Why AI fails without the right data—and how banks can fix it

JUN. 26, 2025

3 Min Read

Lumenalta

In banking, even a small AI model can deliver big results if it’s built on a modern, well-structured data foundation.

Even a small, specialized AI model will stumble without a solid data foundation.

Over 80% of AI projects fail to deliver due to issues like poor data quality and a lack of relevant data. In banking, no AI without good data is a guiding principle; success hinges far more on the information fueling your models than on the models themselves. In contrast, when a bank’s data is unified, accurate, and accessible, even modest AI tools can produce powerful insights, accelerating customer service, improving risk assessments, and driving measurable returns.

Key Takeaways

1. Quality of data is more important than the size or sophistication of the AI model in determining success.
2. Siloed, poor-quality data will cause even the most advanced AI initiatives to falter.
3. A modern, unified data platform ensures information is clean, accessible, and governed – the essential fuel for effective AI.
4. Data and AI strategies should be planned together to meet regulatory requirements and achieve clear business objectives.
5. With a strong data foundation in place, small language models can be deployed quickly to drive major improvements in customer experience, efficiency, and risk management.

Small AI models only deliver real results with a solid data foundation

Banks racing to adopt AI often discover that garbage in, garbage out is more than a cliché. Virtually all AI initiatives encounter data hurdles. One industry survey revealed that over 95% of AI and machine learning projects face data quality issues during execution. This underscores that the secret to AI success isn’t having the biggest model, but having the best data environment.

Crucially, smaller domain-specific models thrive on high-quality, well-structured data. Unlike large generic algorithms, SLMs train on a bank’s own datasets, transaction records, customer profiles, fraud patterns, and more. If that data is siloed or inconsistent, the model’s recommendations will be incomplete or misleading. But a clean, integrated data foundation gives these AI tools a full view of the business. A finely tuned SLM can swiftly spot a fraudulent transaction or pinpoint a client’s need for a new service because it draws on comprehensive, up-to-date information. In summary, small AI models can deliver significant results, but only when backed by a modern data infrastructure that provides truth instead of noise.

Don’t scale your AI ambitions on broken data. Modernize first.

Legacy data silos risk AI compliance in banking

Many banks have invested heavily in AI only to face inconsistent performance and delayed results. The underlying problem is not the technology but the fragmented and outdated data infrastructure supporting it. When information is trapped across disconnected legacy systems, even advanced models produce flawed outputs. This prevents AI from delivering reliable insights and slows the path to measurable value.

Incomplete insights from fragmented data: More than half (57%) of banking executives lack a unified customer view due to siloed data, so AI models see part of the picture, leading to flawed decisions.
Slow, inefficient processes: Data silos prevent real-time analysis. If some channels don’t feed data to AI, fraud detection is delayed.
Inconsistent data quality and governance: Different systems produce duplicate or contradictory data and misleading algorithms. Without unified oversight, errors spread and undermine trust.
Rising costs and stalled innovation: Banks spend about 70% of their IT budgets maintaining old systems, leaving few resources for new AI solutions.
Heightened compliance and security risks: Disconnected data makes it hard to ensure consistent reporting and privacy controls, increasing regulatory risk.

Legacy silos don’t just inconvenience IT teams, they cripple a bank’s ability to deploy AI with confidence. When key information is locked away or inconsistent, AI outcomes become unreliable and hard to explain. That makes both regulators and executives uneasy. To fix this, banks must break down the walls between systems. Only by modernizing how data is stored and integrated can AI move from experimental pilots to trusted, everyday tools in banking operations.

“Even a small, specialized AI model will stumble without a solid data foundation supporting it.”

Modern data platforms unlock AI’s true potential in banking

A modern, well-structured data platform is the antidote to these legacy issues. Unifying disparate data sources into a single accessible repository helps banks create a launchpad for AI to flourish. The banking sector could gain up to $16 trillion in value from AI by 2030.

With a modern data platform, information flows freely across the organization. Without manual exports or delays, an AI application can instantly draw on data from all relevant sources, such as transactions, mobile app logs, market feeds, and fraud alerts. Freed from weeks of data wrangling, teams can deploy models faster and update them continuously, shrinking time-to-market for AI-driven services.

Equally important, data quality improves. Modern platforms include built-in cleaning, standardization, and governance, so models consume consistent, verified information and produce more accurate insights. For example, a customer onboarding AI assistant can auto-fill forms and flag discrepancies by cross-checking multiple sources, while a fraud detection model correlates patterns across accounts in real time to catch schemes that siloed systems would miss. High-quality data plus seamless access empowers small AI models to tackle big challenges, from more personalized customer recommendations to better risk management, with greater confidence and ROI.

CIOs must align data and AI strategies for real business results

AI efforts stall when data and strategy are developed in isolation. CIOs are expected to deliver measurable results, but too often, projects are launched without ensuring the organization’s data infrastructure can support them. Success requires a deliberate connection between AI ambitions and the readiness, quality, and structure of the underlying data.

Prioritize data maturity before AI experimentation

Before deploying advanced algorithms, banks should first achieve a high level of data maturity by modernizing core systems and instituting strong data governance. CIOs must champion initiatives that consolidate data silos and improve data quality enterprise-wide. When data is reliable, well-defined, and governed, any AI project has a much stronger foundation. Notably, 93% of banking tech leaders say their company’s future success hinges on modern core data infrastructure, underscoring that getting data right is non-negotiable.

Tie AI use cases to clear business goals

Every AI initiative should start with a specific business problem and an honest look at the available data. Rather than chasing hype, banks choose targeted use cases and then verify that their data can support the model’s requirements. This approach ensures the project is tied to a real business objective and that any data gaps are identified upfront. Early collaboration between data and AI teams allows gaps to be addressed, setting the initiative up for success.

Embed compliance and ethics into the data-AI lifecycle

Any AI strategy in banking must meet strict regulatory and ethical standards. CIOs should bake compliance checkpoints into data management and model development from day one. That means maintaining auditable data lineage and enforcing privacy and security controls, while on the modeling side, using explainability and bias testing as standard practice. By aligning data governance with AI governance, banks ensure new AI solutions perform well while meeting regulations and maintaining customer trust.

AI cannot operate effectively without the right data conditions, and those conditions require strategic oversight from technology leaders. When CIOs align data priorities with AI execution from the start, banks gain control, reduce risk, and position their AI initiatives to deliver consistent, actionable results.

“Only by modernizing how data is stored and integrated can AI move from experimental pilots to trusted, everyday tools in banking operations.”

Accelerating data-driven AI innovation with Lumenalta

Making these strategic alignments a reality often requires a specialized partner, and that’s where Lumenalta comes in. We work alongside CIOs to modernize data platforms and integrate AI solutions, ensuring each new system is part of a cohesive strategy and not just a one-off deployment. Establishing a high-quality, unified data layer first sets the stage for small AI models to be deployed faster and with greater confidence. This approach cuts time to value and reduces risk. When your data is trusted and well-governed, AI projects avoid common pitfalls and start delivering insights sooner.

From streamlining compliance reporting to uncovering new revenue opportunities, every solution is tied to concrete outcomes. We focus on the metrics that matter, from cost savings to customer satisfaction. With expertise in financial data architecture and AI, we bridge the gap between vision and execution. The result is a future-ready bank that can use small language models as reliable drivers of efficiency and growth, thanks to a rock-solid data foundation.

Table of contents

Small AI models only deliver real results with a solid data foundation
Legacy data silos risk AI compliance in banking
Modern data platforms unlock AI’s true potential in banking
CIOs must align data and AI strategies for real business results
Accelerating data-driven AI innovation with Lumenalta
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