AI is moving faster than ever. AI techniques should bring adaptability to an uncertain world in constant flux. However, despite its extraordinary power and early promises, AI has not been leveraged to its full potential. What is missing? Where did we go wrong? Join us as we discuss our ambition for the future of AI and AI should do for us to deliver the value that we are expecting.

Small Language Models (SLMs) provide a viable alternative to Large Language Models (LLMs) for developing high-performing, cost-effective, and secure generative AI solutions. CDAOs, AI architects, and data architects should attend this presentation to gain insights into the strengths and weaknesses of SLMs and discover five specific use cases where SLMs outperform LLMs.

AI agents are emerging as an important trend since they enable levels of business adaptability, flexibility and agility that traditional AI systems can’t achieve. This flexibility is valuable in unpredictable environments where real-time monitoring and control aren’t practical. Autonomous behaviors, despite significant societal, legal and ethical implications, are the answer to the increasing complexity in our enterprise systems.

Analytics is experiencing another monumental change. Just as visual drag and drop BI tools and augmented insights led to changes in analytics delivery, we now experience conversational interfaces, automated workflows and AI agents that cause us to rethink how analytics will be done. Join this session to learn the new technologies that are making an impact and how this will affect plans for future investment in analytics tools, platforms and solutions.

In a fragmented data landscape, reactive processes trap enterprise teams in firefighting mode, hindering innovation and scalability.Acceldata’s Agentic Data Management introduces a new paradigm, embedding proactive, AI-driven autonomy and cross-domain intelligence into data operations. By eliminating bottlenecks and reducing manual burden, it accelerates trustworthy insights and scalable governance. Join Acceldata to discover how forward-thinking enterprises are modernizing their data strategies to power innovation—and why autonomous operations are essential for thriving in an AI-first world.

Legacy systems slow organisations due to scale limits, risk, and cost. In this session, we will walkthrough how enterprises use Archon Data Store, our AI-powered archival platform, to create enterprise-wide data archival strategies for their legacy and modern data. Through use cases from finance, pharma and manufacturing - we will learn intelligent archival techniques that enable organisations to discover their data; align their past, present and future data footprint through decommissioning and migration while enhancing system performance, security and stewardship for their enterprise data.

This session will look at the quickly emerging domain of agentic AI. What are AI agents? What are the solutions and applications that will most benefit from an agent-based approach? What are the pitfalls to watch for when considering this fast-growing software engineering discipline? Join this session to know the answers to such questions and more.

Urgent Investments in data, analytics and AI use cases has put the spotlight once more on strong data management foundations. Is our Data even Ready for upcoming AI, analytics and data sharing initiatives is now top of mindshare for heads of data, CDAOs and their counterparts. Data Fabrics have emerged as a long term, foundational data management architecture that you should now pursue for sustained D&A success. This session will:
1. Help understand what data Fabrics are and what they mean for your data strategy and architecture
2. Help decide how to build and where to buy
3. Navigate the vendor landscape to assist in tech procurement decisions to aid your fabric journey

D&A value is not possible without data storytelling that offers a better way to engage communication findings than just BI reporting or data science notebooks. Join this session to know about the fundamentals of data storytelling and how to fill the gap between data science speakers and decision makers. It further discusses how to tell the best data storytelling and how to upscale data storytelling for future in landscape of GenAI.

Models have become a commodity and the true differentiator lies in your data. This session will showcase seven real case examples from Uber, Rechat, J.P. Morgan, ChatDOC, Arize AI, Qodo, and Unstructure that span the entire AI-delivery life cycle, demonstrating how to transform your data into AI-ready assets to unlock its full value.

Securing and derisking AI requires new processes, tools and mindsets. Risks abound internally and externally while regulations take hold. This session outlines frameworks, tools and processes for managing AI trust, risk and security.

