Data readiness has become one of the biggest determinants of AI success across financial services. Yet many organisations remain far from the maturity required to support scalable, trustworthy AI.

Our recent research found that 51% of banks and insurance organisations rate their data maturity as moderate or low, while 47% cite poor data quality as a major barrier to progress. Despite the hype surrounding AI, the sector’s data foundations are often not ready to support it.

That gap matters. Fragmented datasets, ageing technology estates, accumulated technical debt and inconsistent governance continue to undermine confidence in analytics outputs. They also make it harder to deploy AI safely or at scale. In financial services, AI-supported decisions can have significant consequences for customers, regulators and the organisation itself. Whether a firm is assessing credit risk, identifying suspicious transactions or determining how to handle a customer query, trustworthy AI starts with trustworthy data.

By bringing together disparate data sources, modernising outdated systems and strengthening governance and resilience, financial institutions can build the foundation needed to scale AI with confidence. With this groundwork in place, they can move beyond isolated pilots. They can unlock value across fraud detection, customer service, risk modelling, compliance monitoring and operational efficiency.

Data quality issues with AI have run out of hiding spots

Data quality remains a critical stumbling block for successful AI deployment. The truth is that no financial institution runs on perfect datasets and pipelines. Data maintenance and quality assurance have always been resource-intensive. Particularly in environments where structured, semi-structured and unstructured data sit across multiple legacy systems. Banks, insurers and other financial services organisations may hold decades of customer records. Along with ransactional data, policy documents, applications and regulatory information across platforms that were never designed to work together.

Scaling AI effectively requires a reliable baseline of data quality, but many organisations have yet to reach it. Some have layered generative AI tools onto data that is incomplete, duplicated, inconsistent or siloed. Unsurprisingly, this can produce inaccurate or misleading outputs. This compromises model accuracy and limits the ability to trace or justify how a decision was reached. Thus eroding the explainability that robust model governance requires. 

This drives a second crucial blocker: reduced trust in AI outputs. When risk teams, compliance leaders, fraud analysts or customer operations teams lose confidence in AI-generated insights, adoption understandably slows. Senior leaders hesitate to approve AI‑driven changes to high‑stakes processes. And frontline staff become reluctant to use AI insights in their everyday decision‑making. 

Centralising data isn’t enough

In response, many financial institutions are investing heavily in cloud data platforms to centralise enterprise information and improve standardisation. These initiatives are an important step towards improving data quality and creating more consistent foundations for AI.

However, centralisation alone rarely resolves the full challenge. Accessing and using data held within these platforms can require extraction processes and knowledge of technical languages such as SQL. These are skills that most employees working in risk, compliance, customer operations or fraud prevention neither have nor should be expected to have. As a result, AI use cases can remain concentrated within central data and technology teams, removed from the business context in which decisions are made.

This reflects a traditional IT-led approach to rolling out data and analytics applications. But that approach doesn’t suit AI deployment. The teams closest to customer onboarding, payments, lending decisions, fraud investigations, compliance processes, investment operations and risk management are often best positioned to identify high-value use cases. And define what ‘good’ looks like operationally.

For financial institutions aiming to become next-generation intelligent enterprises, data readiness requires more than centralisation. Teams need governed, accessible and context-rich data close to where decisions are made. They can then embed business logic, define guardrails and shape AI workflows. This is particularly important in financial services, where decisions must be transparent, auditable and aligned with regulatory requirements.

However, the push to centralise data does not necessarily address the trust challenges that continue to hold AI deployments back. Even when strong data foundations reduce the risk of inaccurate or unpredictable outputs, organisations can still face challenges. Without clear, enforceable controls over what data is used, how it is transformed and how it flows into and out of AI workflows issues remain.

Building a stack that can actually scale

Data lakehouses provide a unified architecture for storing, managing and analysing enterprise data at scale. While they can help organisations progress towards industrialising AI, centralisation alone does not remove the barriers that have historically limited adoption.

An independent analytics layer can reduce friction by enabling teams to work with governed data stored in these platforms without extraction. This makes it easier for employees to design effective, context-rich AI use cases built on high-quality datasets.

Importantly, these layers also provide a foundation for capturing and operationalising the business logic that underpins financial services operations. From credit policies and fraud controls to pricing models, customer onboarding processes, risk thresholds and regulatory requirements. Embedding this logic within AI workflows helps ensure outputs are reliable, consistent and repeatable. Governance rules set centrally at the administrative level can then be applied consistently across the organisation.

Analytics software can also offer low-code or no-code interfaces that enable business users to build AI workflows without specialist programming skills. Because these workflows are visual in nature, line-of-business leaders can more easily review them, understand data dependencies, validate business logic and approve processes before deployment.

Together, data lakehouses and an analytics layer provide an effective foundation for financial institutions looking to close the gap between AI pilots and production. But achieving this requires sustained leadership commitment. Success depends not only on deploying new technologies, but also on building data literacy through targeted training, sharing successful use cases across teams, and establishing transparent governance policies that enable AI innovation while maintaining trust, compliance and accountability.

Final thoughts

As financial institutions accelerate their pursuit of AI, only those willing to confront their data challenges head-on will progress beyond stalled pilots, failed projects and isolated successes. Strengthening data quality, governance and accessibility is essential to building trust in AI-driven insights and decisions. When modern data architectures are paired with intuitive analytics tools, teams gain the confidence and capability to develop compliant, context-rich AI workflows that align with both business objectives and regulatory requirements.

The financial services organisations that treat data readiness as a strategic priority will be best positioned to move beyond pilot programmes and realise the full value of AI, safely, responsibly and at scale.

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