Join us at the Data & AI Conference Europe 2025 in London from 13–16 October 2025, where industry leaders and experts will explore the future of data management, AI innovation, and cutting-edge technologies.
A practical, hands-on session for both leaders and teams. I share seven attributes of AI-empowered professionals, focusing on how to collaborate effectively with AI tools like ChatGPT. This talk often includes an interactive live-prompting segment that audiences find both fun and immediately useful.
As agentic AI systems evolve from passive tools to active collaborators, organizations must prepare their people—and their data—for this shift. In this keynote, Jan explores the convergence of AI literacy, governance, and modern data architecture as the bedrock of workforce enablement. Delegates will learn how to build AI-ready teams by fostering literacy, trust, and ethical oversight—while also ensuring their data platform can deliver trusted, accessible, and context-rich business data at scale. Drawing on real-world examples, this session provides a roadmap for responsibly deploying agentic AI and upskilling the workforce to thrive alongside it.
Key Takeaways:
• How to foster AI literacy across technical and non-technical roles
• Governance strategies tailored to agentic and autonomous AI systems
• The role of a modern data platform in ensuring accessible, high-quality, trusted data
• Case examples of AI-enabled workforce transformation—what works and what doesn’t
• Practical steps for aligning data, technology, and people in AI-augmented environments
Speakers:
As AI systems grow from passive tools to active agents, the future of enterprise work is no longer just about automation: it’s about co-creation, co-decision, and shared accountability between human professionals and AI copilots.
In this keynote, Donald Farmer explores how AI-human partnerships are transforming how we analyze data, manage complexity, and govern decisions across industries, from medical diagnostics and financial services to logistics and manufacturing.
Rather than focus on workforce displacement or reskilling alone, this talk dives into the operational core of hybrid work: how decisions are made, how data flows through human-AI systems, and how value is jointly created.
With practical examples and actionable frameworks, this session will give practical insights for the following challenges:
• Designing roles and workflows where human insight complements AI pattern recognition
• Building decision systems that are explainable, auditable, and aligned with enterprise values
• Cultivating "analytic dignity”: ensuring people are empowered, not overridden, by intelligent systems
Come prepared to rethink not only the tasks we assign to machines, but the architecture of intelligence that will define effective organizations in the years ahead.
The rapid deployment of generative AI is creating an unprecedented crisis in organizational knowledge management and workforce development. With companies reducing entry-level hiring while increasing mid-level recruitment, organisations are shifting from pyramidal to diamond-shaped structures—eliminating many of the traditional pathways through which expertise develops. This transformation threatens the very foundations of organisational competence. Entry-level roles have historically served as critical learning environments where employees develop tacit knowledge, pattern recognition, and the contextual understanding necessary for quality control and ethical decision-making. Without these opportunities, organizations face a future where no one possesses the grounded experience needed to validate AI outputs or maintain governance standards.
The implications extend beyond individual companies to society as a whole. As AI assumes routine cognitive tasks, we risk creating a generation unable to critically assess, guide, or govern these systems. Research indicates that employees with broad analytical skills may be best positioned to navigate this transition, yet educational systems and corporate learning and development teams remain largely unprepared for this shift. The window for action is rapidly closing: every day of delay compounds the loss of irreplaceable human expertise and increases societal vulnerability to ungoverned AI systems. Drawing on nearly 30 years of experience in data governance, data quality, and digital transformation, adult education and university lecturing, as well as his doctoral research into Human Factors in Data Governance and Hybrid Intelligence, Daragh sets out a manifesto for action.
Dr. Janet Bastiman, Chief Data Scientist at Napier AI, Chair of the Royal Statistical Society’s Data Science and AI Section and member of FCA’s newly created Synthetic Data Expert Group, brings a pragmatic perspective to deploying AI in regulated financial environments. This session explores how financial institutions are harnessing the power of AI while remaining ethical, explainable, and compliant. Drawing on real-world use cases from anti-money laundering and financial crime prevention, Dr. Bastiman will examine the challenges of bias, transparency, and accountability, offering a practical blueprint for implementing responsible AI that aligns with both business goals and regulatory expectations. How to take those steps for other use cases.
