4 Blockers Stalling AI Implementation in Banking
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Banks know AI is a competitive play. KPMG’s Intelligent Banking report found 80% of executives believe the banks that embrace AI will gain an edge over those that do not. The intent is there. What’s missing is connection across systems, data, teams and governance, which is why AI often sits beside the work rather than inside it.
The truth is that the foundation is shaky. The same report found that most banks sit in early “Enable” phases, and only 25% have enterprise-wide cloud or hybrid-cloud platforms for data-driven services. With that kind of runway, pilots struggle to graduate into day-to-day operations. This is why so much AI lives beside the work instead of inside it.
As Assad Mahmood, Vertical Consulting Lead in AI banking solutions at vector8 explains: “It’s a proper industrial revolution that we’re living in. If the belief is that this is just a tech phase, and that banks can capture value by doing proof of concept and give 0.5% of the overall budget allocation to AI, it’s not going to work.”
The symptoms vary by bank, but the pattern is the same. Regulation pulls focus, legacy slows change, teams lack the right skills and work fragments without a clear vision. Fix these four and AI implementation in banking starts to scale.
Too many banks treat AI as a set of neat pilots, not a company strategy. That is why demos look great, then vanish. AI ends up bolted on to existing processes instead of improving how work actually gets done. You get fragmentation, duplicated effort, thin adoption and tomorrow’s legacy systems built today.
Assad explains that vision is key, but that leaders often take a narrow perspective. “They see somewhere they can use AI, but the real value comes from having a real purpose for it. You need to think bigger — where are you going as a company, and how can AI help?”
Regulators often set the tempo inside the bank. New rules land, teams drop everything, and compliance takes centre stage. You cannot flip a switch on a new model or park sensitive data in the cloud and hope for the best. Every move needs clear answers on privacy, security, explainability and monitoring.
Assad has a positive take, saying, “Regulators are asking for transparency. They are not blocking any use cases. They are asking for observability, for making sure you are not misleading the client, for keeping the client’s care at the centre of what you do.” The fix is to design for compliance from the start, not retrofit it at the end. Build with a blended team so risk and delivery move together.
For many enterprise banks, legacy systems can feel like an impossible mountain to map, never mind climb. Assad puts it plainly: “Changing anything in the core banking system is a massive project. It’s not just about technology, it’s about risk, compliance and the fact that nobody wants to be the one who breaks something that’s been running for decades.”
This is where AI in banks often stalls. The way through is to use reuse to chip away at technical debt. Build on a common base so every new use case plugs into the same rails. Swap one-off integrations for standard adapters. Touch fewer brittle systems. Turn risky big changes into small, safe steps. Reusable components make it possible to strangle legacy over time rather than fight it head on.
Many bank tech teams are built to keep the lights on. AI implementation in banking needs a different toolkit. You need people who can package models, wire retrieval, run evaluations, watch drift and produce evidence that stands up to audit. The work spans data, product, risk and site reliability engineering (SRE). If those skills are scattered or part-time, delivery crawls.
Cloud also raises the bar. Ways of working differ across providers, so strong in-house teams often need to relearn how they build and ship, and tighten data protection for each environment. In practice that can mean retraining for AWS or Azure and setting up clearer privacy and oversight routines for cloud services. Pick one secure way to build, teach it, and give a small squad the space to make it the default.
AI in banking is evolving quickly. New models, tools and frameworks arrive every few months and each brings its own learning curve. Inside banks, specialisms are growing: GenAI experts, model risk specialists, data engineers and cloud architects. The work can move faster than the structures built to support it.
It’s a lot to juggle. Bring the specialists together around one plan and make it easy for teams to build on each other’s work. When skills, systems and priorities line up, the pace starts working for you, not against you.
Banks can see the huge opportunity of AI, but moving it from the sidelines to the core of their strategic vision is no easy task. Regulation pulls focus, legacy slows change, skills are stretched and processes can quickly fragment when you take a verticalised approach.
vector8’s AI Implementation System (AIS) gives you a way to turn wins into something you can reuse and grow, so AI implementation in banking is easier to scale without creating more technical debt.
It gives you a shared base to build on, with reusable accelerators that become skills inside everyday tools. AIS fits wherever you are on the journey and works with your existing systems and tools, so you can move forward without a rebuild.
Want to see how our approach can help you tackle your AI blockers? Talk to our team and find out where you can start to chip away at your biggest obstacles
Regulatory pressure, legacy systems and technical debt, tech team maturity and skills gaps, and siloed projects that create fragmentation. They show up as stalled pilots, long approval cycles, brittle integrations and low adoption.
Start small but design for reuse. Pick one use case that fits inside existing tools, build it on shared components and accelerators, then reuse those parts in the next case. That is how you move toward scalable AI in banking.
It rewards teams that embed risk early. Work with blended squads, design for transparency and observability and keep evidence consistent across projects. Do that and GenAI in banking features move from pilot to production faster without adding new fragments.
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