Just 30% of financial firms use AI for customer retention

Financial services firms are spending more aggressively on AI than healthcare or media companies, yet only 30% deploy it for customer retention and churn prediction. The gap between back-office AI adoption and customer-facing use reveals a sector that has automated revenue and risk functions while leaving growth and experience largely untouched.

The findings come from a PYMNTS Intelligence survey of 60 senior technology executives at U.S. enterprises with at least $1 billion in annual revenue. The report, "Financial Services Pulls Ahead in the Enterprise AI Race," found that 85% of financial services and insurance firms plan to increase AI budgets over the next 12 months. The strongest investment drivers were productivity and competitive positioning, each cited by 65% of respondents.

Where financial firms are spending - and where they aren't

Financial services has scaled AI across more tasks than the other two sectors surveyed. But the distribution of those use cases tells a lopsided story. The sector pushed AI deep into revenue operations, risk management, and forecasting. Customer-facing applications lagged significantly.

Only 30% of financial firms use AI for churn prediction and retention targeting. Just 20% apply it to know your customer (KYC), know your business (KYB), and identity verification. A mere 10% use AI for A/B testing and experimentation. The numbers suggest that while banks and insurers have automated internal decision-making, they have not yet connected that intelligence to the customer experience layer.

For professionals working in AI for Finance, the pattern is clear: the hardest problems are no longer about building models. They are about data infrastructure and organizational readiness. Financial firms cited data quality and fragmentation as their top barrier to broader AI adoption, at 30%.

Healthcare and media take different paths

Healthcare's top AI use case was customer service chatbots and virtual agents, deployed by 60% of firms. Workforce planning and model risk management followed at 55% each. The sector is using AI to relieve operational pressure rather than redesign patient journeys. Only 5% of healthcare firms used AI for customer journey orchestration, and just 30% used it for regulatory compliance monitoring - notable in a sector defined by regulation and operational complexity.

Media and advertising firms led in audience retention, with 55% using AI for churn prediction and retention targeting. They also showed strong adoption in quality assurance and call analysis at 60%. Yet only 10% used AI for user experience personalization and adaptive interfaces, the lowest rate in the survey. Media firms appear to understand and protect their audiences well, but underinvest in the real-time personalization systems that shape the audience experience.

The barriers are different by sector - and so are the fixes

Financial services firms identified data quality as their single biggest blocker. Healthcare firms split their concerns between system integration and data quality, each at 30%. Media firms reported no dominant barrier, instead citing a mix of internal skills gaps, governance issues, leadership alignment, and integration challenges as separate constraints.

Across all three sectors, 80% to 85% of leaders said their five-year vision is AI that helps people make decisions, not AI that replaces them. The consistency of that answer across industries is striking. But the path to that vision diverges sharply by sector. Financial services needs cleaner data. Healthcare needs connected systems. Media needs organizational alignment. For those focused on AI for Executives & Strategy, the report makes a case that scaling AI well has become the real challenge, not initial adoption.

Why this matters for finance, IT, and development professionals

The survey reveals a specific and addressable gap: financial firms have the budget, the executive intent, and the technical capability to deploy AI more broadly. What they lack is the data infrastructure to extend AI from back-office functions into customer retention, identity verification, and experience optimization. For IT leaders and developers in finance, this means the next wave of AI investment will likely target data quality, integration pipelines, and the systems that connect predictive models to customer-facing applications. The work is less about algorithm selection and more about data engineering and cross-team coordination.