Key Takeaways
• Understand the core principles of ethical and responsible AI in high-risk industries
• Learn from real-world applications of AI in compliance and financial crime prevention
• Discover practical approaches to explainability and bias mitigation in machine learning models
• Gain insight into balancing innovation with regulation in financial services
• Explore best practices for AI governance
What does it take to deploy a data platform serving 22 European territories? We built it in just six months, and two years on, I'm sharing the insights. This is a journey filled with challenges and discoveries. I'll be sharing the inside story of how SkyShowtime achieved this ambitious goal, from the initial vision to the realities of large-scale implementation. Expect candid insights, valuable lessons, and a glimpse into how we're leveraging data to fuel SkyShowtime's continued growth and innovation.
Exploring the real-world challenges of improving data quality at scale and drawing on experiences from across the Ministry of Defence, this presentation will highlight both the aspirational goals and the practical solutions that can drive meaningful progress in complex organisational environments.
Key learnings:
• Reality is never the same as the textbook solution
• Break a complex and daunting problem (“improving data quality”) into manageable steps
• The relationship between Data Quality and Data Governance is key
That the world around us is transforming at breakneck speed goes without saying. Organisations clearly need to work overtime to stand a chance of keeping up. For businesses that collect, prepare, store, analyse, and share large volumes of data from multiple sources, leveraging advanced analytics and conducting meaningful data exploitation are key ingredients in achieving that competitive edge. This is where having a modern enterprise data strategy becomes critical. It provides a blueprint of the policies needed to generate business value from an organisation's data assets. But how do we make sure that our strategy not only embraces the technological advancements of today, but is ready to tackle the data challenges that will be brought about by the next big industry disruption?
This talk provides a guided tour of how the data strategy for a FTSE 100-indexed organisation was reimagined from the ground up to improve contextual decision making, optimise business processes, and achieve strategic business goals. It walks through the strategy production approach from start to finish, covering the following topics:
• Vision: how do we define where we want to get to?
• Target State: how do we identify what capabilities we need to have in place?
• Principles: what fundamental tenets should we comply with?
• Missions: what delivery elements do we need to achieve our goals?
• Culture: how must we change to be able to execute our strategy?
• Maturity: how do we assess ourselves as we navigate our data journey?
As enterprises accelerate their adoption of Generative AI, many struggle with the foundation: their data governance strategy. While AI promises innovation, real-world deployments quickly expose cracks in lineage visibility, data quality enforcement, ownership clarity, and regulatory alignment.
In this session, I will share lessons learned from working with large-scale organizations across healthcare, finance, and public sector as they adopt metadata-driven governance frameworks. Drawing from my experience helping launch Amazon DataZone and supporting 8,000+ AWS customers, I’ll walk through practical approaches to setting up scalable governance models, metadata catalogs, and trust-building mechanisms that enable responsible, compliant AI innovation.
I’ll also share real-world challenges — such as domain definition pitfalls, policy enforcement gaps, and the underestimation of cultural change — and how we addressed them through cross-functional collaboration, repeatable templates, and internal enablement programs.
Key Takeaways:
• Proven strategies for establishing metadata-driven governance at enterprise scale
• Lessons learned from working with heavily regulated industries on GenAI adoption
• How to use lineage, glossary, and data quality as trust-building tools
• Common pitfalls in domain modeling and ownership structures — and how to fix them
• Governance blueprints that enable business innovation, not just risk mitigation
In today's deluge of messy and disjointed information, the ability for CDOs and Heads of Data to drive sound judgment and strategic decisions is paramount yet increasingly challenging. This session delves into a powerful framework for navigating this complexity: cultivating the "Essential Trio" within data teams and across the wider organisation.
