Artificial intelligence, generative AI, and AI agents are revolutionizing government regulatory processes and compliance activities, making them faster, smarter, and more effective. AI-driven analytics enable real-time monitoring and analysis of vast data sets, allowing regulatory bodies to detect anomalies, predict compliance risks, and identify potential violations with greater speed and accuracy. Gen AI takes it a step further by decoding complex regulations, automating time-consuming report writing, and translating jargon into clear, actionable steps. This technology not only speeds up processes but also helps regulators enforce rules consistently and fairly. The result? Less time, more transparency, and a government better equipped to tackle the challenges of tomorrow.
What does this mean for government workers in regulatory roles? This progression signifies a shift toward an “AI-augmented worker” and even an “AI super user” within regulatory bodies, requiring new skill sets like prompt engineering and AI supervision, alongside traditional skills like policy expertise and strategic thinking. From tax auditors and building inspectors to financial regulators and licensing and permitting professionals, a wide range of jobs are a part of the government’s regulatory and compliance activities. And while there are bound to be some unique job-specific benefits from using AI, there are several that are role-agnostic.
Let’s take a closer look at how AI technologies might change the government regulator role and what it means for the worker doing the job. Click through the slides below or download the complete PDF.
As AI continues to drive efficiency in regulatory functions, new roles are expected to emerge that focus on improving processes and reducing unnecessary complexity. One such future role is the organizational debt auditor, a professional who is dedicated to identifying process improvements and reducing organizational debt. What might this new public sector job look like?
Jennifer, an organizational debt auditor, has just completed an AI-powered audit of the new-hire onboarding process for a back-office government department. She discovers that the current onboarding process has 29 steps, many of which are confusing and duplicative and impose additional processes on both new hires and managers. Using a gen AI–powered “friction finder” tool, Jennifer is able to identify bottlenecks, duplication, points of friction, and delays, and quantify their impact on cost and efficiency. With insights from the tool and consultations with stakeholders, Jennifer creates a plan to cut the process in half and improve efficiency for all parties.