CDAO Government schedule

• Transforming defense logistics through data: leveraging enterprise systems to improve visibility, forecasting, and decision-making across the supply chain
• Modernizing IT infrastructure: advancing cloud, data platforms, and digital services to support mission-critical logistics operations
• Driving efficiency and resilience: using data and analytics to strengthen supply chain agility in contested and dynamic environments

• Translating federal data strategy into operational impact by aligning data governance, analytics, and mission priorities across agencies
• Building sustainable data ecosystems that enable agencies to move from fragmented datasets to enterprise-wide insights and decision support
• Overcoming common barriers to data adoption, including cultural change, workforce development, and cross-agency collaboration
• Lessons learned from serving as a CDAO across multiple  

• How are federal agencies operationalizing AI, advanced analytics, and data to support mission outcomes and improve decision-making across government?
• What progress has been made in building secure, interoperable data environments that enable collaboration across agencies, partners, and the defense enterprise?
• How are federal leaders addressing the challenges of scaling AI—from pilot projects to mission-ready capabilities—while maintaining security, governance, and trust?
• What role do partnerships with industry, academia, and allied nations play in accelerating innovation and strengthening the federal digital ecosystem?
• Looking ahead, what policy, acquisition, and workforce changes will be most critical to enabling government to harness emerging technologies at speed and scale?

• Moving from strategy to execution by building enterprise data platforms that support multiple agencies and mission areas
• Breaking down silos through shared data infrastructure to enable more coordinated, citizen-centered service delivery
• Lessons from implementing large-scale, cross-agency systems, including governance, integration, and stakeholder alignment
• Sustaining long-term transformation by aligning technology, policy, and operations to deliver measurable outcomes

• Translating AI and machine learning initiatives into practical tools that improve government operations and decision-making
• Building the data infrastructure and governance frameworks needed to support scalable AI adoption across agencies
• Overcoming organizational and workforce challenges associated with integrating AI into public sector environments
• Identifying high-impact use cases for AI that enhance service delivery, efficiency, and long-term modernization efforts across state government

• Translating AI strategy into practical applications that improve safety, efficiency, and mobility across transportation systems
• Leveraging data and AI to enhance infrastructure planning, traffic management, and operational decision-making
• Integrating AI into complex, multi-modal environments while addressing interoperability, governance, and workforce challenges
• Scaling AI initiatives from pilot programs to enterprise adoption to deliver measurable impact across the transportation ecosystem

• Organizations must build strong data foundations (quality, metadata, lineage, governance) to achieve true AI readiness.
• AI‑ready data requires standardization, interoperability, provenance, and automation of quality checks to support trustworthy AI.
• Federal urgency (EO 14110, OMB, NIST RMF) and rapid AI adoption make modernization essential now, not later.
• AI readiness depends on four pillars: data maturity, modern architecture, governance/risk management, and workforce skills.
• Agencies should focus on high‑impact, low‑risk use cases like document summarization, workflow automation, enterprise search, and anomaly detection.
• Success requires a structured 18‑month roadmap, strong KPIs, secure AI sandboxes, and a culture that supports responsible AI adoption.

• Strengthening data stewardship, governance, and data quality to support reliable AI and analytics across government
• Building interoperable and trusted data environments that improve collaboration and decision-making within public health and regulatory ecosystems
• Balancing innovation, transparency, and responsible data management in the adoption of AI technologies
• Leveraging modern data practices to improve operational efficiency, scientific research, and mission outcomes across the public sector

• Leveraging data and AI to improve public health outcomes and strengthen service delivery across communities
• Enhancing coordination across agencies and programs through better data sharing and interoperability
• Balancing innovation with governance, privacy, and trust in the adoption of AI within health and human services
• Building scalable digital capabilities that support more responsive, efficient, and citizen-centered government services

• Leveraging enterprise data to strengthen financial operations, transparency, and accountability across government
• Enabling data-driven decision-making to support fiscal management, payments, and program delivery
• Building modern data infrastructure to integrate legacy systems and support real-time insights
• Aligning data strategy with mission priorities to improve efficiency, oversight, and public trust

• Establishing governance over data pipelines to ensure reliability, consistency, and trust in financial data
• Breaking through organizational barriers to access and integrate mission-critical data across agencies
• Synthesizing complex datasets into standardized formats that enable a shared, consistent understanding across stakeholders
• Leveraging enterprise platforms such as Advana to align financial data and support more cohesive, data-driven operations

• How can industry support the Department of Defense in building resilient, cyber-secure digital architectures that enable AI-enabled operations and joint battle networks?
• What acquisition and partnership models are most effective for delivering cyber and AI capabilities to the warfighter at the speed of modern threats?
• How can industry and government embed security, zero trust principles, and resilient system design into next-generation defense platforms and data ecosystems from the outset?
• What approaches are needed to manage risk, governance, and trust as AI systems, software-defined capabilities, and autonomous technologies become central to defense operations?

