How AI Will Redefine Retail in 2025

As we look back on 2024 and forward to 2025, we wanted to share some ideas and perspectives about how the Hopsworks AI Lakehouse platform can provide value for our retail clients, today and into the future. In 2024, we saw some significant and valuable interest from some of Europe’s largest online retailers, who have been fast adopting Hopsworks’ technology to enable highly valuable real-time machine learning use cases. We want to share some of the insights from these deployments. A recent, yet technical, example is of a free online course we did on how to build a TikTok-like Personalized Recommender for H&M articles, exploring the engineering aspects of applying real-time AI into retail. In this post, we wanted to share a less technical, more business-level perspective. 

Both the online and “bricks and mortar” retail landscape are rapidly evolving, and Artificial Intelligence (AI) is demonstrating to be a key driver of this transformation. Retailers in 2025 are facing increasing pressure to deliver even more personalized consumer experiences, optimize their internal operations, and build on their existing successes to create resilient businesses. AI and Machine Learning (ML) platforms, like Hopsworks AI Lakehouse, offer a comprehensive toolset that will help deliver that value for retail AI.

Based on what we saw in our customer deployments in 2024 (and before), we wanted to share a few recommendations for retail organizations, highlighting how they can thrive in the age of AI - with the support of the Hopsworks AI Lakehouse, of course.

Prioritize Data Quality and Governance

High-quality, reliable data is the foundation of any successful AI initiative. Retailers often grapple with data silos and inconsistent data quality, hindering AI model training and insights. Hopsworks addresses this challenge by providing a unified platform for data management and governance.

Some features which the AI lakehouse enables for data quality and governance is data Integration, data cleansing and validation as well as data lineage and auditing. Data integration consolidates data from diverse sources (such as customer interactions and supply chain data) into a centralized repository, whereas data cleansing and validation ensures data accuracy, completeness, and consistency for improved model training. Lastly, data lineage and auditing tracks data provenance and transformations to maintain data integrity and comply with regulatory requirements.

Embrace an AI-Driven Organization Culture

Transforming into a data-driven retail enterprise requires a cultural shift in the organisation, a shift that will continue to take shape in 2025. It’s crucial to foster collaboration and knowledge sharing between different teams within an organization in order to avoid pitfalls and information bottlenecks caused by miscommunication, and production delays caused by slow iterations or prototyping.     

Hopsworks AI Lakehouse platform aims at facilitating collaboration between data scientists, analysts, and business stakeholders, through shared datasets, workspaces, transformation rules and project management tools. The AI Lakehouse also fosters experimentation and innovation through providing an environment for experimentation and rapid prototyping of AI solutions. Data scientists don’t have to reinvent the wheel, they can work from existing, validated feature groups, and can leverage a pre-integrated set of tools to get to value faster. Fast iteration is key to doing so.

Leverage AI for Hyper-personalization

AI continues to revolutionize personalization in 2025, enabling tailored product recommendations and targeted marketing campaigns and offers at a much higher level of sophistication than what was previously thought possible. Hopsworks empowers retailers to deliver hyper-personalized experiences through various techniques and processes such as:

Feature Engineering and Model Training using AI techniques

Using the same techniques that have yielded the spectacular results of Large Language Models (LLMs), we can support the development of sophisticated recommendation engines and customer segmentation models.

Real-time predictions based on Machine Learning and AI

Enabling real-time insights into customer behavior for dynamic personalised experiences and targeted offers.

A/B Testing and Optimization

Facilitating continuous improvement of personalization models through extensive iteration and experimentation loops.

Optimize Operations for Efficiency and Sustainability

AI can streamline retailers’ operations, from supply chain optimization and demand forecasting to inventory management and fraud detection. Machine learning models leverage advanced pattern recognition and neural network technologies on the Hopsworks AI Lakehouse, which supports these initiatives with specific use cases, like for example:

Predictive Analytics

AI models can enable accurate demand forecasting to optimize inventory levels, reduce waste, and enhance supply chain resilience.

Anomaly Detection

Machine learning systems can be used to identify unusual patterns in data to detect fraud, prevent losses, and improve security.

Process Automation

AI systems can be used to automate repetitive tasks to free up resources for strategic initiatives.

Ensure Responsible and Ethical AI: 

As retailers increasingly rely on AI, addressing ethical considerations, including data privacy and responsible AI development, will be more crucial than ever. Hopsworks promotes responsible AI by:

• Implementing the F.A.I.R. Principles in Data for AI: implementing these principles suggests that we always make sure we have findable, accessible, interoperable and reusable data underpinning our AI systems.

• Enabling advanced Data Security and Privacy: Hopsworks AI Lakehouse implements robust security measures to protect customer data and ensure compliance with regulations like GDPR and the EU AI Act.
• Providing Model Explainability: the AI Lakehouse provides detailed reports and tools to understand model decisions and mitigate biases, ensuring fairness and transparency. The Hopsworks AI Lakehouse enables advanced feature and model monitoring to make sure that appropriate care is guaranteed. 
• Monitoring and Auditing: the AI Lakehouse provides model performance and data usage tracking functionality to identify potential risks and ensure ethical use of AI.

Summary

As we see retail organizations adopting AI and ML in their daily operations, we believe that the usage of versatile AI infrastructure can help retail organizations unlock the full potential of AI. With an AI Lakehouse, retailers have the potential to transform their businesses to become more customer-centric, efficient, and resilient.