Generative AI has firmly crossed the line from theoretical debate to day-to-day operational reality for compliance teams. Over the past year, many firms have moved beyond pilot programmes and controlled experiments, embedding AI into real workflows.
This shift has been driven by mounting regulatory complexity, persistent resourcing pressures, and the challenge of maintaining consistent compliance standards across multiple jurisdictions.
However, rapid adoption has brought a more difficult question into sharper focus. How can compliance functions unlock the efficiency benefits of AI without surrendering accountability?
Zeidler Group recently delved into AI in compliance and how to bring efficiency without abdication.
Based on ongoing work with firms and regular engagement with regulators, the answer appears to lie less in the sophistication of the technology itself and more in how it is governed and applied, it explained.
One increasingly common approach is to treat AI-generated output in the same way as work produced by third-party providers. Compliance teams are already accustomed to assessing external inputs based on risk, materiality, and the nature of the task. Applying the same logic to AI means accepting that low-risk activities may require lighter review, while higher-risk decisions still demand rigorous scrutiny and clear justification.
The past year has also reinforced why human-in-the-loop models have become the default for effective AI adoption in compliance. Concerns around hallucinations, over-automation, and unanticipated errors remain very real. Current generative AI technology is not yet robust enough to eliminate the risk of harmful mistakes without meaningful human oversight.
Regulatory attitudes have also evolved. Rather than rejecting AI-assisted compliance outright, regulators have shown growing openness to tools that can enhance consistency and operational efficiency. At the same time, expectations are rising. If compliance becomes easier through automation, firms will be expected to deliver better documentation, clearer audit trails, and more robust explanations.
Governance and bias have emerged as some of the most complex challenges in live deployments, it said. Generic, firm-wide AI policies have often proved insufficient on their own. More effective approaches apply governance at the task level, using subject matter experts, structured sampling of outputs, and periodic review processes tailored to specific use cases.
Looking ahead, one lesson stands out clearly. Generative AI can make compliance faster, more scalable, and more consistent, but it does not remove responsibility.
For more insights, read the full story here.
Read the daily FinTech news
Copyright © 2026 FinTech Global
Copyright © 2018 RegTech Analyst