• Enabling cross-government collaboration: aligning data and AI strategies across U.S. agencies and international partners
• Interoperability through partnerships: overcoming barriers to data sharing, standards, and integration across borders
• Building the workforce: developing talent and training programs to support data-driven government operations
• Lessons from international engagement: best practices for coordinating across diverse organizations, missions, and governance frameworks

• Accelerating the transition of AI and emerging technologies from research environments into operational mission capabilities
• Strengthening collaboration between government, academia, and industry to drive innovation across national security ecosystems
• Leveraging data, advanced analytics, and applied research to support decision advantage in complex mission environments
• Building scalable and trusted frameworks for responsible AI adoption across defense and intelligence organizations

• Using data and AI to improve decision-making and strengthen mission-driven initiatives across communities
• Building scalable data strategies that support more effective operations and measurable organizational outcomes
• Leveraging analytics and digital transformation to better understand community needs and improve service delivery
• Ensuring responsible and inclusive adoption of AI technologies across public-facing and mission-oriented organizations

• Leveraging data science and AI to support complex mission environments and accelerate scientific and operational discovery
• Transforming large-scale datasets into actionable insights that improve decision-making and mission performance
• Overcoming challenges related to data integration, scalability, and advanced analytics in high-volume environments
• Exploring the future role of AI, automation, and predictive analytics in driving innovation across government and research organizations

• The role of open standards in enabling interoperable geospatial data ecosystems across federal, state, and international partners
• Supporting AI adoption: ensuring geospatial data is accessible, usable, and aligned with modern analytics and AI workflows
• Cross-agency and cross-domain integration: advancing data sharing and interoperability across government organizations
• Partnering with industry and academia: accelerating innovation through standards-based collaboration and open, nonproprietary frameworks

• Applying AI to enhance aviation safety, situational awareness, and decision-making across increasingly complex airspace
• Leveraging data and machine learning to improve air traffic management, predictive maintenance, and operational efficiency
• Integrating AI into mission-critical systems while ensuring reliability, certification, and regulatory compliance
• Scaling AI capabilities across the aviation ecosystem to support modernization and the future of air mobility

As artificial intelligence transitions from experimentation to operational capability, defense leaders must address how AI systems can be deployed responsibly and at scale. This roundtable will explore the challenges of integrating AI into real-world defense missions, building trust in AI-enabled decision support, and aligning data, testing, and governance frameworks to ensure mission readiness.

Data is increasingly central to decision advantage across the Joint Force. This discussion will examine how defense organizations are building interoperable data environments that enable advanced analytics, AI applications, and faster operational insight. Participants will explore challenges around data governance, accessibility, and integration across services, agencies, and coalition partners.

Cloud computing is becoming foundational to defense digital transformation, enabling scalable computing, secure data environments, and advanced AI capabilities. This roundtable will explore how defense organizations are leveraging cloud architectures, edge computing, and hybrid environments to support mission systems, enhance operational resilience, and deliver digital capabilities at speed.

 Despite significant investment in innovation, many promising technologies struggle to transition into sustained use. This roundtable will explore how defense organizations can overcome the “valley of death” by aligning requirements, acquisition pathways, and operational needs 

 As AI becomes increasingly embedded in decision-making processes, ensuring effective human–machine collaboration is critical. This roundtable will examine how defense organizations can design systems that enhance operator performance while maintaining trust, transparency, and accountability. 

As defense systems become more digitally integrated, they are increasingly exposed to sophisticated cyber threats. This roundtable will explore how organizations can strengthen resilience across networks, platforms, and data environments while enabling secure information sharing.

In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.

To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.

AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:

• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks

Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.

In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.

To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.

AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:

• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks

Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.