Financial Services push AI from pilots to scale

Financial services firms have been experimenting with artificial intelligence (AI) in isolated pilots such as chatbots in banking, claims automation in insurance, and portfolio analytics in wealth management. While early efforts demonstrated promise, they did not lead to enterprise-wide transformation. However, that is now changing.  

Across the industry, leaders are moving from experimentation to scaled adoption that’s driven by competitive pressures, rising customer expectations, and the pursuit of operational efficiency. Executives are increasingly viewing generative AI (GenAI) as a strategic priority rather than a tactical tool, but enthusiasm alone is not enough. Scaling AI requires a deliberate approach that integrates governance, operational readiness, and alignment with long-term business objectives. 

From early experiments to enterprise integration 

The AI journey began with narrow applications. Banks were among the first to deploy AI for customer service automation and fraud detection, proving its ability to streamline interactions and flag anomalies. Insurers explored claims triage and underwriting support to reduce cycle times and improve accuracy. Wealth and asset managers tested AI in portfolio analytics to enhance investment insights, reflecting a more cautious approach to adoption. 

Today, these sectors are converging on a common goal: embedding AI into the core of their operations. Banks are extending AI into credit decisioning and compliance monitoring, enabling faster lending and stronger risk controls. Insurers are moving beyond claims automation to dynamic pricing and predictive fraud detection, reshaping how policies are underwritten and serviced. Wealth managers are leveraging GenAI to deliver hyper-personalized advice and automate regulatory reporting, positioning themselves to meet rising client expectations for tailored experiences. 

This progression reflects a broader industry shift, from isolated wins to integrated capabilities that touch every part of the value chain. 

Governance as the cornerstone of scale 

As adoption accelerates, governance has emerged as the defining challenge. Financial institutions operate in highly regulated environments where data privacy, model transparency and ethical considerations are nonnegotiable. Without robust frameworks, the risks of bias, security breaches and compliance failures multiply. 

Governance remains a top priority for executives. According to a recent EY survey, 79% of banks said they would prioritize strengthening governance if they could restart their GenAI initiatives, ranking this over any other factor. Leading firms are establishing oversight committees, embedding risk controls into model development, and adopting explainability standards to satisfy regulators and build customer trust. Governance cannot be static, it must evolve alongside technology and regulation, anticipating new risks while enabling innovation. Institutions that strike this balance will be best positioned to scale responsibly. 

Aligning AI with strategic ambitions 

One reason many pilots stall is misalignment with enterprise objectives. Deploying AI for isolated efficiency gains, such as automating a single workflow, rarely delivers transformative value. Of implemented GenAI use cases in banks, a recent EY survey found that 40% of uses cases failed to reach a desired result or were discontinued.  

Successful organizations connect AI initiatives to broader strategic priorities: enhancing customer experience, driving growth, and improving resilience. 

Consider how this plays out across sectors. Banks integrating AI into credit risk modeling are not simply reducing manual effort; they are enabling faster, more accurate lending decisions that support revenue growth. Insurers using AI for claims automation are improving customer satisfaction and retention, not just cutting costs. Wealth managers deploying GenAI for personalized advice are differentiating their brand in a crowded market while scaling operations efficiently. 

When AI investments align with long-term goals, they become catalysts for competitive advantage rather than short-lived experiments. 

The next phase: AI as a transformation engine 

The future of AI in financial services involves moving from a collection of discrete tools to an integrated capability embedded across the value chain. In banking, expect end-to-end digital lending powered by AI-driven risk and pricing models. In insurance, anticipate fully automated claims ecosystems with predictive fraud analytics. In wealth management, envision real-time portfolio optimization and personalized financial planning at scale. 

Achieving this vision requires operational readiness, cross-functional collaboration and continuous innovation. Firms must modernize data infrastructure, upskill talent, and reengineer processes to support AI at scale. They must break down silos between IT, risk, compliance and business units to accelerate deployment. And they must treat AI as a living capability that evolves with market dynamics and regulatory landscapes. 

From promise to performance 

AI in financial services has moved beyond hype. The question is no longer whether to adopt, but how to scale responsibly and strategically. Banks, insurers, and wealth managers that integrate governance, align use cases with enterprise goals, and invest in operational readiness will unlock the full potential of AI, not as a series of experiments, but as a catalyst for industry-wide transformation. 

The next chapter is not about isolated wins. It is about embedding AI into the DNA of financial services, creating smarter, more resilient institutions that deliver value to customers, shareholders and society at large. 

The views expressed in this article are those of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.