Artificial intelligence is rapidly reshaping how financial institutions manage regulatory change, but its success depends far less on the sophistication of the model and far more on the environment surrounding it.
According to Corlytics, as organisations look to streamline how they classify new regulations, identify emerging obligations, and flag areas of potential risk, AI is increasingly seen as a fundamental tool.
Yet, its impact is only meaningful when supported by strong data foundations, robust governance, and a clearly defined operating model.
Many compliance leaders acknowledge the potential of AI but hesitate to label themselves experts. What becomes immediately clear, however, is that AI can be exceptionally powerful when designed with strong fundamentals—or ineffective when those foundations are missing. The real difficulty is not building the model itself. It is creating the conditions that allow it to perform reliably, transparently, and at scale.
A successful regulatory change management framework demands that three interconnected components work in harmony: high-quality regulatory data, an efficient operating model, and governance that ensures control and oversight.
The quality of data remains the first—and often biggest—barrier. AI systems can only perform as well as the information they ingest. If regulatory content is incomplete, inconsistently formatted, or disconnected from contextual insights, those weaknesses will be amplified. Effective models are driven by regulatory data that is authoritative, structured, and linked to taxonomies, jurisdictions, risks, and relevant enforcement trends. Without this level of organisation and verification, AI may accelerate processes, but it will also accelerate errors.
For many firms, this is where the struggle begins. They may have access to advanced AI tools, but their underlying data does not meet the standard required to properly support them. Strong AI does not start with code—it begins with clean, connected regulatory intelligence.
Once this data foundation is established, the next challenge is integrating AI into the organisation through a clear operating model. At Corlytics, this is understood across three dimensions: people, process, and systems. Teams need defined responsibility across compliance, risk and technology functions, along with the ability to interpret and validate AI outputs. Processes must incorporate AI insights into existing review cycles and workflows. Systems must be interoperable, ensuring regulatory data and automation function cohesively across different platforms.
Central to this is governance and human oversight. AI should not replace professional judgement. Its role is to enhance decision-making, not override it. Firms that successfully operationalise AI bring these elements together, enabling consistency and scalability that would be impossible with manual approaches alone.
When high-quality data is combined with strong operating models, regulatory change management can finally move from reactive to predictive. Updates can be automatically classified, prioritised and contextualised, and risks can be identified earlier, allowing teams to focus on strategic work rather than administrative triage. At this stage, AI becomes less of a buzzword and more of a practical driver of efficiency and resilience.
Ultimately, AI provides the power, data delivers the intelligence, the operating model defines its purpose, and governance ensures trust. The objective is not to become AI specialists, but to understand how to deploy AI within the regulatory frameworks and governance structures that institutions already manage.
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