automated service-assignment agent. The Triage Agent assists clinicians during patient intake by transcribing phone calls, extracting key information from those calls, analyzing symptoms, patient history, and clinical guidelines to recommend the appropriate level of care with transparent, explainable recommendations. The human in the loop reduces patient care risks and provides the feedback to improve agent’s performance. The Service Provisioning Agent then identifies the most appropriate paramedic based on clinical qualifications, availability, location, workload, and equipment, generates an optimized route, and continuously adapts assignments as conditions such as traffic, emergencies, or resource availability change.
Attendees will see how the solution enables organizations to build trusted and highly accurate AI systems that combine traditional AI, semantic graph intelligence, business rules, and agentic workflows to turn scattered healthcare data into real-time, actionable intelligence, delivering faster response times, improved operational efficiency, and better patient care.
Learn how AWS is helping organizations move AI initiatives from experimentation into scalable production environments. This session will explore Amazon's latest approach to deploying enterprise AI and how Progress Agentic RAG can support the development of grounded, context-aware AI applications. Attendees will also learn how the new AWS AgentCore Harness connector integrates with Progress Agentic RAG to help organizations connect enterprise information with AWS AI services and operationalize agentic AI within production environments. The session will cover practical considerations for integration, scalability, governance, and deployment.