The session will explore the indispensable roles of the Truthsayer, who unearths critical facts and fosters clarity even amidst uncomfortable truths; the Storyteller, who transforms raw data into compelling narratives that resonate and inspire action; and the Guide, who charts the course forward, empowers self-realisation, and builds enduring trust.
Attendees will discover practical strategies for identifying, nurturing, and empowering these crucial roles within their organisation. They will learn how to build a data-informed culture where data assists, stories align, and guidance empowers, ultimately driving tangible ROI and strategic advantage.
Take-aways:
This session will challenge attendees to consider:
• Whether they are actively championing these roles?
• What concrete steps data leaders can take in partnership with HR and the wider leadership team to embed these capabilities across the organisation?
• What the compelling business case is for investing in this cultural transformation, and whether their HR function is ready to lead this critical change?
Join this session to gain actionable insights and a renewed perspective on how to cut through the noise and empower their organisation with clarity, aligned action, and strategic direction for a data-driven future.
Speakers:
An introductory session focussing on my journey as a neurodiverse (Autistic) individual and leader in the world of Data Quality, Data Management and Governance. Sharing insights that I have gained throughout my journey, the challenges I've faced and key methods I've developed along the way.
The session concludes with how 'out of the box' thinking has assisted me in developing novel ways to monitor and tackle data quality issues across multiple systems in a diverse data landscape.
Key takeaways:
• Why being neurodiverse is a blessing and a curse in the world of Data Quality
• How to focus on the SOLUTION rather than the PROBLEM
• Creative solutions for Data Quality monitoring and improvement across a vast data landscape.
This session explores how data analytics professionals can develop business acumen to drive organizational change. During this session, Laura will share how her team transformed their approach to Customer Experience ("CX") by speaking the language of business (ROI) instead of traditional CX metrics.
Key Takeaways:
• Translate technical metrics into financial impact to gain stakeholder buy-in
• Build cross-functional bridges by adapting your communication to different audiences
• Drive organizational change through data storytelling that resonates with decision-makers
High-performing tech teams don’t just happen - they’re built through trust, clarity, and data-driven interventions. But what happens when those conversations turn tough? In this fast-paced talk, you’ll learn a simple yet powerful framework to handle sensitive discussions with confidence - grounded in behavioral insights and data-driven practices. Discover how to handle cultural complexities, strengthen collaboration, and turn difficult moments into measurable growth. Packed with actionable takeaways, this talk will help you unlock peak team performance - one dialogue at a time.
For obvious reasons, currently the topics of data strategy and AI governance are a priority in most enterprises. However, what exactly is meant by these terms and how the relate to existing enterprise digitalization capabilities is astonishingly unclear. In this presentation we will will show that Enterprise Architecture Management plays an essential role in any IT strategy, and more specifically also for defining data & AI strategies, as well for implementing data & AI governance capabilities. We will illustrate these findings via use cases from the aviation industry with current technologies like Generative AI and Data Mesh.
Data is changing… It’s more local, it’s more unstructured, there’s more of it, it’s used faster and of course it’s produced by AI. Currently, far too much data suffers from a lack of governance and the changing shape of data in the future will only make the problem worse. Hence, we need to find a different approach to data governance and management.
This presentation will:
• Take delegates through the different ways data is changing – where it is coming from, how it is structured and how it is used;
• Show how our current data problems will only get worse and how new data problems will arise;
• Outline how both traditional and new ways of data governance can help organisations tackle these problems.
In a world where AI can cure cancer and spread fake news, the biggest risk isn’t the tech—it’s our assumptions about it. In this session, I’ll walk you through 9 surprising, research-backed stories—from billion-dollar AI blunders to strategic missteps and quiet revolutions. Each story ends with a question, not to test your AI IQ, but to challenge your certainty. These stories are drawn from my work translating cutting-edge AI research into plain English, helping leaders think past the hype and into second-order consequences.
