As AI evolves from answering questions to driving decisions and actions, trust has become the defining factor in business success. In this keynote, John Ainsworth, EVP & GM, Application and Data Platform, explores why organizations need more than powerful models. They need trusted data, governed intelligence and confidence in the outcomes AI produces.
Through the lens of Context, Control and Confidence, John will share a vision for how organizations can transform enterprise information into trusted knowledge, apply governance and accountability to AI-driven processes, and empower people to make faster, better decisions. Attendees will learn why a strong data foundation is essential for AI readiness, how businesses can move from insight to action with greater confidence, and what leaders should consider as AI advances toward greater autonomy.
The session will also offer perspectives on the future of the data and AI landscape, including how the announcement to acquire the Domo AI and Data platform could help organizations connect data, analytics and AI to accelerate business outcomes. Join us to explore how trusted intelligence will shape the next generation of enterprise innovation.
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.
Mississippi Medicaid is transforming prior authorization in response to the CMS Interoperability and Prior Authorization Final Rule, replacing slow, manual processes with a more interoperable and consent-driven ecosystem.
This session provides a practical update on the state’s implementation and the steps being taken to support faster beneficiary access to care while reducing administrative burden for providers. The session will explain how FHIR-based Prior Authorization APIs are being deployed alongside Patient Access, Provider Access and Payer-to-Payer APIs. Attendees will see how clinical, claims and eligibility data can be integrated to support compliant prior authorization responses within CMS-mandated timelines, while also advancing broader interoperability goals.
Key themes include state readiness, integration of disparate data sources, intelligent decision support, governance models, technology choices and collaboration across payer and provider stakeholders. The session will also share early lessons, implementation challenges and scalable strategies for meeting 2026 and 2027 compliance deadlines. Attendees will leave with practical guidance for modernizing Medicaid processes, overcoming legacy constraints and turning regulatory requirements into operational and clinical value.
Organizations are under pressure to anticipate the next layer of the technology stack while still making sense of the systems they already run. In AI programs, that challenge often becomes more difficult when metadata, taxonomies and ontologies are treated as optional documentation rather than core foundations. This session explains why these structures provide the scaffolding and guardrails AI systems need to produce useful, reliable and context-aware outputs.
The session will cover the foundational layers of AI implementation, where they sit within enterprise ecosystems and how they interact with existing data, content and application environments. It will examine different types of metadata, the role of taxonomies in organizing meaning and the value of ontologies in representing concepts, relationships and business context.
Attendees will also learn why AI does not remove the need for metadata discipline. Instead, well-managed semantic foundations help guide AI, reduce ambiguity and improve retrieval, classification and reasoning. The session will close with practical guidance on keeping these guardrails current as business language, systems and AI use cases evolve.
For forty years, every data decision meant picking a side: operational or analytical, governed or self-service, relational or unstructured, aggregated or federated. AI did not add a fifth choice. It made all four unavoidable at once, while inference costs quietly crowd out the software budgets that used to fund everything else.
In this session, Matthieu Jonglez argues that the winners of the AI era will not own the biggest model or the largest data lake. They will own the control plane: the semantic, governed, agentic layer that sits above fragmentation instead of replacing it, and turns scattered data into context, and context into decisions a business can trust and afford. The winner will not be the platform with the most data, but the platform that can deliver the right context to both humans and agents alike, on equal footing. Drawing on production systems running at billions of documents and the latest analyst research, he shows why the real question is no longer build or buy, but orchestrate or be orchestrated.
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.
AI initiatives often face a tradeoff between accuracy, transparency, and governance. While knowledge models and taxonomies provide trusted business context and explainability, modern AI offers powerful capabilities for classification, matching, and discovery. This session explores how these approaches can be combined to deliver more trustworthy and effective AI systems.
