Cost-effective regulatory compliance powered by artificial intelligence

Artificial intelligence (AI) is increasingly becoming vital for companies to manage the ever-expanding array of regulations they must navigate, offering a more consistent, efficient, and practical approach to compliance. Modern regulations now encompass a broad spectrum of organizational activities, including health and safety, data protection, cybersecurity, bribery, fraud prevention, and consumer protection, making it crucial for businesses to mitigate risks in these areas. Organizations must also meticulously document their efforts to ensure accountability in case of breaches and streamline coordination between various departments and compliance functions. However, these tasks' sheer scale and complexity have grown beyond the capacity of manual processes to handle effectively. Large language models (LLMs), the advanced computational systems powering generative AI, are proving instrumental in addressing this challenge by facilitating the collection and organization of critical information provided by business units, enabling companies to identify and quantify risks more accurately as a precursor to mitigating them.

In many organizations, managing regulatory compliance is still highly manual, relying on numerous forms with free text fields that multiple people must read, review, and amend. This approach often results in inconsistent and variable information, as the content depends entirely on the individual contributors, making standardization a rarity. AI can play a transformative role in improving this process, starting with refining the tools used to collect information. By helping to design better questions, surveys, and forms, AI can reduce the reliance on free text fields and increase the standardization of responses. This alone is a significant advantage, as standardized data is much easier for AI systems to organize, cross-reference, and analyze. By processing structured information, AI can identify patterns and insights that would be difficult, if not impossible, for human teams to discern from lengthy, unstructured text. Over time, AI could even cross-reference risks across different areas, providing a more comprehensive view of organizational vulnerabilities. Additionally, this approach reduces the human effort required for information gathering, shifting the focus from repetitive tasks to applying human judgment, which adds more excellent value to the process.

Implementing AI in compliance processes will vary by subject area but could include improvements like the categorization of risks. For instance, rather than asking someone to describe a health and safety risk in free text, the system might prompt them to identify a broad category first and then guide them through increasingly specific subcategories until a precise, standardized term is selected to describe the risk. The AI would recognize This standardized term as being connected to numerous related risks, enabling it to direct further information gathering more effectively. For example, instead of requesting a detailed description of a situation where someone is working at height on a construction site, the AI might guide the user toward selecting a specific term related to "falls from height." This term could then link to additional queries about insurance coverage, liability distribution through subcontractors, weather conditions like high winds, or training databases to verify staff skills and certifications. In this capacity, AI functions almost like an embedded compliance team member, collaborating with colleagues to process data, financial information, or IT systems to ensure risks are accurately identified, recorded, understood, and mitigated. Beyond this role, the standardization of collected data is equally critical, as it allows AI to serve as a comprehensive, overseeing intelligence that monitors and connects an organization’s entire spectrum of risks, fostering a more integrated and proactive compliance strategy.

A codified and standardized record of all compliance activities enables AI to retrospectively analyze instances where risks materialized into liabilities, comparing them to cases where risks were successfully mitigated. This analysis allows organizations to continuously refine and enhance AI-enabled compliance processes, improving the overall quality of their programs. Over time, AI will be capable of cross-referencing different types of risks, uncovering connections that might elude human observers, such as links between data protection and cybersecurity risks or systemic ties between bribery and corporate fraud. However, while AI systems can automate many lower-level tasks and identify areas requiring expert attention, they cannot operate entirely without human intervention. The value of AI lies in its ability to streamline routine processes, freeing compliance professionals to focus on more complex, high-value tasks.

The best approach to implementing such systems is to start small, selecting a discrete operational area with reliable underlying data. By concentrating resources on making the compliance system in that area robust, standardizing its inputs and outputs, and leveraging AI to analyze outcomes, organizations can build a solid foundation for broader implementation. Data protection, for example, is an excellent starting point because it operates relatively independently from other functions and typically relies on accurate information already available within the organization.

Nevertheless, data quality is a critical factor in the success of AI-driven compliance systems. Training AI on poor-quality data—such as inconsistent, inaccurate, or incomplete information—will only compound errors at a pace beyond human correction. Therefore, investing resources now to establish a high-quality system for gathering and processing compliance data is a forward-looking strategy. Such an investment will yield significant long-term benefits in greater efficiency and effectiveness in managing compliance activities.