Key takeaways:
• Spot red flags in AI strategy before they become PR disasters
• Evaluate AI products beyond the buzzwords
• Identify real user pain points vs. imagined ones
• Understand the hidden risks in AI training, data, and behaviour
• Build AI teams that think critically, not just prompt better
Speakers:
Artificial intelligence is transforming business operations, yet many organizations struggle with effective implementation. In this session, Olivier Khatib, CEO of DATANEO, will explore how AI-driven automation, predictive analytics, and intelligent workflows are revolutionizing industries by eliminating inefficiencies, reducing costs, and enhancing decision-making.
Attendees will gain insights into real-world AI automation strategies, covering key business functions such as sales, finance, HR, inventory management, customer support, and business intelligence. Using case studies from top enterprises, Olivier will demonstrate how AI accelerates growth, improves accuracy, and enhances agility in rapidly evolving markets.
Key takeaways:
1) AI for Efficiency – How businesses can automate processes to reduce manual work and optimize resources.
2) Predictive Decision-Making – Using AI-powered analytics to anticipate trends and drive smarter business strategies.
3) Real-World Use Cases – Success stories of AI automation in finance, HR, sales, and supply chain management.
4) Overcoming AI Adoption Challenges – Practical steps to integrate AI while avoiding common pitfalls.
5) Scalability & Future Trends – How AI-driven businesses can achieve sustainable growth.
This session is ideal for business leaders, data professionals, and decision-makers looking to leverage AI as a competitive advantage. Whether you’re exploring automation for the first time or scaling existing AI capabilities, this talk will provide actionable strategies to drive efficiency and innovation.
Speakers:
As the capabilities of agentic AI evolve, so too must the way we structure and manage IT functions. In this session, discover how one global IT Strategy team is reengineering 72 operational processes through automated workflows and AI agents. This shift is redefining the nature of IT work, from execution to orchestration, and unlocking new efficiencies and value through the augmentation of existing processes.
We’ll explore the ripple effects on workforce planning, talent development, and change management, offering a blueprint for leaders navigating similar transformations.
Key Takeaways:
• How agentic AI can streamline and transform existing IT Strategy processes
• Lessons learned from reworking 72 processes across a global IT Strategy team
• The shift from process execution to AI agent management
• New skills and roles emerging in a post-agentic operating model
• Implications for talent strategy, workforce design, and reskilling
STOP THE TRAFFIK harness the power of data and technology to disrupt and prevent human trafficking and modern slavery. Our mission is to create a world where people are not bought and sold.
We were confronted with the severe global data gap on this crime and began developing our Traffik Analysis Hub, now the world’s richest dataset of human trafficking with over 9 million data points. Through several streams of work, we gather lived experience narratives through open-source web scraping, frontline data, and directly from those with lived experience and uplift them into this secure space to drive change. The TA Hub visualises data to create a live picture of human trafficking trends, routes, hotspots, recruitment methods, and a range of detailed views based on bespoke filters.
We will not stop trafficking alone.
Lessons learnt:
• Gathering ground truth data
• The cost of innovation
• Partnering to drive change
How do you turn an abstract idea into a live Agentic AI system? Which are the different strategies and points to consider before starting? How do you integrate with proprietary services? How can users trust suggestions and actions performed by agents?
After leading the development of a multi-agent AI workflow in the insurance industry, the speaker shares her experience thriving innovation in the industry with a concept and evolving through proving the idea, prototyping, internal experimentation, and ultimately enterprise deployment. Covering challenges navigated, such as working with no ground truth data, integration with custom services, managing prompt chains, and establishing trust through reliability and human expertise oversight.
Key Takeaways:
• How to take an Agentic AI solution from concept to production.
• Strategies to make agents reliable, trustworthy, and accurate.
• Approaches for evaluating systems without existing data or benchmarks.
• Lessons learned from early adoption and late-stage success.
