A Four-Pronged Gen AI Strategy at Experian
Experian’s approach to generative AI is a clear example of strategic application in a data-driven business. Instead of chasing trends, the company focused on amplifying the value of one of the largest consumer data sets available. Shri Santhanam, Executive Vice President and General Manager of Platforms, Software, and AI, has led this transformation with a structured four-part plan targeting Products, Productivity, Platform and Governance, and Education and Adoption.
Experian’s AI efforts predate the public surge of Gen AI interest following ChatGPT’s launch in 2022. The company’s innovation labs were already exploring advanced machine learning, transformers, and natural language processing. When Gen AI gained widespread attention, Experian avoided fragmented pilot projects. Instead, it implemented an enterprise-wide strategy focused on scaling meaningful AI applications.
Products and Productivity: AI That Supports Human Expertise
On the consumer side, Experian developed a virtual assistant providing personalized financial guidance. This AI goes beyond simple chatbots, offering nuanced and accurate answers about credit scores and financial management. It currently serves 16 million consumers, helping to advance Experian’s goal of promoting financial health at scale.
For business clients, the challenge was democratizing access to Experian’s proprietary data expertise. The solution is the Experian Assistant: a 24/7 digital data advisor delivering on-demand insights that previously required expert human intervention. This AI tool has significantly boosted client engagement and received multiple innovation awards.
Internally, AI tools have improved productivity across teams in engineering, customer service, and knowledge work. Examples include code generation and enhanced query resolution. The focus is clear: AI empowers employees to concentrate on higher-value tasks rather than replace them.
Platform and Governance: Scaling with Confidence
Experian identified early that deploying Gen AI at scale involves risks—ethical, regulatory, and reputational. To avoid getting stuck in pilot phases, the company built a strong internal AI platform paired with strict governance. A dedicated risk council reviews every AI use case, ensuring compliance with data privacy and ethical standards.
This governance framework allows Experian to move confidently from proofs of concept to full production, protecting its brand integrity while leveraging AI advancements.
Education and Adoption: Fostering a Culture of AI Engagement
Experian recognized that technology alone isn’t enough; adoption is critical. To encourage wide participation, they launched company-wide hackathons, weekly AI seminars, and interactive training agents. This approach spreads AI knowledge beyond specialists to all 20,000 employees worldwide.
The company fosters shared ownership and reduces job displacement fears by emphasizing AI’s role in amplifying human creativity rather than replacing workers. This grassroots innovation culture drives new ideas from all levels of the organization.
Measuring Success and Looking Ahead
Experian tracks ROI carefully to align AI initiatives with business goals. Consumer engagement metrics include monthly active users, conversation volumes, and Net Promoter Scores, all showing positive trends. In the B2B sector, the Experian Assistant cuts task time by up to 75%, demonstrating clear efficiency gains.
Internal usage and adoption rates guide ongoing improvements. The company is focused on sustaining momentum, not resting on early results.
Looking forward, Experian aims to develop “trusted agents”—AI systems that combine advanced capabilities with transparency, ethics, and reliability. The next phase of AI adoption will require building trust at the same pace as technological progress. Experian’s four-pronged strategy sets a solid foundation for this future.
Executives seeking to implement AI in their organizations can learn from Experian’s balanced approach: clear goals, strong governance, inclusive education, and measured outcomes. This combination ensures AI initiatives deliver value without compromising trust or company values.
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