5. Compliance and Reporting
Continuous compliance that preserves confidentiality and
supports lighter regulation.
How It (Doesn’t) Work Today
Across most countries, the number one demand from businesses is clear: reduce bureaucracy and administrative burden. The cost of compliance and reporting is enormous and, for example in the European Union, amounts to as much as 3.5 percent of GDP. It weighs heavily on small businesses and large corporations alike.
Furthermore, the entire compliance, certification, and reporting apparatus is outdated and poorly suited to its intended purpose. Periodic inspections and static documentation offer, at best, a limited snapshot of compliance at a single point in time, often incentivising box-ticking rather than meaningful adherence. A certification may only tell us a company was compliant on one day, two years ago. Even well intended ‘risk-based approaches’ are only partially successful, introducing complexity and legal uncertainty as to whether a given company's measures meet requirements.
The system is not working for regulators either: They face information overload from reporting requirements. Financial supervisors, for example, must sift through thousands of transactions or reports hoping to spot anomalies, which is a needle-in-haystack exercise. In many cases, violations are uncovered through whistleblowers or random audits, not through a systematic screening.
Let’s be clear: the purpose of these rules and regulations are not in question. From clean air and water to safe workplaces and stable markets, the public interest rationale is strong. These rules correct for market failures and protect people. The real problem lies not in the regulatory intent, but in its execution.
A Vision for Agentic Compliance and Reporting
Compliance monitoring and reporting is one of the most obvious cases for agentic AI. When let loose on the real-time internal data of any enterprise, an AI agent can outperform human compliance officers on every of the following dimension:
Thoroughness: Agents can examine the full picture of enterprise data, gigabits per second streams, to discover risks. Unlike existing solutions for algorithmic monitoring and fraud and risk detection solutions, it can also formulate novel hypotheses and expand its input data as needed.
Managing complexity: AI can act as compliance copilots. They can ingest new rules and regulations, map them to enterprise systems, and highlight where necessary changes, exceptions or trade-offs are needed.
Real-time validation: Instead of depending on quarterly reports or scheduled inspections, a statement of conformity becomes a live reflection of the present. Compliance is no longer a retrospective snapshot; it is an active, up-to-date status.
Minimal disclosure, maximal assurance: Most government regulators do not need full internal datasets — they just need to know whether a company is compliant. A well-governed AI compliance agent, running on a verifiable, tamper-resistant algorithm, could issue YES/NO compliance attestations without transmitting sensitive internal data. In this model, reporting becomes proof, not just paperwork.
Cost-reduction: All the above measures save time and will be increasingly automatable, feeding into cost-reductions for both companies and regulators alike.
On a broader level, agentic compliance will enable emergent ecosystem benefits. Many regulations are built with significant safety margins to compensate for imperfect compliance. With more precise, real-time monitoring and verifiable reporting, regulators could cautiously recalibrate requirements toward socially optimal levels. In the long run, we can envision a situation where compliance in domains like health, safety, financial, environmental, cybersecurity, and ethics become a component of overall quality management, with less, not more, internal information crossing organisational boundaries.
Key Questions
What lessons can we take from early AI use in fraud detection? Machine learning has been used in financial supervision and anomaly detection for years. What worked, and what failed, in those efforts that can inform the next generation of agentic compliance systems?
How do we design compliance agents that are both universal and contestable? What standards for audit trails, appeals, and oversight are needed to ensure that small businesses and multinationals alike trust the system? And that regulators can enforce decisions with confidence and due process?
Where should we draw the line on automated enforcement? If AI agents can issue fines or trigger legal action in real time, what safeguards are needed to prevent runaway enforcement or unjust penalties? What role should human oversight play in preserving legitimacy?
How can governments lead by example? How might the public sector apply agentic compliance tools internally, for monitoring procurement, ethics, or anti-corruption rules, before asking others to follow suit?