By Vivek Daga
AI is reshaping the foundations of financial services, yet many institutions remain stuck in the planning phase. Real resilience now depends on building the infrastructure that allows AI to move from clever experiments to dependable systems — from isolated pilots to assets the whole organisation can use with confidence.
Financial Services’ New AI Building Boom
The industry is in the middle of its most ambitious construction boom yet — not of branches or data centres, but of intelligent systems. According to the Ataccama Data Trust Report 2025, 99% of financial services institutions are now experimenting with AI, drawing up blueprints for new operating models and imagining digital skylines that promise speed and smarter decision‑making.
Yet the same report shows that only 3% of the sector has deployed AI into production. The gap isn’t caused by a lack of algorithms; it’s caused by the quality and reliability of the data beneath them. Models can be tested in minutes, but earning trust in those models takes far longer. Early attempts often struggle with explainability, repeatability, or simply being usable beyond the team that built them.
Meanwhile, expectations keep rising. Customers demand personalised, real‑time services. Regulators mandate transparency. Boards want efficiency and certainty. Meeting all three requires more than clever models. It requires the infrastructure that surrounds them: strong code, dependable data, and systems that can adapt as the rules evolve.
Why AI Adoption in Financial Services Is Still Unsteady
Across the industry, AI “construction sites” share a common problem: the ground they’re being built on is uneven. The infrastructure needed to support scalable AI simply isn’t ready. Still, the stakes are rising fast.
Recent FT Longitude research, commissioned by GlobalLogic, shows that four in five financial services firms risk falling behind without a more unified approach to technology and business model innovation. The barriers are familiar:
- Fragmented technology landscapes.
- Uneven or incomplete data foundations.
- Opaque data flows that hinder traceability.
- Shifting requirements and unclear ownership.
- Misalignment between technology and business teams.
The issue isn’t the code. It’s everything around it — the data, the governance, the operational “utilities” that keep systems running reliably. Without these foundations, AI remains fragile, inconsistent, and difficult to scale responsibly.
How Financial Services Can Build Strong AI Infrastructure
To compete — and to withstand the pressures of a rapidly shifting market — financial institutions need to stop treating AI as a standalone model or a one‑off experiment. AI now needs to be approached as infrastructure: long‑term, explainable, dependable, and built to support the organisation for years, not months.
Three principles — code, capital and change — matter most:
1. Code as Trust: Build AI Governance Into the Frame
Trust has to be engineered from the start. Governance shouldn’t be something added later to satisfy an audit; it should be part of the structure itself.
When governance is treated as the framework rather than the finishing touch, decisions become easier to trace, regulatory reviews move faster, and audit conversations become far more straightforward. Boards gain confidence in the integrity of automated systems, and the organisation gains a foundation strong enough to support growth without buckling under scrutiny.
2. Capital as Protection: Treat Data Quality as a Core Asset
Data quality has become one of the most valuable forms of capital in financial services. If the foundations are uneven, everything built on top of them will eventually show the strain — and AI is no exception.
High‑quality data enables more accurate models, faster deployment cycles, fewer failures, and components that can be reused across the organisation. The benefits compound over time, creating a base that supports repeatable patterns, pipelines, and models that reduce cost and accelerate innovation.
3. Change as Readiness: Use Regulation as a Catalyst
Regulation is evolving as quickly as the technology itself. Institutions that build AI systems capable of flexing with the rules will stay ahead.
Embedding transparency, adaptability, and responsible model management into the architecture leads to lower compliance costs, faster responses to new requirements, stronger customer trust, and a clearer leadership position in responsible AI. In other words, compliance becomes a competitive advantage.
Build for the Future, Not the Past
Financial services is entering a new era — one where resilience is defined not by the number of AI pilots underway, but by the strength of the infrastructure built around them.
To move from architectural renderings to operational reality, institutions need to engineer trust into the code, treat data quality as a core capital asset, and design systems that can evolve as regulations change. When these elements are embedded into the architecture, AI becomes reliable, responsible, and reusable at scale.
AI is no longer an experiment. It is the new infrastructure of financial resilience — and the organisations that thrive will be those that build systems strong enough to carry the future.
About the Author
Vivek Daga is the Managing Director for UK, Ireland and Emerging Markets and EMEA Leader for Financial Services and Consumer sectors. He is an entrepreneurial leader with extensive, global experience in building and leading high-performing teams. Vivek has worked extensively with clients across various industry verticals to shape and deliver technology-enabled solutions and services for enterprise modernisation and digital transformation.
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