Deepak has building AI systems since 2014 starting with a Logistic Regression based model to now building Gen AI based systems in 2025. His talk will feature both the technical, business and human aspects of the AI systems he has built and contrast and compare them over the years. The intention is to peek into what could be possible in the future, keeping the past in mind. 

AI agents have seen increasing adoption across multiple industries as the next wave of AI. 

Existing regulations such as the European Union AI Act also apply to AI agents.

The session is based on Sunil Soares' books that can be found here: https://yourdataconnect.com

This session will cover the following topics:

• Agentic AI governance framework

• Applications with embedded AI

• Associated AI Governance Regulations including EU AI Act

• Agentic AI Governance Platforms

The term 'agentic AI' is all the rage these days, but there's still not much clarity around what it means. We'll walk through the basic building blocks of these agentic AI systems - predictive AI, generative AI, and workflow automation - and discuss why it's harder (and more important) than ever to ensure a trusted, enterprise-grade, and secure data backbone to get the reliable and trusted solutions our end-users are looking for. We'll also touch on market trends where we see the technology and capabilities evolving in the coming months.

In this session, you will learn how to take vibe coding practices and embed them in a safe and secure way to build truly differentiated AI enabled sofware at enterprise scale.

Amid soaring expectations, where is AI currently delivering true impact and what should we be looking to next? This expert-level panel features leading voices from Capco, FTI Consulting, LexisNexis Risk Solutions and Women in Data® unpacking AI’s real-world cross-industry applications.

Our panel of data leaders will explore topics such as explainability, emergent regulation and scalable ethical frameworks by sharing their first-hand insights from real-world learnings. While acknowledging AI’s complexity, the session highlights where technology is delivering transformative results, and what still requires scrutiny.

Powered by: Women in Data®

In the age of hyper-personalisation, AI-powered tools are reshaping marketing by creating deeply tailored consumer experiences. While these technologies drive engagement and conversions, they also open the door to manipulative tactics, data exploitation, and algorithmic biases that challenge consumer trust and safety.

This talk explores the ethical dilemmas surrounding AI-driven marketing strategies, particularly how the line between persuasion and manipulation can blur. We’ll examine real-world examples of hyper-personalisation gone too far, the risks of algorithmic bias, and marketers' responsibilities in ensuring transparency, fairness, and consent.

Attendees will leave with actionable insights on fostering ethical AI practices in marketing—balancing innovation with governance to protect consumers and build trust in an increasingly AI-driven world.

Agentic AI—systems that autonomously set goals, make decisions, and execute multi-step business processes—is transforming the enterprise, unlocking new levels of productivity. But with greater autonomy comes greater risk, as agentic AI amplifies the challenges of traditional and generative AI by increasing agency.

In this session, attendees will learn how to govern agentic AI with trust and transparency, enabling innovation without compromising safety. The speaker will discuss how targeted controls—enabled by the right tools and frameworks at the right time—can keep pace with fast-moving technology. Real-world case studies will illustrate how leading organizations are successfully managing agentic AI to transform workflows, boost productivity, and scale responsibly.

DfE are providing schools and local authorities with world-leading MI and intelligent reporting to take early action and help pupils thrive. With a national view of data, and through collaboration, system integration, smart analytics and responsible AI, we make it easier to spot issues, understand context and drive down absence.

Powered by: Women in Data®

Are AI code generators delivering SQL that "looks right but works wrong" for your data engineering challenges? Is your AI generating brilliant-sounding but functionally flawed results? 

The critical bottleneck isn't the AI's intelligence; it's the missing context.

In this talk, we will put thing in context and reveal how providing AI with structured, deep understanding—from data semantics and lineage to user intent and external knowledge—is the true paradigm shift. 

We'll explore how this context engineering powers the rise of dependable AI agents and leverages techniques like Retrieval-Augmented Generation (RAG) to move beyond mere text generation towards trustworthy, intelligent automation across all domains. 

This limitation highlights a broader challenge across AI applications: the need for systems to possess a deep understanding of all relevant signals, ranging from environmental cues and user history to explicit intent, to achieve reliable and meaningful operation.

Join us for real-world, practical case studies directly from data engineers that demonstrate precisely how to unlock this transformative power and achieve truly reliable AI.

With the pace of change of AI being experienced across the industry and the constant bombardment of contradictory advice it is easy to become overwhelmed and not know where to start. 

The promise of LLMs have been undermined by vendor and journalistic hype and an inability to rely on quantitative answers being accurate. Afterall, what good would a colleague be (artificial or not) if you already need to know the answer to validate any question that you ask of them?

The promise of neuro-symbolic AI that combines two well established technologies (semantic knowledge graphs with machine learning) enable you to get more accurate LLM powered analytics and most importantly faster time to greater data value.

In this practical, engaging and fun talk, Ben will equip you with the principles and fundamentals that never change but often go under-utilised, as well as discussing and demonstrating the latest techniques, platforms and tools so that you can get started with confidence.

