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.

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.

As organizations scale GenAI from concept to production, they face challenges like ensuring accuracy, explaining responses, and connecting GenAI to unique knowledge. This session shows how GraphRAG combines knowledge graphs with retrieval-augmented generation to build GenAI apps grounded in enterprise data. Learn how companies like Klarna have deployed GenAI to build chatbots grounded in knowledge graphs, improving productivity and trust, while a major gaming company achieved 10x faster insights. We'll share real examples and practical steps for successful GenAI deployment.

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.

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.

Productivity and operational efficiency are one of the key measures of business performance and economics. GenAI has promising capabilities of improving productivity and operational efficiency of data management function, and data governance. Organizations should explore and assess those capabilities to align it with strategic goals to improve the productivity and operational efficiency.

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.

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

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.

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.

As enterprises embrace GenAI and intelligent agents, securing sensitive data—like PII, financial records, and IP—while maintaining compliance is crucial. This session explores how Skyflow helps meet modern privacy demands, including India’s DPDP Act, using polymorphic encryption, tokenization, consent management, and fine-grained access controls. See real-world architectures that show how to embed privacy into both legacy and AI-first systems, enabling innovation without compromising security or regulatory compliance.

CDAOs and AI leaders are grappling with two crucial questions: 1. What public cloud provider should we choose for AI and GenAI initiatives, and 2. how do we assemble the right cloud architecture to scale and deploy AI more effectively?
This session compares public cloud AI and Generative AI architectures from AWS, Azure and GCP and provides insights on their points of differentiation.

Creating a business case for GenAI is hard. GenAI initiatives produce outcomes that range from everyday to game-changing and cannot be described by a single currency of value. Gartner has identified three business case types and recommends CIOs create a value portfolio that encompasses the variety of GenAI investments enterprises make. Come to this session to learn how to frame your GenAI business case and align executive expectations on value creation from GenAI.

The emergence of Agentic AI will revolutionize automation and efficiency gains cutting across businesses. However, challenges like hallucination and inconsistency hinder the immediate adoption of GenAI models. Utilizing Agentic AI to tame the inherent variability of unstructured data, which is at the core of every white collar work, is the basic step to exploit larger AgenticAI automation in enterprises. This is based on our experience of designing hundreds of automation solutions. We will showcase a few use-cases in this session.

The fourth industrial revolution is fueled by AI and data. This talk will describe how to create and consume data products leveraging capabilities of Generative AI. Empowering knowledge workers with self-service access to data is an essential capability of a modern enterprise. Robust metadata and a framework for Minimum Viable Governance (MVG) is at the heart of trusted AI. The use of Generative AI to automate creation of data products will be discussed along with critical techniques to ensure integrity of the data product design. The use of natural language interfaces for interrogating data products will also be discussed.
-Learn about the anatomy of data as a product in support of corporate AI initiatives.
-Learn about the concept of Minimum Viable Governance to drive the right balance between agility and governance.
-Learn how business users can be empowered to create data products through a scalable manufacturing process using Generative AI.

CDAOs are making investments daily. Perhaps you're looking to grow your team, or maybe making a technology investment to support GenAI, or another investment where you need to build buy-in and gain funding. This workshop will help you develop personal influence skills while also building a strong story for investment.

Philips, with its legacy of innovation and diverse AI portfolio, recognized the potential of Large Language Models. To enable scalable and responsible Generative AI adoption, Philips established the "AI Foundation." This multi-disciplinary team provides Generative AI as a service, addressing, awareness and education, rapid iteration, enterprise data integration, platform experiences, and robust compliance. Learn how The AI Foundation empowers Philips teams to build innovative, Generative AI-powered solutions at scale through foundational services and a governed platform.

Generative AI costs can rapidly escalate due to poor architectural decisions, lack of operational know-how and inadequate governance. In this session, we outline the top ten best practices that D&A leaders can adopt to optimize costs, enabling them to achieve quicker business value and operational efficiency.

The rapid advances in generative AI are fueling great excitement. Within just a few years, one-third of generative AI interactions are expected to utilize autonomous agents, propelling a new wave of productivity for enterprises. However, this potential can only be realized if the challenges surrounding AI trustworthiness, inferencing costs, domain-specificity, and effective and secure leveraging of quality enterprise data can be overcome. Data and AI leaders require a practical approach to accelerate AI adoption. Discover actionable techniques to maximize the value of your data for AI, learn from real-world examples of data and AI driven innovation in defence and aerospace, and gain insights into fostering greater AI productivity across your teams with IBM watsonx.

The Bank of India is redefining trust through the power of data. Join its Analytics Head as they share how AI, real-time analytics, and predictive insights are transforming security, transparency, and customer experience. Discover how a legacy institution is embracing agility to lead the future of banking.
In addition - see how organizations unlock value with SAS Viya — achieving over 100x performance gains and half the cost in compute and storage costs when modernizing SAS 9 environments. It will explore how Intelligent Decisioning and Generative AI are integrated with data and models to automate decisions and drive stronger business outcomes.

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.

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.