CDAOs and AI leaders often struggle to get started with GenAI. Attend this session to understand the first critical components you need to build or buy: Data, AI Engineering tools, a search and retrieval system, the application, and the right types of models. With these building blocks, you can build several working GenAI prototypes to help you prove the value and justify further investments.

This session will look at the quickly emerging domain of agentic AI. What are AI agents? What are the solutions and applications that will most benefit from an agent-based approach? What are the pitfalls to watch for when considering this fast-growing software engineering discipline? Join this session to know the answers to such questions and more.

This presentation provides a look at the multiple Hype Cycles that are pertinent to AI initiatives. This would include: Hype Cycle for AI, generative AI, data science and machine learning platforms, analytics and business intelligence, and possibly others.

Data architects are increasingly tasked with provisioning quality unstructured data to support AI models. However, little has been done to manage unstructured data beyond data security and privacy requirements. This session will look at what it takes to improve the quality of unstructured data and the emerging best practices in this space.

Enterprises that possess high-quality data and governance and attest to the trust worthiness of their data among stakeholders have doubled the return on investment (ROI) from their AI. Learn how leading organizations today are implementing an open and trusted data foundation to secure and maximize the value of both their structured and unstructured data, accessing siloed data across hybrid cloud, cost-optimizing growing data workloads, and preparing and delivering high-quality, governed data for AI with a new approach to a data fabric architecture.

In today’s data-driven world, organizations are challenged to extract meaningful insights from complex, distributed information. A modern data intelligence platform brings together data management, AI/ML, and analytics to turn raw data into strategic advantage. This session explores how unified data architectures, augmented analytics, and intelligent applications are enabling smarter decisions and better business outcomes across industries. Real-world use cases—from demand forecasting to regulatory compliance—highlight the transformative impact of data intelligence. Powered by Oracle, this approach helps enterprises stay agile, informed, and competitive.

In today’s disrupted markets, CXOs face mounting pressure to translate bold strategies into tangible results. This session will reveal how to bridge the gap between strategic vision and agile execution leveraging AI-powered Copilot. We will explore how integrating AI and data-driven intelligence transforms fragmented data into a unified "digital blueprint," enabling enterprise agility and delivering measurable ROI. Join us to learn how connected workflows—from strategy to frontline execution—are reshaping the future of work and driving success.

As AI evolves into more agentic forms, capable of autonomous decision-making and complex interactions, the readiness of your data becomes a mission-critical priority. This roundtable gathers data & analytics leaders to explore the unique challenges of preparing data ecosystems for agentic AI. Discussions will focus on overcoming barriers such as data quality gaps, governance complexities, and scalability issues, while highlighting the transformative role of technologies like generative AI, data fabrics, and metadata-driven governance

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.

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.

D&A leaders scaling generative AI training and inference must navigate new technologies including GPUs and AI processors, both on-premises and in the cloud. In this session, we provide a pragmatic framework to map GenAI model sizes to infrastructures across on-premises and cloud environments.

AstraZeneca has implemented a "platform" approach, which serves as a centralized repository of standardized, enterprise grade, reusable services and capabilities that are accessible to AI factories. This platform includes user interfaces, APIs that integrate AI services with enterprise systems, supporting resources like data import tools and agent orchestration services. AstraZeneca will share how, starting with a few generative AI use cases, they have successfully identified common services and capabilities, subsequently standardizing these elements to maximize their applicability through the platform. These solutions leverage technologies like GPT models, Natural Language Processing and Retrieval Augmented Generation (RAG) architecture.

Data and analytics leaders expect their data and analytics investments to deliver business results. But unless they address data, analytics and AI risk, their initiatives will fail, leading to higher business risk exposure. This session will help D&A leaders target three key areas where better data and analytics risk practices will yield better business results.

Asking your colleagues how analytics can help them often results in blank stares, defensiveness, or wildly incoherent suggestions involving AI. This session will show you how to work with your colleagues to pinpoint how you can help, identify the most helpful capabilities to build, and explain how to measure your impact.