The Silo Effect: Why Fragmented AI is Failing Banking
Right now, fragmented AI in banking is holding back progress. Fragmentation boils down to a lack of connection in three key places: technology (disconnected stacks), organisation (isolated teams), and delivery (one-off projects or products).
This disconnection creates “the silo effect”, where AI initiatives, data and capabilities remain isolated, preventing reuse and scale. Assad Mahmood, Vertical Consulting Lead in AI banking solutions at vector8, explains:
“The biggest thing I'm seeing these days is the silo effect. Many companies have tons of things in the pipe, but nobody's talking to each other to create those low-level bricks that can be reused. My feeling is that in 2025 they will achieve a lot, but they have created the legacy of next year rather than building on something they can expand.”
Over time, that fragmentation creates drag, even if a solution is functional. Assad sums it up neatly, “It’s not going to fail. But it’s not going to scale.”
Current research backs this take. A recent BCG survey found that only 25% of institutions have woven AI into their strategic playbook. The other 75% remain stuck in siloed pilots and proofs of concept.
Disconnected AI thinking sees technical debt build, regulatory compliance get harder and teams stretched thin. Banks stuck in silos repeat work. Those that design for connection, reuse and scale build once and benefit many times.
Fragmentation doesn’t look the same in every bank. A retail bank piloting onboarding automation will experience it differently to a private bank trialling call transcription or an asset manager testing research tools. Some institutions are starting to connect these wins with shared data and governance. Many are still stuck in silos, with progress boxed into a single team or function.
Most journeys begin as projects. A project fixes a local problem on a deadline. A product is different. A product is a reusable capability with an owner and a roadmap, built to serve for the long term. If work stays a project and never becomes a product, it stays local and scale stalls. Connection comes from turning projects into products, then making those products reusable and shareable so value can travel.
Assad explains that a consumer mindset has shaped too much early AI thinking: “ChatGPT is customer-centric, so individuals do whatever they want as users. But at an enterprise level it cannot work like that. AI should be built to connect across the bank, not stay locked inside individual projects.”
Tools designed for individuals encourage isolated adoption, not connected systems. What looks like quick progress at the edge creates what Assad calls “the legacy of next year” at the centre.
Siloed AI solutions and fragmentation often grow out of good intentions: teams solving real problems, often under time and budget pressures. But when those solutions stay narrow, they hold back progress and stop AI becoming a genuine force multiplier.
These patterns all stem from the same issue: a lack of connection across systems, data and teams:
Without a shared roadmap, similar tools are built again and again across different teams. A compliance pilot here, a know your customer (KYC) tool there. Instead of one shared capability, banks end up with multiple versions of the same thing, adding cost instead of removing it.
Pilots that succeed in isolation rarely integrate smoothly with core systems. Each short-term fix becomes another piece of “the legacy of next year,” piling up complexity that slows future adoption.
In banking, regulation is intense and constantly changing. When systems are siloed, every new rule demands fixes in multiple places, adding cost and delay. Assad points out that regulators are not blocking innovation, but they are “demanding more transparency and observability”, with compliance baked in from the start. Fragmented systems make that difficult, leaving banks reactive when they need to be proactive.
IT and data teams already stretched thin are left to maintain multiple disconnected tools. Many do not have the skills or capacity to support AI consistently, so projects stall after the pilot stage.
Fragmentation might look like progress in the short term, but it erodes competitiveness over time. As Assad warns, “If you start to use out of the box solutions, you will have an out of the box experience, and there will be no way to differentiate bank A, bank B, bank C, because they all do the same.”
JPMorganChase shows what is possible when AI is treated as a connected strategic capability rather than a collection of pilots. A 2025 Harvard Business School review explored how the bank placed GenAI tools on the desktops of 200,000 employees and invested 17 billion dollars in technology and data foundations The bank has already credited AI with delivering up to 1.5 billion dollars in value through fraud prevention, personalised service and operational efficiency. By embedding connectivity from the start and building a unified platform, JPMorganChase has turned scattered use cases into enterprise-scale capability.
When AI scales, the benefits multiply. A clear example is KYC, where banks must collect and process client documents to meet strict regulations. A document extraction tool built once for retail KYC can also support SME onboarding and help asset managers review investment research. In private banking, a call transcription tool designed for relationship managers can be reused in corporate banking to support compliance checks. Building once and applying many times reduces cost, avoids duplication and embeds compliance early.
As Assad describes it, progress depends on creating “low level bricks that can be reused.” vector8’s accelerators and skills bring this to life, combining pre-built components for tasks like document parsing or message classification with skills that adapt them to a bank’s own data and workflows. Together they form the building blocks of our AI Implementation System (AIS), vector8’s blueprint for enterprise AI. The AIS connects existing systems, integrates third-party tools and embeds compliance from the start, turning scattered wins into a system that scales.
We’re already seeing this shift in practice. At a leading private bank, transcription tools laid the groundwork for scalable expansion of conversational intelligence across the organisation. Elsewhere, a global retail banking and payments provider rolled out document automation establishing a flexible foundation for further automation initiatives. These examples show how reusable components, built with scalability in mind, move AI beyond pilots and into enterprise capability.
vector8’s AIS is designed as a blueprint for scale. It is not a replacement for the systems banks already use. And it doesn’t mean organizations have to strip everything back and start again. Instead, it provides the structure to connect them, integrate third-party tools, and stop innovation from getting trapped in silos.
AIS helps banks:
AIS gives banks the structure to connect what already exists—systems, data and teams—so progress scales instead of fragmenting.
Isolated pilots and side-of-desk projects add up to duplication, technical debt and compliance challenges that make it harder to compete. The banks that move fastest treat AI as a strategic capability and build connection: systems that talk to each other, data that moves securely across domains, and components that are reusable and shareable so value can travel.
Connection shows up in practice as shared interfaces and data, common governance and monitoring, and a platform where new use cases add to what came before instead of starting from scratch. When connection comes first, scale follows.
Assad echoes the sentiments of Nvidia founder and CEO Jensen Huang, “AI won’t replace you. But someone using AI probably will.” Leaders are already moving. Now is the moment to decide how you keep pace.
Talk to the vector8 team about how our AIS can help you cut through banking silos and build AI that lasts.
AI silos in banking happen when teams build isolated tools or pilots that do not connect with each other or with core systems. Each project may solve a problem locally, but without integration and reuse the bank cannot scale its AI capabilities.
Fragmentation slows AI maturity in banking by forcing teams to rebuild the same capabilities in different places. Instead of creating shared foundations, banks add duplication and technical debt. True maturity comes from reuse and integration across functions.
AI regulation in banking requires transparency and compliance by design. When AI is fragmented, every new rule adds cost and delay because fixes must be applied in multiple places. Scalable AI makes regulation easier to meet with consistent governance across the organisation.
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