As an organisation, Vanquis Banking Group does not prohibit the use of artificial Intelligence software and understands the potential benefits of harnessing it. Therefore, it is crucial to understand the risks of using AI. An AI Standard has been developed to ensure that VBG has an accessible and defined stance on leveraging AI technology and to manage the risks that are associated with the use of it. Implementing the standard has been challenging, and this sssion will cover the pragmatic approach taken including:
• How we decided what to include
• The pragmatic approach to rollout
• The challenges in getting adoption
• Where are we now?
Beth Fitzpatrick and Andy Oddy from the Data Management team in Royal London share practical insights and strategies for effective reference data management. Learn how they became two of the most popular colleagues in the UK’s largest mutual as they gathered and integrated high quality reference data with a complex regulatory pension dashboard programme.
The session will explore innovative approaches to reference data management, presenting case studies and examples of where reference data can improve AI models. Attendees will learn how reference data supports data governance, new methods and technologies for integrating reference data across systems, and the undeniable benefits of applying these practices themselves.
Key Takeaways:
• There are no shortcuts to effective reference data management, but you can’t afford not to do it!
• How to apply these practices in attendees' own organisations.
• What further insights you could unlock by leveraging these best practices.
The rapid advancement of Artificial Intelligence (AI) technology offers new opportunities for organisations across various industries to enhance their processes and remain competitive. However, many businesses face challenges in effectively leveraging AI to address specific needs and avoid falling behind. One primary challenge is identifying where and how AI can be applied to fulfil particular business requirements. While individuals have eagerly embraced generative AI tools like ChatGPT, integrating AI solutions more broadly poses difficulties.
Another critical consideration is ensuring AI solutions are fit for purpose. Identifying the right AI application for a specific problem necessitates thorough investigations and multiple Proofs of Concepts, distinguishing it from traditional technology project analyses. Organisations also grapple with resource allocation and budgeting for research and development activities, especially when immediate benefits are not evident.
Furthermore, adopting AI solutions introduces its own challenges, such as the adaptation period, training, and potential deviations from models, which can frustrate end-users and impede successful implementation. To navigate these challenges, a shift in approach to solution development is vital. Agile methodologies can support this process, yet existing frameworks must be customised to address AI project complexities.
This session aims to explore strategies to manage these complexities within development frameworks, ensuring smooth project delivery and creating robust solutions that effectively empower businesses.
AI is redefining how the financial services industry operates, but how can we harness its full potential to solve real-world challenges at scale? At Just Group, I lead the development and promotion of AI-driven solutions across business-critical areas, helping embed innovation into the core of home equity release and retirement planning.
With over a decade of experience across Data Science, big data, and AI strategy, I bring both technical expertise and business alignment to the work we do. We have successfully deployed solutions powered by Large Language Models to shape internal communication standards that reflect our organisational values and uncover subtle data patterns that improve operational and risk-based decision-making. In parallel, we are advancing AI-driven tools to support underwriters by summarising complex medical reports and building predictive models of future longevity—focused on conditions such as diabetes, cancer, and stroke.
This session will deliver actionable insights on how to responsibly scale AI in highly regulated environments. Attendees will gain learnings on turning complex AI systems into business-aligned tools, building trust in data-driven decisions, and fostering an innovation culture in traditional sectors.
This talk provides an essential guide for harnessing the transformative potential of AI while navigating and mitigating its risks. The authors of ‘In Machines We Trust’ provide a practical, flexible framework for building comprehensive and robust AI governance.
The talk will discuss AI principles and policies, understanding and assessing risks, developing safeguards and selecting the right technical tools and training. It will cover traditional AI systems, the complexities of generative AI and autonomous agents. Whether you're beginning your AI journey or refining your approach, this is your essential guide to seize the opportunity — and avoid the pitfalls — of AI.
Key takeaways:
• Understand what worked to design and build AI governance at global companies like Accenture, NatWest and Lloyds Bank
• Learn to identify the key risks from traditional AI, generative AI and agentic AI
• Discover risk mitigation approaches and supporting technologies which won’t stifle innovation
