Why your bank’s AI pilot failed and how to fix it
Most banks’ AI pilots fail not because of poor models, but because their legacy core systems and data layers can’t support real AI in production.
AI use case prioritisation matrix for banks
Every bank is under pressure to do something with AI, but not every use case should be first.
This matrix helps product, tech, data, risk, and compliance teams decide where to start and what to park.
Use the template to score AI ideas across impact, feasibility, data readiness, regulatory sensitivity, and time-to-value – so you can move beyond hype and focus on use cases that are both realistic and meaningful.
Fill in the form below to download the AI use case prioritisation matrix.
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90-Day AI pilot planning template
This 90-Day AI Pilot Planning Template helps banks turn AI from a slide in a board deck into a live, measurable pilot.
In four simple sections, it guides your product, tech, data, risk, and compliance teams to align on one realistic use case, the data it needs, and a safe 90-day timeline.
It also covers governance, success metrics, and rollback plans so you can move fast without adding uncontrolled risk. Download the template to leave your next AI discussion with a concrete plan.
The digital-first bank’s guide to AI in 2026
Financial institutions are pouring $97 billion into AI by 2027, but here's what nobody tells you: 95% of AI projects fail to deliver on their promises. The reason? Bad data. Not bad algorithms, not insufficient budget, just terrible, siloed, inaccessible data that no AI can work with.
Before you buy another chatbot platform or fraud detection system, ask yourself: can you actually access your own banking data in real-time? Can your data scientists query your core without crashing production systems? Do you have a single source of truth, or are you duct-taping together CSV exports and hoping for the best?
This guide shows you how to build the foundation that makes AI actually work. We cover everything from credit scoring with alternative data to operational automation that cuts costs by 40%. But most importantly, we show you why your data layer matters more than any algorithm and how to fix it before you waste money on AI that goes nowhere.
Think bigger. Go further.
Come and see the future with us. Talk to one of our core banking experts.