Across this Intelligent Operations in Investment Banking series, we have explored how investment banks are re-thinking critical workflows such as Know Your Customer (KYC) and due diligence, trade reconciliation and client onboarding through Agentic AI-powered operating models. Yet beneath each of these transformations lies a common dependency that is often overlooked: Trusted enterprise data. Without a connected, governed data foundation, even the most advanced AI-enabled workflows struggle to scale, limiting the value banks can realize from their investments.
That challenge has intensified in 2026. The World Corporate and Investment Banking Report 2026 from Capgemini reveals that industry growth is slowing, with compound annual growth set to fall from 6.5 percent to 5.4 percent over the next five years.1 Combined with increased competition and rising client expectations, the pressure to convert investment into measurable return has rarely been greater. In a year defined by tariff volatility, geopolitical risk and compressed deal timelines, the ability to act on trusted, real-time data has shifted from a competitive advantage to an operational necessity.
As AI in banking moves beyond experimentation, organizations recognize that enterprise data management is paramount. It has become the foundation for intelligent operations, regulatory reporting and AI adoption across the enterprise.
However, legacy issues are holding many firms back, with banks devoting 43 percent of annual IT budgets to maintaining legacy platforms, and just 29 percent to transformative technologies, from AI to distributed ledger. Dig into this imbalance and a single culprit emerges: Data. Capgemini’s research finds that 71 percent of banking executives consider fragmented data across business lines the biggest constraint to value creation, with records scattered across systems that hold inconsistent definitions and incompatible lineage. When the data beneath a number cannot be fully trusted, the impact echoes throughout the enterprise.
The Data Challenge
71% of banking executives consider fragmented data across business lines the biggest constraint to value creation.
Nowhere is the cost of that fragmentation clearer than in data management and reporting. 59 percent of banking executives say data management and reporting processes remain highly manual and fragmented, making them the fourth most inefficient workflow across the investment banking value chain.
The Process Bottleneck
59% of banking executives say data management and reporting processes remain highly manual and fragmented, making them the fourth most inefficient workflow across the investment banking value chain.
The urgency is sharpest for institutions deploying Agentic AI in banking. Multi-agent systems that orchestrate end-to-end workflows across KYC, reconciliation and reporting can only operate reliably when the data they draw on is trusted, consistent and current. Without that foundation, Agentic AI accelerates errors rather than operations.
The encouraging news is that the forces re-shaping the rest of the bank are now reaching its data core. The convergence of modern data engineering and governance and next-generation technologies is making it possible to treat data not as fuel for individual processes but as a governed, re-usable asset, one that makes reporting a by-product of well-managed data rather than a workflow in its own right. Increasingly, enterprise data management and master data management are evolving beyond maintaining trusted records to creating AI-ready data foundations that enable information to be governed, shared and re-used consistently across the enterprise.
In this article, we explore how organizations can re-imagine data management and reporting and, in doing so, turn one of banking's most persistent operational drags into a durable source of advantage in 2026 and beyond.
Why the Data Foundation is Holding Investment Banks Back
As Capgemini's research shows, what today's clients, regulators and AI ambitions demand of enterprise data has outpaced how data is managed. For years, data has been built business line by business line and system by system, with each desk, product and function assembling what it needed to operate, along with its own definitions, controls and reporting layer. The result is an estate that works locally but fails enterprise-wide, and three forces are now exposing its limits:
Firstly, the cost of fragmentation is rising.
Without shared catalogs and quality benchmarks, the same data is duplicated, re-keyed and reconciled across functions, and pilots that look efficient in isolation stay isolated. The result is teams working from partial views. As Capgemini research reveals, only 30 percent of banking executives say they can provide clients with proactive strategic insights, because data arrives too late and is too incomplete to act on.
Secondly, legacy systems are slowing change.
Two-thirds of executives (66 percent) report that legacy environments make integration harder and directly slow change, forcing new solutions to connect through multiple handoffs and custom rules that erode the very gains they were meant to deliver.
Finally, ungoverned data is spreading.
A persistent bottleneck across investment banks is the volume of unstructured documents circulating outside governance boundaries and repeatedly uploaded to large language models without standardization or oversight. This slows time-to-market, increases model drift and inflates processing costs, turning well-intentioned experimentation into a new source of risk and expense.