Ben will show that far from taking months, data products can take minutes or hours to prepare, publish and start gaining value from, all in a sustainable and maintainable manner.

When Virgin Media and O2 merged, they faced the challenge of unifying thousands of pipelines and platforms while keeping 25 million customers connected. Victor Rivero, Head of Data Governance & Quality, shares how his team is transforming his data estate into a trusted source of truth by embedding Monte Carlo’s Data + AI Observability across BigQuery, Atlan, dbt, and Tableau. Learn how they've begun their journey to cut data downtime, enforced reliability dimensions, and measured success while creating a scalable blueprint for enterprise observability.

75% of GenAI projects fail to scale—not because the models lack sophistication, but because they’re built on fragmented data. If your systems don’t know who they're talking about, how can your AI deliver reliable insights?

This talk unveils how real-time Entity Resolution (ER) is becoming the silent engine behind trusted, AI-ready data architecture. We will discuss how organizations across financial services, public safety, and digital platforms are embedding ER into modern data stacks—delivering identity clarity, regulatory confidence, and faster outcomes without the drag of legacy MDM.

You’ll learn:

• Why ER is foundational for AI trust, governance, and analytics
• Patterns for embedding ER into streaming and event-driven architectures
• How ecosystem partners and data platforms are amplifying ER value
• How to build trust at the entity level—without slowing down innovation

Whether you’re modernizing architecture, launching AI programs, or tightening compliance, this session will equip you to embed trust from the ground up.

75% of GenAI projects fail to scale bc models are built on fragmented data. Learn why entity resolution can help in your data architecture.

Agentic AI is only as good as its data. Predictive AI, generative AI & workflow automation needs trusted, secure data to deliver.

Ethics is often treated like a product feature—something to be added at the end, polished for compliance, or marketed for trust. But what if that mindset is exactly what’s holding us back? In this keynote, we’ll challenge the idea that ethics is optional or external to the development process. We’ll explore how ethical blind spots in AI systems—from biased models to black-box decisions to unsustainable compute—aren’t just philosophical dilemmas, but human failures with real-world consequences. You’ll learn how to spot ethical risks before they become failures, and discover practical tools and mindsets to build AI that earns trust—without compromising on innovation. From responsible data practices to transparency techniques and green AI strategies, we’ll connect the dots between values and code. This isn’t just a lecture—it’s a call to rethink how we build the future of AI—together.

We are entering an Age of Artificial Intelligence with unprecedented opportunities. Companies are integrating AI-driven solutions to enhance efficiency, drive innovation, and maintain a competitive edge. However, prevailing myths about AI create uncertainty in strategic decision-making and adoption. We will discuss four foundational myths in our AI centric world: 1) regulation is an innovation killer; 2) scaling current models will lead to Artificial General Intelligence (AGI); 3) general models create maximum value; and 4) the value of data is unlimited. We will show these myths are delaying AI progress and provide research in overcoming their challenges.

As AI systems become increasingly agentic - acting with more autonomy, initiative, and influence - the need for a new kind of human leadership has never been greater. Technology may accelerate decisions, but it cannot set direction, build trust, or inspire people to change. That’s the role of agentic leadership: intentional, visible, and human-centred. 

Drawing on both current research and her experience leading global data programmes and coaching senior executives, Helen Mannion will show why organisations must evolve their leadership as much as their technology. 

Attendees will leave with clear insight into how they can accelerate AI adoption in organisations, the specific leadership behaviours that make the biggest difference, and practical steps they can take to start building the conditions for AI to succeed.

Join Amperity’s Marcus Owens, Lead Solution Consultant, to learn more about the rapid innovations in data architecture brought by the new wave of AI agents. This session will start with a quick overview of what makes a good AI Agent – and then focus on how Agentic strategies can accelerate two key needs in customer data: 

Make Customer Data Usable – How AI Agents accelerate customer data engineering with Amperity’s Stitch and Chuck Data – saving data engineering teams hundreds of hours of effort. 

Make Use of Customer Data – How AmpAI allows Marketers to build outcome-driven customer journeys, going from intent to results faster than ever before.

Arch Capital Group, a $34 billion S&P 500 specialty insurance leader managing $21.5 billion in gross premiums across 60+ global offices, faced a critical challenge: ensuring data quality and consistency across their complex risk assessment operations. With 25+ predictive models supporting AI-driven underwriting for specialty lines—the industry's most complex and unusual risks—incomplete or inaccurate data inputs threatened the accuracy of critical business decisions spanning property & casualty, reinsurance, and mortgage insurance operations. 

In this session, Sam from Arch Capital shares how the organization partnered with DQLabs to transform their data trust framework, implementing automated quality checks across their global data ecosystem. Learn how this transformation enabled Arch to maintain their disciplined underwriting approach while scaling operations, improve regulatory compliance across multiple jurisdictions, and enhance their ability to respond rapidly to emerging risks while supporting the data accuracy essential for their leadership position in specialty insurance markets.