Data increasingly underpins how banks operate, manage risk and serve clients. Banks are generating and processing growing volumes of data, while artificial intelligence (AI) is expanding their ability to elicit insights and hyper-personalise products and services.

Investment in AI reflects this urgency. A 2026 Bloomberg Intelligence survey found that European banks expect net headcount to rise 4% on average over the next three years as they hire engineers and data scientists to deploy AI. Separately, a Bloomberg survey found that 75% of finance leaders saw lost profitability or obsolescence as the greatest consequence of falling behind in AI adoption.

To realise its potential, AI first needs to be grounded in an understanding of the business – every process, interaction and decision. AI has transformed decision making, risk identification, client service and product development and is driving impact at scale. But AI is only as effective as the data, governance and human judgement that underpin it.

Today’s digital and hyperconnected financial ecosystem generates so much information from every payment, trade and interaction, making data quality as important as quantity. Money moves more quickly and clients are more demanding as products and markets become more integrated. For banks operating across multiple markets and business lines, trusted data is essential to serve clients, manage risk, adhere to regulation and innovate responsibly.

Building a trusted data foundation

Banks rely heavily on well-governed, reliable and usable data assets. Banks must align critical data definitions and taxonomies, so data carries the same meaning regardless of where it is created, accessed or consumed. Governance in the form of data ownership, quality controls and access must be baked in from the beginning.

When data is trusted and well governed, teams spend less time reconciling inconsistencies and more time generating insights and taking action. Connected data supports better client servicing by amalgamating information from products and channels. For instance, a relationship manager supporting a multinational client should be able to access the same trusted client information, portfolio holdings and interaction history wherever the data originates. That consistency enables faster decisions, a more seamless client experience and effective innovation.

Data quality is another priority. The goal is not simply to measure quality, but to make those measures meaningful and actionable. Quality indicators must drive action where the data is created or managed. This depends on clear ownership, practical standards and accountability embedded close to the business. Data is a shared resource but can only become a shared strength when responsibility is understood across the bank.

Governance as an enabler of innovation

With AI, governance cannot be bolted on as an afterthought. It builds the guardrails that allow teams to innovate safely and confidently. Data standards, quality rules and access controls should be embedded into platforms from the outset so teams can innovate and iterate more quickly while maintaining oversight.

Not all AI use cases carry similar risks, and they should be governed accordingly. Low-risk implementations should not be impeded by friction, while high-risk use cases need stricter oversight, tighter controls and sharper scrutiny. Responsible experimentation should be enabled without compromising control, client trust or regulatory confidence.

This balance is key, as data may sit across fragmented systems and legacy architectures that were never designed for AI workloads. Simultaneously, banks must navigate privacy, security and cross-border data sovereignty requirements while ensuring innovation can move quickly. Scaling this balance requires governance to become more embedded, practical and repeatable.

An important shift is to move governance left from retrospective reviews to controls embedded early in design and development. Policies and standards need to be translated into practical requirements, automated where possible and built into daily workflows. This brings together business, data and technology teams from the start, making governance an enabler of speed.

Building the skills to scale AI

The future of data governance will be defined by people as much as technology. People in banks who understand how to use data effectively and responsibly can then take advantage of modern platforms and advanced AI. Skills such as data literacy, analytics, critical thinking and informed use of AI tools are becoming essential across the workforce.

This is important because innovation is increasingly happening within the business, not only through central teams. A federated model, supported by strong guardrails, allows banks to scale innovation while maintaining consistency and control. Execution needs to be the remit of teams that understand the data, the processes and the client outcomes best.

Within this model, the Chief Data Officer (CDO) plays a central role in setting standards that provide coherence and confidence. As AI becomes more embedded, the CDO’s role evolves from being a governance and control leader to a strategic enabler of how a bank operates in an AI-driven environment. CDOs ensure the data risks associated with scaling AI are understood and managed proportionately through clear standards, embedded controls and close partnership with other teams.

The CDO also enables data consistency and reuse. However, guardrails must be clear and practical. If frameworks are too complex or disconnected from how teams deliver, they will impede progress. Simple standards, focused controls and better tooling help the teams closest to the data operate effectively.

Trust cannot be created through policy alone. Clear ownership, better data quality at source, controls embedded into delivery and investment in people capability all matter. The most significant constraint on scaling AI is often not the model itself but the quality, consistency and governance of the underlying data.

When teams understand why trusted data matters and how governance supports better decisions, these practices become second nature to how the bank operates. Banks that combine reliable data, proportionate governance and skilled people will be better positioned to scale AI with confidence.

Shebani Baweja is Group Chief Data Officer of Standard Chartered