Closing that gap means changing what data management and reporting are for, evolving from fragmented, system-specific records toward a single source of truth; from periodic, manual re-construction toward continuous, validated reporting; and from data treated as the by-product of individual processes toward data managed as a governed, re-usable asset. So how can organizations build the operating models that deliver this future?
The Four Pillars of Intelligent Data Management and Reporting
Building a trusted enterprise data foundation requires more than modern technology. It demands a connected operating model that governs how data is captured, managed, validated and shared across the enterprise. While the technologies involved may vary, the most effective transformation programs tend to be built around four interconnected capabilities.
Most banks have responded by investing in more tools and layering new reporting platforms, dashboards and point automations onto the same fragmented base. The effect is to accelerate the symptoms without ever treating the cause.
What has changed in 2026, however, is that the means to fix the foundation itself — not just the tools sitting on top of it — are ready to be harnessed. Used together, today's data and AI capabilities make it possible to re-design how data is governed, validated and shared, turning a fragmented estate into a single trusted source the whole bank can draw on.
An investment bank with such a governed data layer can capture a client, instrument or transaction once, with every downstream function drawing on the same validated record. Reconciliation shrinks because there is less to reconcile. Regulatory reports can assemble themselves from data that is already lineage-tracked and audit-ready, and AI agents can act on information the bank trusts. Realizing this future takes coordinated work on four fronts:
1
A Unified, Trusted Enterprise Data Foundation
Everything downstream depends on a single, governed source of truth rather than a patchwork of system-specific records. Converging on a unified enterprise data layer with strong lineage, permissioning and quality standards allows information captured once to be trusted.
This creates an AI-ready data foundation that enables information to be re-used with confidence across client onboarding, reconciliation, regulatory reporting and other investment banking operations.
Capgemini’s research underlines the stakes, noting that without unified data foundations, pilots remain isolated and enterprise impact stays limited. Interestingly, Gartner predicts that through 2026, organizations will abandon 60 percent of AI projects that are not supported by AI-ready data.2
2
Governed Unstructured Data
A large share of the data that drives investment banking, including contracts, confirmations, term sheets and correspondence, is unstructured and sits outside formal controls. Closing this gap requires clear document governance, ingestion rules and sharing protocols, supported by intelligent document processing that captures, standardizes and tags information at the point of entry.
Bringing unstructured information within the governance perimeter allows investment banks to extend trusted data beyond structured records, creating re-usable information that supports AI-driven decision-making, regulatory reporting and intelligent operations across the enterprise.
3
Continuous, Audit-ready Reporting
With a trusted foundation in place, reporting can move from periodic, manual assembly to continuous, validated output. Shared distributed-ledger architectures strengthen data integrity and end-to-end traceability across regulated workflows, creating a tamper-resistant record so that transaction data is stored consistently and can be audited and verified.
The benefits extend well beyond reporting itself. When reporting is generated from trusted, continuously validated data, analysts can focus less on assembling information and more on interpreting it, enabling faster decisions, stronger regulatory confidence and more effective business oversight.
4
Embedded Data and AI Governance
As AI shifts from experiment to everyday use, the oversight governing it must extend just as far. According to Capgemini research, only 26 percent of banks today have a centralized AI governance model, while 49 percent report AI model reliability issues. In investment banking, where trust underpins every decision, that governance gap becomes especially significant.
Without clear accountability, accuracy thresholds, escalation paths, defined ownership and human oversight across the model lifecycle, AI-generated reporting risks becoming a source of operational and regulatory exposure rather than strategic value. Governance is not a constraint on the data foundation, but what makes it trustworthy enough for the rest of the business to build on.
The Governance Gap
26% of banks today have a centralized AI governance model.
Where Transformation Efforts Come Unstuck
Done at enterprise scale, the four elements outlined above can turn data from a liability into a compounding asset. As fragmented domains are brought under common governance, the same information serves reporting, risk, compliance and client insight without being re-built for each use, and the marginal cost of every additional report and model falls. The benefit accrues across the bank rather than in a single function, which is what makes the foundation worth getting right.
