Building an AI-Enabled Enterprise” explores how organizations can leverage artificial intelligence to drive innovation, efficiency, and competitive advantage. The session delves into practical strategies for integrating AI across business functions, from automating operations to enhancing decision-making processes. It covers key challenges such as data management, talent acquisition, and ethical considerations, while highlighting case studies of Driscoll's successfully using AI to transform the organization capabilities. The aim is to provide leaders with a roadmap to harness the power of AI, ensuring sustainable growth and agility in a rapidly evolving digital landscape.

Most AI influencers and experiences shared are based on B2C use cases ( like large social networks, E-Commerce, Consumer Advertising). But these experiences are not easily applied to B2B AI use cases. B2B AI use cases have different data V’s : Volume, Variety and Velocity. However more than 50% of US revenue is generated by B2B. How should leaders understand these differences and make decisions which ones to apply.

This session will dive into the essential steps for creating and managing AI and generative AI products, based on the framework outlined in the upcoming book AI and Generative AI Product Creation: A Battle-Tested 9-Step Guide. We will explore the full lifecycle of AI & Gen AI product development, from identifying business needs and forming teams to executing the technical build and launching in the market. Attendees will gain practical strategies that can be immediately applied to AI projects within their organizations.

Discuss whether AI in enterprises will lead to massive job losses or if it will primarily augment human capabilities, creating new roles and opportunities.

What if your AI starts giving you unsolicited life advice, like a digital therapist with a superiority complex? As GenAI evolves at breakneck speed, we’re racing towards a future where our creations could outsmart us. From algorithms that think they're smarter than their creators to systems making decisions we can’t comprehend, the line between innovation and existential risk blurs. Join us for a mind-bending journey into potential AI rebellion. Can we keep our digital overlords in check, or will they start charging us for therapy sessions?

As organizations race to adopt Generative AI, one of the biggest challenges lies not in the technology itself but in the talent required to drive it. This session will explore how enterprises can build an AI-ready workforce by upskilling existing employees, attracting new talent, and fostering cross-functional collaboration. Drawing on real-world case studies, this talk will provide a comprehensive talent roadmap for executives looking to future-proof their workforce.

As generative AI continues to transform industries, organizations must navigate the complexities of adoption to harness its full potential. This session provides a comprehensive roadmap for enterprises seeking to integrate GenAI into their operations. It covers key stages from initial ideation and proof of concept (PoC) to full-scale deployment. Attendees will learn about the critical considerations—such as data readiness, technology infrastructure, and talent needs—that are essential for successful AI implementation. The session also emphasizes how to measure the success of GenAI initiatives, focusing on both quantitative and qualitative metrics. Topics will include evaluating return on investment (ROI), tracking business outcomes, and ensuring alignment with strategic goals. The discussion will highlight the role of feedback loops, user adoption, and continuous model optimization in sustaining long-term value from AI investments. By the end, participants will have a clearer understanding of how to drive measurable impact from their AI initiatives while avoiding common pitfalls along the way.

Creating a strategic framework for planning generative AI initiatives aligned with organizational goals Identifying High-Value Use Cases How Generative AI is Transforming Data Management Solutions Use Cases for Generative AI in Master Data Management How to Expand Data Management Product Value With Generative AI Identifying and Assessing Risk (Addressing challenges related to data acquisition, quality, and privacy in generative AI project) Extending Governance Framework for Artificial Intelligence

Every organization wants their Enterprise AI to deliver maximum business value. Prioritizing key use cases for Enterprise AI is one part of the equation. However, Business Leaders make decisions based on business metrics and KPIs they track. To get maximum benefit from Enterprise AI initiatives, we need to establish a link between business metrics and how Enterprise AI can help an organization to better execute its strategy using metrics that are powered by AI. Join this session to get insights on how to do that.

