Banks and insurers are often seen as difficult environments for AI. The sector is heavily regulated, risk-sensitive and complex. Data cannot be used casually, decisions need to be explainable, and new technology must fit into existing controls, processes and operating models.
While this is all true, it has also forced organizations to build many of the structures that practical AI needs, such as governed data, documented policies and ways of working, access controls, audit trails, quality checks and risk ownership.
AI is not useful in a vacuum. It needs data, information, context, boundaries and feedback, workflows where outputs can be reviewed, and people who understand where the risk sits. In that sense, financial services are more ready for AI than the usual discussion suggests.
Financial services are full of AI-ready work
There are many ways to use AI in the finance sector. Some are about automation. Some are about supporting experts. Some are about improving quality, consistency or speed in existing work. The important point is not to start from the technology, but from the work itself.
Banks and insurers make thousands of decisions everyday across credit, fraud, claims, compliance and customer service. Most of these processes depend on large volumes of information and expert judgement. Creating opportunities for AI to support experts by making information-intensive work faster, more consistent in quality and easier to scale. Much of this is information work in which people search, read, compare, summarize, classify, draft, check and document. They do this across customer data, policies, contracts, claims material, transaction data, reports and regulatory requirements.
Can AI help a claims handler understand a case faster? Can it help a compliance specialist find relevant material across multiple systems? Can it identify missing information before a case moves forward? These are normal pieces of work which can be used now. And because financial services operate at such high volumes, even small improvements can have a meaningful impact.
One practical example comes from our financial services client for whom we deployed an AI agent to help their customer service representatives produce answers faster. The agent pulls data from multiple systems into a single view, giving tens of customer service representatives what they need to answer customer questions without manual searching. The solution improves service quality in environments where data is scattered across many systems.
The high volumes can feel heavy but it also creates a foundation for AI. If data is not described, AI does not know what it is using. If ownership is unclear, no one knows who can approve the use. If definitions differ across business areas, AI may give confident answers based on inconsistent meaning. If process documentation is incomplete, AI agents will follow the gaps just as a new employee would. Metadata, data quality, access management, process documentation and ownership models are part of what makes AI usable in real work.
Governance work and AI development do not have to be sequential. For one financial services client, we built them together. We developed a data governance model and integrated AI agents directly into the governance work. The agents supported with metadata descriptions, management routines, and documentation across data owners, stewards, and business teams. In a regulated environment, well-governed metadata underpins not just AI development but also risk governance and reporting. By combining governance development with AI-supported processes, the organization has started building a clearer data foundation, with AI agents supporting the journey toward more repeatable and responsible AI use.
From pilots to real change with AI
Most banks and insurers have already started their AI journey. They may have introduced personal productivity tools, tested generative AI in selected teams, created internal guidelines or explored early use cases.
Moving from early exploration to systematic adoption often means building the underlying platform and governance that makes AI deployable at scale. For one client, who lacked the governance foundations for AI development we built the foundation in the form of an AI platform and related baseline governance and development processes. With development principles, AI policies and AI-equipped landing zones, teams gained a repeatable way to develop and deploy new AI use cases in a regulated finance environment.
The challenge is how organizations move from isolated experiments to practical, governed adoption. Many financial institutions already have governance structures, ownership models and control processes in place. The opportunity is to use these foundations to scale AI responsibly.
In this sense, financial services are not fundamentally different from other industries. Organizations need to choose where AI is actually worth applying, implement solutions systematically, build them with sufficient quality and make sure the organization is able to change.
Regulation has made financial services harder than many sectors, but it has also made it more prepared.
Where could your organization create AI value by building on the data, documentation and governance structures you already have?
Listen Kuulapodi episode: "AI and governance in banking industry"
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