Why AI governance is becoming more important than automation

Compliance is changing faster than ever. New regulations, rising expectations from regulators, international sanctions regimes, stricter anti-money laundering obligations and the rapid rise of artificial intelligence are putting compliance departments under pressure. But that’s not all. At the same time, organisations expect greater efficiency, faster decision-making and lower operational costs. Technology is meant to help achieve this. However, an increasing number of compliance experts are realising that automation alone is not enough.

The real challenge for 2026 is not how organisations can deploy more technology, but how they can maintain control in an increasingly complex risk landscape.

During the webinar The Future of Compliance Operations: AI, Regulatory Pressure & Managing True Risk, Kemal Sahin and Emmanuel Florendo, compliance experts from the financial sector, and Nileema Ali, Senior Product Manager for Risk & Compliance at Creditsafe, discussed how organisations are currently dealing with this new reality. The insights from the webinar, combined with a prior survey of compliance professionals, clearly demonstrate where the priorities lie for the coming years.

The survey results leave little room for interpretation.

• No less than 50% of respondents regard cross-border regulation as the biggest challenge for their organisation.
• In addition, 67% say they are concerned about over-reliance on AI and a lack of human oversight.
• A further 20% see data privacy and data security as significant risks when deploying AI.

Download the survey report

Many organisations today have sufficient information to keep abreast of new regulations. The problem does not lie so much in monitoring changes.

The real challenge arises when regulations need to be translated into operational processes.

• How do you ensure that new requirements are integrated effectively and efficiently into existing controls?
• How do you prevent changes from leading to extra manual work or inconsistencies in implementation?

The most mature organisations therefore no longer treat changing regulations as a legal or administrative process, but as an operational discipline.

They start with risks and business activities, determine which regulations actually have an impact on their organisation, the criticality of the impact; and then translate these into concrete controls, workflows and responsibilities.

This approach not only makes compliance more efficient but also ensures that organisations can respond more quickly to future changes.

In recent years, AI has established itself in numerous disciplines, including compliance. From customer due diligence and transaction monitoring to negative media screening and risk analysis, more and more processes are being supported by algorithms and machine learning.

However, there is also a growing realisation that technology alone does not provide a solution.

When an AI model classifies a customer as high-risk, issues an alert or flags a transaction, one question remains central: who is responsible for that decision?

That is precisely why AI governance is becoming one of the most important areas of focus within compliance.

Proper AI governance means that organisations can clearly explain how a system arrives at a particular recommendation. It also means that decisions can be reviewed, challenged and accounted for.

In practice, a strong governance framework is based on four fundamental principles:

• human oversight of critical decisions
• transparency and explainability of models
• clear structures regarding ownership and responsibilities
• continuous monitoring of performance and risks

The message from regulators is becoming increasingly clear. Organisations may use technology to improve processes, but people still bear ultimate responsibility.

A striking observation within many compliance organisations is that the workload remains high, despite increasing investment in automation.

The cause often lies not with the technology itself, but with the quality of the processes being automated.

When poor-quality data, inefficient workflows or unclear controls are automated, this does not usually result in a more efficient process. The organisation simply processes the same problems more quickly.

This explains why many compliance teams still struggle with:

• high volumes of alerts
• large numbers of false positives
• time-consuming review processes
• complex configuration of monitoring tools

The organisations achieving the greatest efficiency gains today therefore focus first on process optimisation and data quality before investing in further automation.

After all, automation only works when the underlying processes are sufficiently mature.

One of the most recurring challenges in compliance is managing alert volumes.

For years, the focus was on processing as many alerts as possible. Today, we are seeing a fundamental shift, with the emphasis moving towards the quality of the alerts.

A system that generates thousands of alerts is all well and good. But if it detects only a limited number of relevant risks, it mainly creates extra work. Compliance professionals then spend their time assessing irrelevant signals rather than addressing the real risks.

That is why the market is increasingly prioritising alert quality over alert volume.

This means:

• better data quality
• more accurate models
• stronger risk indicators
• continuous optimisation of detection rules

The ultimate goal is not to find more possible risks, but to detect more probable risks more quickly.

In addition to internal governance, there is also growing focus on external technology partners.

More and more organisations are using specialised AI solutions, cloud platforms and external data sources. This creates new risks relating to data protection, ownership and control.

For compliance teams, this means that vendor due diligence is playing an increasingly important role.

Key questions in this regard include:

• Where is data stored?
• Is data used to train external models?
• What audit rights does the organisation have?
• Who has access to sensitive information?
• How are cross-border data flows managed?

The reality is simple: technology can be outsourced, but responsibility cannot.

Organisations that invest in robust vendor governance today not only build greater trust with regulators but also mitigate their operational risks in the long term.