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Breaking the data ceiling in financial services: a path to AI at scale
Whitepaper
For Senior IT leaders in Banking, Financial Services, and Insurance Industry (FSI)
Executive summary
Iron Mountain’s global report, "Breaking through the data ceiling," identifies three organizational archetypes based on their data maturity and readiness to scale AI: Custodians, Connectors, and Accelerators.
- Custodians (low content intelligence maturity - 17% of organizations surveyed): This group is characterized by manual processes, siloed data and legacy technology. Because their data remains fragmented, their estate creates AI agent blind spots that stall adoption at the pilot stage.
- Connectors (moderate content intelligence maturity - 75% of organizations surveyed): For these organizations data and technology modernization is underway but plagued by AI pilot fatigue they are experiencing some gains or business impact. They can support basic AI use cases, but disconnected data, systems, and governance prevents scaling autonomous agents enterprise wide.
- Accelerators (high content intelligence maturity - 8% of organizations surveyed): These are our market leaders who are furthest ahead in AI innovation, leveraging higher rates of modern cloud architecture and automated workflows to deliver enterprise insights and measurable business performance. By building an AI-ready content foundation, they have established an active trust layer required to scale specialized AI agents.
Most global enterprises (75%) sit in the middle tier as Connectors. These organizations have initiated data, system, and workflow modernization, but struggle to scale those efforts. They lag top-tier Accelerators (the market leading 8% of companies) in their ability to scale AI beyond pilots and advance lasting business impact.
The root of this divide is structural: Accelerators are prioritizing investments in a unified content intelligence layer and deploying modern, AI-native capabilities that help discover, govern, and activate data into operations. By turning siloed, unstructured files into structured, AI-ready information, Accelerators are leading when it comes to feeding clean AI-ready data directly into models, specialized agents, and mission critical workflows.
When financial services, banking, and insurance (FSI) organizations were more closely examined within this global dataset, a new pattern emerged with implications for how IT leaders in these industries should think about their specific roadmaps. This paper aims to compare this sector relative to the global benchmarks, unpack the specific barriers that are dragging AI adoption in financial services, and recommend actions to help unlock next level growth and business value for this sector.
Despite persistent data and infrastructure challenges FSIs report high-performance gains
When comparing FSI organizations to global benchmarks we found that they experience similar challenges related to technical debt, legacy maintenance costs, and unstructured data silos.
Even with similar levels of legacy infrastructure burden, FSI organizations report higher levels of performance when it comes to decision speed, team productivity, and AI readiness.
*Percentages in brackets represent the relative difference in the average reported increase between FSI and non-FSI respondents.
While this business performance is commendable, it likely masks a pending structural limit. Our research also shows FSI organizations are leaning on manual processes, carrying high legacy maintenance costs, and ignoring dark data archives. Outperforming their peers in other industries might create a false sense of security, causing businesses to mistake operational grit for AI scalability.
A hardened FSI data ceiling
FSI organizations face unique modernization hurdles that make their data ceiling harder to break through compared to other industries:
- Trapped physical estates. Decades of accumulated non-digitized content such as collateralized lending files with paper trails, wet-ink loan collateral, and physical vaults remain isolated alongside other records fragmented across legacy ECM systems.
- Ingestion failures and trapped knowledge. Conventional Optical Character Recognition (OCR) engines routinely fail to reliably parse complex tables, multi-column layouts, footnotes, low-resolution faxes, and handwriting keeping vital financial intelligence aware from AI models.
- Strict security mandates. Massive volumes of fiduciary records contain non-public personal information (NPI) and sensitive trade data requiring rigid isolation to prevent unauthorized exposure.
- Mandatory oversight. Stringent regulatory frameworks require audit-ready, human-in-the-loop validation for automated AI outputs to eliminate the risk of AI hallucinations and satisfy governance.
(Our) data volume is doubling rapidly, increasing expenses and the workload associated with legacy storage systems.
These constraints drain both IT capital and engineering time, preventing even high-performing institutions from retiring legacy debt and investing in AI-ready infrastructure.
Only 34% of FSI respondents report that their content is connected and readily accessible across systems—below the 52% reported by respondents from other industries in the global research.
Because FSI organizations often outperform cross-industry peers in certain business metrics, it's possible for IT leaders in this sector to underestimate how much their data estate is acting as a ceiling on performance. Breaking through means confronting compliance and legacy-IT hurdles that most other industries do not face with the same intensity.