Data is the fastest growing asset for every enterprise, and traditional rule-based systems are not delivering intelligent, context-aware security that adapts at speed to evolving threats while reducing operational overhead. Join Ash Hunt to explore how enterprises are revolutionizing data security through the transformative power of artificial intelligence. Drawing from extensive experience protecting Fortune 500 companies, Ash will share how AI-native solutions are powering real-time threat detection and automated risk remediation across complex cloud environments.

In the face of siloed systems and fragmented processes, Helvetia, a leading international insurance group, sought to increase efficiency and innovation. Discover how improving the integrity of location data, centralising it on a scalable platform, and automating workflows led to significant business transformation. With streamlined spatial operations integrated seamlessly with pricing and underwriting, Helvetia eliminated manual work, accelerated regulatory response, revealed new cross-sell opportunities, and enabled greater AI relevance and accuracy.

AI-powered decisioning is here—but why aren’t enterprises fully leveraging its potential? Many organizations still struggle with fragmented decisioning engines, inefficient workflows, siloed data, and lack of AI governance.

Join us for an engaging discussion with your peers. We’ll tackle the real barriers to AI-driven automation, explore strategies for integration, and explore how organizations are achieving measurable impact through seamless, scalable decisioning.

AI-powered Data Engineering Agents usher in a new era of data agility. Engage with Google Cloud and your peers to explore the implementation of autonomous data agents and their impact on enterprise agility. From automating data pipelines to ingestion to transformation, discover how to leverage autonomous data agents to build self-managing data ecosystems and accelerate the time from raw data to impactful decisions. This is where data's potential truly meets AI power.

Enter the agentic era of data and analytics with Tableau and Agentforce. Discover how AI agents are accelerating data modeling and unlocking conversational analytics. Hear how leading organizations are harnessing agents to reimagine decision-making, supercharge insight delivery, and unleash the full potential of their data-driven workforce.

Join us for an insightful session where biopharmaceutical leader Pfizer shares valuable lessons learned during the development of their global SMART application suite, which empowers representatives to deliver more patient-centric care. With a mission to create breakthroughs that change patients’ lives, the SMART application suite supports over 20,000 pharmaceutical sales employees across dozens of countries. Deploying at this scale required not just advanced technology, but also transformative ways of working. The result? A threefold increase in the adoption of previous solutions.

Rapid changes demand innovative decision-making tools beyond traditional methods. Businesses are turning to AI, BI, and data science to gain a competitive edge. The perfect blend of these technologies can be a true differentiator.

- Take a quick look at what to expect from this session
- Challenges in data and analytics today
- Unlocking the power of AI, BI, and data science
- The transformative role of AI-powered self-service BI platforms
- Live demos of next-generation analytics in action

Learn how these innovations can drive better decisions to deliver transformative business outcomes.

Whether you are new in your role or a seasoned veteran, you’re likely being asked how to extract more value from your analytics investment. In this session, we will review how to embed AI and conversational analytics into the flow of users’ daily lives to drive adoption and improve overall customer end-user experience. Putting the power of true self-service into the hands of all users, both internal and external, has never been easier and is now a requirement more than ever.

Data and AI are shifting industries and reshaping society, but with great power comes the need for robust governance. In this session, we will focus on governance in data and AI, highlighting why it is a strategic priority for organizations worldwide.

Join this session to discover how trusted AI governance with SAS Viya ensures transparency, ethics, and performance—without slowing innovation. Walk away with real-world strategies to keep your AI trustworthy, efficient, and impactful. Don’t let bad AI decisions cost you.

AI architects are challenged to define a technology roadmap in an environment that is currently defined by a rapid pace of AI technology, specifically with generative AI initiatives, evolving product capabilities, needs for up-skilling, all of this with the goal of improving business outcomes. This session will provide the framework, architectures and tools to define such a roadmap.

Traditional approaches and thinking around data quality are out of date and not sufficient in the era of AI. Data, analytics and AI leaders will need to reconsider their approach to data quality going beyond the traditional six data quality dimensions. This session will help data leaders learn to think about data quality in a holistic way that support making data AI-ready.

Metadata, data quality and data observability tools provide significant capabilities to ensure good data for your BI and AI initiatives. Metadata tools help discover, and inventory your data assets. Data quality tools help business users manage their data at sources by setting rules and policies. Data observability tools give organizations integrated visibility over the health of data, data pipeline and data landscape. Together the tools help organizations lay good foundation in data management for BI and AI initiatives.

In this session, we will explore DeepSeek’s transformative impact on AI development, highlighting its potential to enhance enterprise innovation at a price/performance that is unprecedented. Data, analytics & AI leaders can bring their questions on how they can capitalize on new training and inferencing paradigms in AI, the future of AI model development and the impact of reasoning models on a more autonomous future in AI.

With new pressures coming from AI, CDAOs must be agile in implementing and enabling business innovation. But, with quick adaptation, inevitably comes stress on traditional D&A delivery processes and practices. Join this interactive session to lean how to: clarify strategies to support business innovation, design adaptable and scalable delivery models and collaborate with business units to ensure value of D&A capabilities and services.

Data and analytics leaders need to support the opportunities and challenges of today’s digital business with the right competencies. This is the time to evaluate data and analytics roles and skills that are fit for now and the future. This session will provide key considerations for D&A and AI roles and skills.

Find out about the emerging, most hyped and ready for prime-time concepts and technologies in data, and analytics, and AI programs and practices.

Moving AI projects from pilot to production requires substantial effort for most enterprises. AI Engineering provides the foundation for enterprise delivery of AI and generative AI solutions at scale unifying DataOps, MLOps and DevOps practices. This session will highlight AI engineering best practices across these dimensions covering people, processes and technology.

Data ecosystems, built on data fabric design and infused with AI, promise an integrated, cost effective, and operationally simple approach to varied data management challenges. However, they don't yet always deliver on that promise. This research explores the maturity of various ecosystem components and provides a guide for D&A leaders and others looking to invest in data foundations for competitive differentiation.

Join this session to get oriented about where you are on the AI maturity curve and develop the next steps to achieve your goals. Explore AI maturity by addressing its building blocks such as use case selection, technology operationalization and AI adoption.