Author: Simon Axon, Global Financial Services Industry Strategist at Teradata
After years of experimentation, the banking industry has reached a critical turning point in its use of AI. Many financial institutions now have pilots or proofs of concept in progress, yet few have succeeded in achieving consistent operational results.
The issue here is not a lack of innovation, but the inability to deliver at scale. In AI, success is not measured by prototypes or dashboards but by what can perform effectively in the real world. Ultimately, execution is what really matters.
To understand why AI initiatives often fall short, it is essential to examine the key challenges that keep models from delivering practical outcomes.
Bridging the gap from model to outcome
Despite steady investment, many banks remain caught between intent and impact. This gap in AI adoption is caused by three ongoing challenges that hinder their progress.
Firstly, we have model operations, or ModelOps, that continue to present a major hurdle. Building a model is one step; deploying, monitoring and improving it in production is another. Too often, models are developed in isolation, with limited insight into their real-world performance. Without structured governance, monitoring for drift, and transparency in decision-making, models can lose both accuracy and trust.
Next comes data lineage. AI systems are only as dependable as the data that fuels them. When banks cannot trace the origin, transformation or reliability of data, models become opaque, and regulators will not accept systems that lack explainability.
Finally, business integration poses an ongoing challenge. Many AI projects are confined to innovation labs, disconnected from day-to-day workflows. The result is a collection of intelligent models that never reach the operational front line. Operational AI solves this by embedding intelligence directly into business systems, ensuring outputs translate into measurable actions rather than static insights.
AI without operationalisation is like an engine without oil. It may look advanced, but it will not perform under real-world pressure.
Overcoming these challenges, therefore, requires more than technology; it calls for clear oversight and well-defined governance to guide AI safely into operations.
Governance as a competitive advantage
Regulation, once seen as an obstacle, is now a key enabler of operational AI. Yet, scrutiny under the EU AI Act only increases; therefore, principles such as governance, traceability, and alignment between risk and finance data have become crucial. To gain this support, banks have turned to BCBS239, a standard that requires banks to continuously improve how they manage and report data.
Although originally introduced to strengthen risk data aggregation and reporting, BCBS239 now helps banks ensure their AI systems are transparent and well-governed. The framework is also shaping how the sector designs and manages AI systems responsibly, keeping up with the governance journey.
For banks, this means the path to AI maturity runs through compliance. Building transparent, explainable and well-governed systems is not optional; it is the only sustainable route to scaling AI safely and credibly.
With this in place, banks can move beyond experimentation and apply AI in ways that drive tangible business outcomes.
Operational AI in action
When applied effectively, operational AI moves beyond personalisation and prediction to strengthen how banks manage risk, detect fraud in real-time and improve ESG transparency by embedding intelligence directly into everyday decision-making.
These are no longer experimental use cases. Instead, they are strategic priorities critical to how banks compete, comply and survive.
To ensure these results are consistent, it’s also essential to consider the environments where AI runs, balancing speed, control, and compliance.
Placing AI where it performs best
For AI to deliver consistent results, it must operate in the right environment. This means placing AI closer to the data itself, using on-premises or hybrid models that maintain both innovation and control.
Processing data locally gives banks the speed, governance and security required for mission-critical workloads. On-premises AI provides full visibility, faster decision-making and compliance with data sovereignty rules, while cloud capabilities are still available where appropriate.
In industries where trust, privacy and responsiveness are vital, this hybrid approach ensures AI remains both effective and accountable. It places intelligence at the very edge of the business, not just within its analytical core.
Closing the AI gap
The financial sector is full of creativity and ambition in AI, but what is often missing is consistent operational execution, with the ability to turn insights into actions and innovation into measurable results.
Operational AI bridges that divide by ensuring models are scalable, reliable, accountable, and embedded in business workflows.
Banks that make this shift will redefine what it means to be data-driven. They will transform compliance into confidence, innovation into results and AI into a true competitive advantage.
Ultimately, success is not about how many pilots a bank runs but how many models deliver real outcomes. Operational AI is, quite simply, the only AI that matters.