Doing so requires considerable effort. A governed foundation demands data engineering, governance design, regulatory fluency and the discipline to re-plumb live systems without interrupting the reporting that supervisors and clients rely on daily. Few institutions can assemble all of this in-house while continuing to meet regulatory obligations and support day-to-day operations, making experienced transformation partners critical to accelerating enterprise-scale change.
The cost of getting this wrong is well documented, with Capgemini finding that 82 percent of banking executives have seen no revenue gains from their innovation initiatives, and 51 percent have seen none of the cost savings they expected. The reason is consistent: When banks automate on top of fragmented, ungoverned data, they buy speed without trust, and the intelligence never follows the spend. A governed data foundation is what separates the institutions that achieve transformation from those that keep paying for potential.
A capable partner can shorten this path. Rather than a technology project bolted onto business as usual, the most effective engagements bring data engineers, governance specialists and domain experts together within a single operating model, fixing definitions, lineage and controls at the source while reporting continues uninterrupted. The aim is not to run the data on the bank's behalf, but to leave it with a foundation it owns and can build on.
The Future of Banking Data Management: From Fragmentation to the Foundation for Transformation
For too long, fragmented, ungoverned data has quietly capped the return on everything built on top of it, causing the AI, analytics and digital programs that promised transformation to deliver cost instead. Transformation has stalled less for want of investment than for want of a foundation solid enough to build on. Re-built as a governed foundation, data becomes the opposite — the asset every other capability draws on, and the difference between a bank that is reactive and one that functions proactively with confidence.
The institutions that lead the next phase will be those that stop reporting on their data and start trusting it: Governing it as a product, validating it continuously and holding the AI that reads it to the same standard as the transactions it records. KYC, client onboarding, reconciliation and regulatory compliance all depend on it, with AI-powered data management the foundation on which the next phase of intelligent banking operations is built.
Explore how AI-led data and analytics capabilities are helping banks turn fragmented data into the governed foundation that intelligent operations depend on.
WNS works with investment banks to build the data foundations that make Intelligent Operations real, from governance design and data engineering to regulatory-ready reporting.
About the Author
Garry Harrison
Senior Vice President,
Banking and Financial Services
Garry is a business leader and board advisor at WNS, with 25+ years of experience across technology, AI, FinTech and financial services. He advises organizations and investors on AI-led transformation, financial crime innovation, commercial strategy and business growth.
References
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World Corporate and Investment Banking Report | Capgemini
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Lack of AI-Ready Data Puts AI Projects at Risk | Gartner
FAQs
1. Why is data management becoming critical for investment banks?
Data is the foundation of regulatory reporting, risk management, client servicing, compliance, and AI-driven decision-making. Poor data quality and fragmentation limit operational efficiency and innovation.
2. What is an AI-ready data foundation?
An AI-ready data foundation combines governance, quality controls, lineage, accessibility, and enterprise-wide consistency to ensure AI systems operate using trusted and validated information.
3. Why does Agentic AI require trusted data?
Agentic AI autonomously performs tasks and makes decisions across workflows. Without trusted and governed data, AI can amplify errors rather than improving outcomes.
4. How can investment banks create a single source of truth?
Banks can establish a single source of truth through enterprise data governance, standardized definitions, data quality controls, master data management, and centralized architectures.
5. What are the biggest challenges in banking data management today?
Challenges include fragmented systems, legacy infrastructure, inconsistent definitions, poor lineage visibility, unstructured data growth, and regulatory reporting complexity.
6. How does intelligent data management improve regulatory reporting?
It enables real-time reporting, audit readiness, stronger traceability, improved data quality, and reduced manual reconciliation efforts.
7. What role does AI governance play in banking transformation?
AI governance establishes accountability, transparency, model oversight, risk controls, and compliance frameworks that ensure AI systems operate responsibly and effectively.
8. How can banks govern unstructured data effectively?
Through document governance, intelligent document processing, metadata standards, ingestion controls, and monitoring frameworks.
9. How does WNS help investment banks modernize data management and reporting?
WNS helps banks build trusted data foundations through governance design, data engineering, intelligent reporting, AI-enabled operations, and regulatory-ready operating models.
10. What business outcomes can investment banks expect from data modernization?
Benefits include:
- Improved data quality
- Faster reporting
- Reduced operating costs
- Better AI adoption
- Enhanced compliance
- Stronger decision-making
- Greater business agility