Progress Data Platform Summit 2026

Keynote

Business Futurist, AI Keynote Speaker and Futurist-in-Residence at Amazon, Jonathan Brill helps audiences and executives take advantage of the collision of AI, geopolitical, economic and societal trends, to transform their business, mindsets, and turn ideas into action to Win the Next Five Years.

Progress Data Platform Vision & Roadmap

From Legacy to Live: How the Marine Corps Built a Data-First Logistics Platform at Scale

Modernizing defense logistics data requires more than replacing legacy systems. It requires a governed data architecture that can reconcile fragmented records, support authoritative outputs and prepare operational information for downstream digital and AI-enabled services. This session examines lessons from the U.S. Marine Corps Technical Data Management platform, developed to address product data challenges across more than one million parts and thousands of weapon systems.

The session will explore how a large-scale government program moved from obsolete mainframes, incompatible schemas and disconnected source formats toward a modern, cloud-hosted, non-relational data environment. Topics include establishing a single source of truth, ingesting multi-format data from PDF, XML, CAD graphics and legacy feeds, resolving data disparities and delivering trusted information to operational users.

Attendees will also learn how governance, role-based access, validation tooling and DevSecOps practices can support agility in high-compliance environments. The session will close by examining what it takes to make legacy logistics data AI-ready through semantic enrichment, federated access and intelligent search across parts, publications and configuration records.

From Ingest to Insight: Orchestrating Data and AI Pipelines with Flow Conductor

Enterprises need repeatable ways to design, orchestrate and operate data and AI workflows across increasingly complex environments. This session demonstrates an end-to-end workflow using Flow Conductor, showing how teams can build and manage pipelines on a unified, low-code platform.

The session is anchored in a real-world scenario that follows the lifecycle from multi-source data ingestion through transformation, enrichment and delivery to downstream systems. Attendees will see how workflows can be visually designed, triggered and extended with integrations, business logic and AI-driven steps. The session will also show how reusable workflow patterns can reduce manual coordination, improve consistency and make it easier to operationalize data movement and AI processing across the Progress ecosystem.

Customer use cases and sample workflows will ground the demonstration in practical enterprise needs, including lessons from early access conversations. Attendees will leave with a clear understanding of how Flow Conductor supports pipeline orchestration, how it fits into broader PDP architecture and how low-code workflow design can accelerate delivery without creating disconnected automation silos.

Tokens Spent, Value Missing: Why Most Enterprise AI Stalls and What the Survivors Do Differently

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.

Accelerating Decision Automation with Agentic AI and Business Rules

Decision automation is entering a new phase, where organizations need decisions that are not only consistent and explainable, but also better informed by trusted enterprise data and easier to evolve as business needs change. Progress Corticon provides deterministic, rules-based decision automation, while the Progress Data Platform supplies the governed data foundation, policy context, knowledge models, semantic enrichment, traceability and secure access patterns needed to support richer and more auditable decision services.

This session will explore how PDP leverages the Progress Corticon technology to strengthen established decisioning use cases such as eligibility, claims, compliance, routing, pricing, case handling and operational prioritization. It will also look at how agentic AI can help accelerate the creation and evolution of decision projects by assisting with rules, tests, documentation and supporting project artifacts. Rather than replacing rules-based systems, AI can reduce the effort required to build, update and maintain them, while business rules continue to provide the governance, repeatability and control required for critical decisions.

Attendees will leave with a practical understanding of how governed data, explainable rules and agentic AI can work together to modernize decision automation. The session will clarify the role of each capability: Corticon remains focused on trusted decision logic, PDP provides the data and knowledge fabric needed to operationalize better decisions, and agentic AI helps make decision automation faster, more approachable and easier to maintain without sacrificing trust, transparency or compliance.

AI is Already in Your Environment, and It's Largely Unsecured

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.

Beyond the Database: Extending Your MarkLogic Investment Across the Full Progress Data Platform

MarkLogic remains a powerful foundation for managing complex enterprise data, but the broader Progress Data Platform extends its value across the full data lifecycle. This session is designed for architects and technical leaders who understand MarkLogic and want to see how complementary PDP capabilities can help connect, enrich, govern and operationalize enterprise data.

The session will examine how Semaphore adds semantic modeling, classification and metadata enrichment to improve search, knowledge graphs and retrieval-augmented generation pipelines. It will also show how DataDirect supports real-time connectivity and cross-system ingestion, and how Corticon brings explainable decision automation and policy enforcement to trusted data-driven workflows.

Attendees will also be introduced to Data Symphony as an orchestration layer for bringing these capabilities together into cohesive, automated data workflows. The session will focus on architectural patterns, integration considerations and practical examples that show how existing MarkLogic deployments can evolve into a more complete, AI-ready data platform. Participants will leave with a clearer understanding of PDP’s end-to-end architecture and the next steps for modernization.

Defensible by Design: Integrating Fragmented Healthcare Data for Audit-Ready Decisions

Healthcare and life sciences data is fragmented across clinical records, claims, narrative documentation, social services, environmental data, regulatory filings and systems that were not designed to work together. Yet decisions based on this data must withstand audit, fiscal scrutiny, equity review and litigation discovery.

This session presents an architectural pattern for integrating structured, unstructured and semantic data across high-consequence healthcare domains. The session will examine how the Progress Data Platform can combine document storage, triples, ontology management, semantic enrichment, governed business rules and workflow to support cohort identification, signal detection, outcomes attribution and provenance tracing. Worked examples include a drug safety investigation, a diabetes care measure across a Medicare Advantage population and a housing remediation cohort that spans clinical, eligibility, environmental and social services data.

Attendees will learn how an integrated data foundation can support both analytics and AI workloads that must be accurate, consistent and defensible. The session will close with practical guidance for architects deciding where to start, how to prioritize domains and how to design for governance from the beginning.

Measuring Findability After You Ship

A taxonomy cannot be judged only by its structure at launch. Its real value appears when users search, browse and interact with content in production. This session focuses on how to measure whether a taxonomy is genuinely improving findability after it has been deployed.

The session will move beyond coverage and depth metrics to examine runtime signals that reveal how users experience search. Attendees will learn how to use click-through rate, zero-result rate, reformulation rate, ranking position and relevance-judgment benchmarks to assess whether search quality is improving. Query logs will be treated as a diagnostic tool for identifying failing searches and turning them into actionable taxonomy improvements.

The session will also show how explicit and implicit feedback can be interpreted, how root causes can be traced from a failing query to a taxonomy change and how results can be remeasured after each update. Participants will leave with a practical measurement framework, a findability scorecard and a repeatable improvement loop for proving taxonomy value over time.