The Progress Data Platform AI Data team will present exploration in three areas: Concordance – An evaluation of entity relationship generation using classical OWL/SKOS knowledge models and Semaphore-style extraction techniques. We will share benchmark results, findings, and lessons learned on creating reliable semantic foundations for AI. Classification – A cross-validation approach that combines client data and OWL/SKOS taxonomies with embeddings and Small Language Models (SLMs) to evaluate how structured knowledge and AI can improve classification quality and confidence. Matching – An exploration of hybrid matching techniques that combine transparent lexical matching with contextual embeddings. By reinforcing semantic search with deterministic validation, we assess how missing concept and entity relationships can be identified while maintaining traceability and trust.
Attendees will gain insight into emerging approaches that blend symbolic AI and modern AI to improve the accuracy, and governance of future enterprise AI solutions.
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.
AI is changing not only how enterprises analyze information, but how decisions are made, actions are automated, and knowledge is consumed by both people and intelligent systems. In this session, Eudald Camprubí and Sergei Sokolov will share Progress' perspective on the evolution of enterprise data platforms, exploring why the greatest barrier to AI adoption is no longer access to data or models, but the ability to create trusted, governed, and explainable context across fragmented information landscapes. Drawing on customer challenges across financial services, manufacturing, life sciences, media, and the public sector, they will outline the principles, architecture, and roadmap behind the Progress vision for enterprise intelligence. Attendees will learn how PDP Studio represents the next step in the platform's evolution while continuing to build on the strengths of the current PDP portfolio. Our strategy is to support customer AI journeys allowing for adoption of new platform capabilities at their own pace, leveraging existing products, deployments, and expertise while gaining access to emerging AI-native experiences where they create the most value. The roadmap discussion will provide clarity on how these worlds coexist, how Progress plans to evolve the platform over time, and what customers should expect as the strategy unfolds
Organizations are looking for practical ways to unlock more value from their enterprise data while preparing for an AI-driven future. This session explores how Progress Data Platform makes data more accessible, actionable, and AI-ready through a combination of intelligent automation and AI-powered capabilities.
Using real-world scenarios, the session demonstrates how workflow automation streamlines data-driven processes, orchestrates enrichment and integration across systems, and reduces the effort required to operationalize data at scale. It also introduces the vision for Model Context Protocol (MCP) in MarkLogic, illustrating how AI agents and applications can securely interact with enterprise data using natural language and how these capabilities can be extended through custom tools and integrations.
The session highlights solutions available today alongside a preview of upcoming innovations designed to simplify development, improve operational efficiency, and expand access to data-driven insights. Together, these capabilities showcase how Progress Data Platform is evolving to accelerate AI adoption, enable self-service intelligence, and help organizations turn enterprise data into measurable business outcomes.
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.
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 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.
Every enterprise is under pressure to make better use of their institutional knowledge. Critical information is spread across documents, applications, databases, subject matter experts, operational systems, and years of accumulated experience. Traditional search and standalone AI tools struggle to connect that information, explain where answers came from, or turn knowledge into reliable business outcomes. PDP Studio delivers a fully integrated experience of the Progress Data Platform capabilities. It provides a governed knowledge foundation for the enterprise by bringing together structured data, unstructured content, semantic models, knowledge graphs, search, analytics, and AI-assisted workflows. It helps organizations move beyond fragmented information and generic AI responses toward trusted, explainable, and reusable enterprise intelligence.
Artificial intelligence has evolved from a research breakthrough into a transformative force reshaping how organizations operate, compete, and innovate. In this session, Microsoft will explore the evolution of generative AI, the market forces driving adoption, and how we see the next wave of AI innovation unfolding. Attendees will gain insight into Microsoft's perspective on responsible AI, building trust into AI-powered experiences, and the importance of pairing cutting-edge technology with deep domain expertise. The session will also highlight the strategic partnership between Microsoft and Progress, showcasing how Progress combines market-leading data platform capabilities and industry expertise with Microsoft's AI and cloud technologies to deliver practical, business-focused solutions.
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.
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.
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.
Planning a MarkLogic 12 upgrade or looking to accelerate your team’s learning? Join Progress Customer Success/Support and Professional Services leaders for an informal roundtable focused on your questions, priorities, and goals on MarkLogic 12 upgrade support, PDP Academy, and available education resources in a small-group, open conversation.
