The data dividend: Powering AI success in business 

Artificial intelligence is reshaping the business landscape. With the technology’s market size set to be $1.01 trillion by 2031, AI’s transformative potential is attracting strong investment and governments are laying the groundwork to support innovation. 

Despite this, many businesses are struggling to turn their AI ambition into real-world impact. “It’s such an important time for businesses and AI. They see adoption rising, they understand its importance, and they’re sensing the urgency,” says Christine Foster, General Manager of the GenAI Centre of Expertise at Experian UK&I. “Meanwhile it can feel like there’s a huge gap between the pace of research and the hard work required to implement this. There is so much opportunity for businesses but there’s plenty they need to get right as well.”  

While a potential skills shortage and customer caution go some way to explaining the gap between ambition and execution, the most fundamental challenge to AI implementation is data. Businesses understand that they are sitting on vast amounts of valuable data, yet few have the clarity or infrastructure to turn it into a strategic advantage. Without a clear path to transform data into actionable intelligence, even the most promising AI initiatives risk falling short. 

“AI’s potential comes from its ability to analyze vasts amount of data rapidly and its really data that fuels models, insights and decisions,” says Andrew Abraham, Global Managing Director of Data Quality at Experian. “And so, for AI models to function effectively, they require relevant, complete and accurate data. Without that you really run the risk of increased bias or hallucinations—where you get an inaccurate or invalid answer from the model.”  

AI’s pace is accelerating, and 2025 has been a defining year. To stay ahead, businesses must treat data as a strategic asset to unlock the technology’s full potential. Laying a strong data foundation for AI requires focusing on three key priorities:  

• Data quality and governance: A strong data foundation is anchored in data governance, quality and integration practices. This means establishing clear accountability, whether through a dedicated team or a well-defined framework, and ensuring data is accurate, complete, consistent and timely.  
• Scalable technology and tools: AI’s true potential is unlocked when powerful algorithms are supported by a scalable, intelligent infrastructure. Cloud platforms, data lakes and modern data management systems will form the backbone as well as being paired with the right technology partners.  
• Data literacy and culture: The future of AI depends on people as much as technology. Cultivating a data-driven culture and investing in skills development in both data and AI—through training, partnerships and hands-on experience—will allow teams to use AI not just safely but strategically. 

AI is not only reliant on data, it can also enhance it. Through automation and machine learning, AI can detect anomalies, correct errors and improve data quality in real time. “Data quality is often a big task for businesses,” Foster notes. “Getting their data into shape and all the work of extraction, transformation, loading and de-duping can be really well dealt with by automation and AI.”  

This interplay between data and AI is already delivering results. Experian’s launch of Experian Assistant—a GenAI solution on the Ascend platform—supports data scientists and analysts 24/7 with real-time coding guidance and deployment support. Its effectiveness is not just a product of advanced AI, but of the integrity and structure of the data it draws from.  

Ultimately, in order to unlock AI’s ultimate potential, it must be embedded at the heart of your company’s business strategy. This means aligning AI with clear objectives, integrating it into core operations, and supporting it with a framework for responsible implementation. As Abraham concludes: “As innovation accelerates, the fundamentals are still the most important thing. Data quality, data governance, and ultimately trustworthiness will be essential to AI. They are no longer optional.”  

Head to the Experian Exchange series to hear more about this, as well as how to unlock growth in financial services using AI.