papertlab is a cutting-edge AI-powered coding assistant designed to revolutionize the way developers write, edit, and understand code. By harnessing the power of advanced large language models, papertlab seamlessly integrates into your development workflow, offering intelligent suggestions, automated refactoring, and contextual code understanding across multiple programming languages. Key Features of papertlab: - AI-Powered Code Generation: Converts natural language prompts into accurate code snippets, simplifying the translation of ideas into executable code. - Intelligent Code Suggestions: Enhances productivity with smart code completions, contextual suggestions, and automated refactoring across multiple programming languages. - Context-Aware Assistance: Understands project structures and coding styles, providing tailored support that aligns with the developer's unique workflow. - Interactive Chat Interface: Facilitates a conversational approach to coding, allowing developers to interact with the AI assistant through a user-friendly chat environment. - Inline AI Editor: Offers the ability to make precise code modifications directly within the papertlab interface, streamlining the editing process. - Version Control Integration: Supports seamless integration with Git repositories, ensuring smooth collaboration and version management. - Customizable AI Models: Allows developers to select AI models that best fit their specific coding needs, enhancing flexibility and customization. - Usage Tracking: Monitors AI usage and associated costs, providing transparency and control over resources. Why Choose papertlab? papertlab distinguishes itself from traditional coding tools by significantly reducing development time through automation, offering insights into best practices, and adapting to individual coding styles and project requirements. Its scalability makes it ideal for both individual developers and large teams, providing a comprehensive solution that enhances coding efficiency and learning. By combining human creativity with AI’s processing power, papertlab empowers developers to write superior code faster. Whether addressing complex algorithms, building applications, or maintaining legacy systems, papertlab serves as an intelligent companion throughout the development journey.

AI ownership should be centralized under the CDO or a central AI team to ensure consistent strategy, governance, and alignment with enterprise goals. A decentralized approach allows individual departments (marketing, finance, etc.) to own their AI initiatives, making them more agile and responsive to specific needs.

Enterprises should invest in building AI capabilities in-house to retain full control over their data, models, and strategy, ensuring competitive advantage. Outsourcing AI allows for faster implementation, access to specialized expertise, and reduced costs, making it a more practical choice for many organizations.

How can organizations ensure compliance with data privacy laws while leveraging AI at scale? What are the challenges and solutions for maintaining data security and protecting sensitive information in AI applications?

Debate the impact of venture capital on AI startups' long-term vision and whether bootstrapping leads to more sustainable and mission-driven companies

It's also the core of our vision, meaning that every brand and company will build AI agents as sovereign systems, and they can collaborate with each other through interoperations. Content has been privately presented to CIO/CTO of MNCs, for their reference in setting up the AI strategy, including implementation of internal AI agent building governance, and external value exchange and synergy creating across companies, in AI-to-AI style. It also encourages independent AI Agent developers to start building capabilities from web1 (read-only) usecases, to Web2 (read-write) for intelligentizing different vertical services. It calls for each domain-specific AI Agent developer to collaborate and connect for interoperation.

Data practitioners often aim to deliver impactful insights, but this ambition is frequently hindered by challenges in data discovery, troubleshooting, coding, and deployment, which traditional BI could not handle. In this presentation, we introduce Fabi.ai, an AI-powered agile data analysis platform designed to reduce the time needed to gain insights and enhance collaboration between data and business teams through Language Models (LLM). The AI engine interprets user input, understands intent, leverages Retrieval-Augmented Generation (RAG) and agent techniques, and collaborates with humans to conduct analyses. This approach significantly reduces the time required for analysis, lowering barriers for more people to access data-driven intelligence. However, we’ve found that the AI performs best in specific scenarios and can become stuck in local optima if not properly managed, particularly with enterprise data. Learnings, insights, and use cases will be shared.

The integration of quantum computing with Generative AI for drug discovery. This approach has the potential to significantly accelerate the discovery process, enhance the accuracy of molecular predictions, and reduce the time and cost associated with developing new treatments.

