When you purchase through links on our site, we may earn an affiliate commission.Heres how it works.
The heart of the challenge isnt AI itself, its data.
In many institutions, outdatedinfrastructure, fragmented architectures, and siloed legacy systems continue to dominate.
These setups were never intended to handle the scale and urgency that modern AI demands.
Chief Product and Innovation Officer at Gresham.
The missing link in data readiness
AI algorithms crave completeness, accuracy, and consistency.
Yet firms often rely on data systems that are anything but unified.
Its no surprise many financial institutions struggle to reconcile different feeds or update data in real time.
As a result, data quality quickly degrades.
This discrepancy is telling: where data remains siloed or inconsistent, AI efforts stay small-scale or purely exploratory.
Where data strategies are robust, AI initiatives advance more rapidly.
Investments indatamanagement are also rising.
Communication breakdowns between AI models and legacy platforms can lead to inconsistent data flow and unreliable outputs.
The question of talent is another barrier.
Legal and licensing matters add further complication.
There are also concerns about inadvertently exposing confidential information through AI-driven tools.
The legal and operational frameworks that govern these new technologies continue to lag behind AIs rapid adoption.
Older data cataloguing and governance tools cant keep pace with these evolving requirements.
A static dictionary that flags ownership or acceptable values doesnt capture the contextual nuances that AI demands.
Moving forward - Why bother?
Its easy to see why some remain skeptical.
Retooling data infrastructure, refining governance, and hiring specialized staff can be expensive and time-consuming.
However, the benefits of AI are too significant to dismiss.
Institutions that manage to integrate AI properly often find themselves making decisions faster and catching opportunities that competitors overlook.
Efficiency gains can be especially compelling.
Moreover, AI-driven personalization can deepencustomerrelationships.
This means modernizing legacy architecture, ensuring data quality is consistently maintained, and creating clear guidelines aroundprivacyand licensing.
It also requires investing in people who can bridge the gap between software engineering, finance, and regulation.
Meanwhile, legal teams must keep pace with changing models, usage restrictions, and licensing obligations.
So, can AI truly deliver in financial services?
The short answer is yes: provided institutions build the necessary groundwork.
Genuine success demands reliable foundations.
We’ve featured the best AI website builder.
The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc.
