The retail industry is rapidly scaling AI from experimentation to execution, with 97% of retailers now implementing AI programs and the global AI retail market reaching $18.4 billion in 2026. McKinsey research shows that retailers excelling at AI-driven personalisation achieve 10-15% revenue lift, while advanced forecasting can improve accuracy by 20-30% and lift margins by 2-5%.
Most retailers struggle to operationalise AI due to fragmented data across points of sales, e-commerce, supply chain, and loyalty systems. The Data Intelligence Platform unifies retail data to power real-time personalisation, demand forecasting, and dynamic pricing at scale. Genie enables business users to query data conversationally and build AI agents without code, while Agent Bricks deploys agentic commerce capabilities that guide product discovery and support transactions with real-time context.
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Partner Keynote: Accenture
Karen Fang Grant, Industry Research Lead, Accenture
Andrea Sulzenbacher, Managing Director, Accenture
Kelly Askew, Retail Industry Lead, Accenture
This talk explores how artificial intelligence agents are fundamentally transforming the retail landscape by shifting consumer trust away from traditional brands and toward automated recommendations. Research indicates that shoppers are increasingly delegating complex purchasing decisions to AI, valuing convenience and hyper-personalisation over human advice. To remain competitive, organisations must transition to a consumer-centric model that breaks down internal silos and utilises unstructured data to communicate with both humans and digital agents. Industry leaders are focusing on end-to-end process reinvention, specifically through innovations like merchandise 360 and intelligent stores that utilize sensors for real-time operational efficiency. Ultimately, the successful integration of AI requires a robust data foundation to bridge the gap between back-end supply chains and the evolving shopper journey.
Databricks Keynote
Rob Saker, AVP Retail Consumer Goods, Databricks
In this presentation, Rob Saker outlines the transformative potential of artificial intelligence within the retail and consumer goods sectors. He argues that the era of unlimited experimental spending is ending, requiring companies to shift toward AI applications that deliver measurable operational value. By utilising both structured and unstructured data, businesses can move beyond simple insights to create automated actions that assist frontline workers and improve the customer experience. Saker demonstrates how integrated data platforms like Databricks enable leaders to identify performance gaps and resolve them through intelligent task delegation. Ultimately, the source emphasizes that building a competitive advantage requires trusted AI frameworks that prioritise data governance, cost-efficiency, and accurate contextual understanding.
Customer Presentation: Haleon
Richard Moule, Chief Data Officer, Haleon
This talk is about Haleon’s strategic evolution into an independent consumer healthcare leader through advanced data and AI initiatives. Richard Moule, Chief Data Officer, emphasises that robust data foundations and governance are more critical for long-term success than simply selecting popular AI models. By utilising Databricks to standardize engineering and architecture, the company has transitioned from fragmented systems to a unified, scalable operating model. Future plans include moving toward a "post-dashboard" era driven by autonomous agents and integrated platforms like Consumer 360 that provide proactive insights. The discourse also addresses broader industry trends, such as the rise of coding agents, the economic necessity of FinOps, and the importance of human-centric upskilling to ensure responsible AI adoption. Ultimately, the narrative highlights how specialised data literacy and enterprise-wide orchestration are essential for turning technological potential into measurable business value.
Customer Presentation: Asda
Cassidy Louch, Lead Data Engineer, Asda
In this presentation, Cassidy Louch, Lead Data Engineer at Asda recounts the company’s transition to a modern data ingestion framework for their Manhattan warehouse management software. The original system was hindered by an 8 hrs processing delay and a lack of real-time insights, prompting a search for a more efficient Change Data Capture (CDC) solution. While the team initially spent two months developing a complex custom pipeline using Debezium, they discovered that Databricks LakeFlow Connect could significantly simplify the process. A three-day pilot of LakeFlow successfully ingested nearly 500 tables, surpassing the progress made over months with the previous open-source method. By adopting this YAML-based configuration, Asda eliminated the need for virtual machine management and achieved real-time data availability. Ultimately, the move to LakeFlow has provided analysts with up-to-date supply chain visibility and a user-friendly data structure within the Unity Catalog.
Customer Presentation: The Retail Trust
Tim Walpole, Head of Data, The Retail Trust
Tim Walpole of the Retail Trust describes the development of a sophisticated digital platform, as the "Netflix of well-being," designed to support millions of UK retail workers. Built on the Databricks Data Intelligence Platform, the initiative addresses industry challenges like high turnover and mental health struggles by providing hyper-personalised support. The system utilises agentic AI and two-tower machine learning models to deliver specific content recommendations based on an individual's unique needs and historical signals. By leveraging vector search and real-time data processing, the trust can provide tailored resources in milliseconds at a very low operational cost. This technological journey aims to move from reactive assistance to preventative care, identifying potential crises before they occur to improve the overall health of the retail sector.
Business User Demo
Pavi Singh, Solutions Architects Retail Consumer Goods, Databricks
This demo showcases the unified application on the Databricks platform designed to optimise retail and consumer goods operations through data unification, governance, and intelligence. Pavi Singh demonstrates how different stakeholders, from executives to store associates, can utilise a single source of truth to manage KPIs, demand forecasting, and inventory levels. The platform features an AI-driven audience wizard that automates marketing campaign creation and provides real-time simulations to predict the impact of various business levers. Additionally, the system streamlines in-store operations by assigning tasks to staff and tracking promotion effectiveness via mobile devices. It highlights the use of autonomous agents and natural language querying to orchestrate complex retail workflows from end to end.