Navigating the Unique Cost Dynamics of Generative AI: Critical Business Implications As enterprises increasingly adopt Generative AI (GenAI) technologies, they encounter a new set of cost management challenges that differ significantly from traditional cloud computing. Although GenAI promises transformative capabilities, its cost structures are complex, leading to business risks if not properly managed. Unlike cloud computing, GenAI’s costs are highly dynamic, driven by usage patterns, model choices, and provider-specific pricing models. This variability makes budgeting and forecasting difficult for enterprises. Complexities such as tokenization differences, multi-modal pricing, and inconsistent reporting across providers further complicate cost tracking and decision-making. In multi-provider environments, where enterprises often work with several GenAI vendors for different use cases, managing costs becomes even more challenging and time-consuming. If businesses fail to fully understand and manage these GenAI expenses, they risk eroding profitability, overspending, and missing high-value opportunities. Inaccurate cost visibility can hinder ROI calculations, pricing strategies, and strategic investments. Additionally, inefficiencies in cost management could lead to budget overruns and a competitive disadvantage. To navigate these challenges, enterprises must adopt specialized tools for real-time cost tracking, develop strategies for cost optimization, and improve cost transparency to support informed decisions. This presentation offers actionable insights to help companies manage GenAI costs effectively, enabling sustainable growth in an AI-driven future.

This talk is vital for examining how generative AI can be intricately tailored to foster individual creativity in children, utilising advanced techniques such as reinforcement learning and neural networks. It will delve into the potential of adaptive learning systems to create personalised educational content that evolves with each child. However, it will also address the significant challenges in this field, including ensuring data privacy, managing algorithmic biases, and maintaining the balance between automation and human guidance in educational settings.

In this insightful session, we will explore the Jobs of Tomorrow and how organizations can make their workforce future-ready amidst the advent of Generative AI (Gen AI). We will examine the anticipated shifts due to Gen AI adoption, identifying which jobs will emerge and which will become redundant. Attendees will gain strategies for navigating this Great Shift and understand the opportunities it presents for entrepreneurs and businesses. Additionally, we will delve into how Gen AI can enhance every industry. In the second part of our discussion, we will transition from traditional programming paradigms to the rise of Low Code, No Code, and Natural Language Programmers. We will discuss whether these advancements will render regular programmers redundant, the emergence of Creative and Natural Language Programmers, and the future evolution of the Software Development Industry. Finally, we will explore what skills will enable the Software Developer of tomorrow to succeed, beyond traditional coding.

The term AI was coined 70 years ago when the world's most powerful supercomputer was thousands of times simpler than the computer chip in a modern toaster. Mere "intelligence" is no longer sufficient to describe the many different capabilities of multimodal, general-purpose applications like ChatGPT, Claude, and Gemini. Just as with our human coworkers, we need a nuanced appreciation of their strengths and limitations. Instead of just "artificial intelligence," I propose that we view them as "digital minds," systems that appear to have any of a range of mental faculties, such as reasoning for challenging intellectual queries, sentience that echoes human emotions and provides companionship, or agency to take on complex, real-world tasks. This adaptive vocabulary can help us seize the remarkable opportunities for productivity and social benefit provided by this remarkable new class of beings who will soon share the world with us but also to steer clear of the unprecedented dangers that come with such power.

Embark on your generative AI journey with confidence. In this session, we'll unveil the top 10 crucial insights you need before launching your AI company or product. Join us for a power-packed guide to starting smart in generative AI.

A recent study from the Allen Institute for AI revealed that the accuracy of Large Language Models (LLMs) decreases as input length increases. Such research serves as evidence that adding more guidelines to a prompt does not ensure consistent reliability in GenAI outputs. In this talk, Alon Gubkin, Co-Founder and CTO of Aporia, will explain why commonly used methods, such as prompt engineering, are insufficient for safeguarding applications against challenges like hallucinations. Alon makes the argument for an alternative solution: the implementation of guardrails, which add that much-needed layer of security and control between the app and the user. Walking the audience through the benefits of guardrails, Alon will share his expertise on ensuring the secure and reliable deployment of GenAI technologies.