The rapid expansion of Artificial Intelligence (AI) capabilities and products has prompted individuals and organisations to consider how these technologies can be harnessed within their professional spheres.
Professional regulatory bodies are increasingly engaging with the use of AI by their registrants and publishing guidance to support its responsible adoption. Recent examples include guidance from the Financial Reporting Council, the General Medical Council and the General Osteopathic Council.
However, what role could AI play in the day-to-day operational activities of professional regulators?
First, an important caveat. AI is not a single tool but a broad range of technologies that can be deployed with varying degrees of effectiveness. If used without appropriate training or oversight, its outputs may be of limited value or even actively harmful. Different AI systems—from public generative AI tools to bespoke diagnostic or decision-support models used in healthcare—offer different benefits and present different risks. A separate blog in this series considers the risks AI presents to professional regulators. This article instead assumes: (a) AI is used competently and appropriately; (b) outputs are subject to robust human scrutiny; and (c) any AI system used is secure, confidential, ethical and properly designed for the tasks it performs.
Against that backdrop, there are a number of potential use cases for AI within the operational activities of professional regulators.
1. Education and resources
Professional regulators typically have two core functions: overseeing fitness to practise or professional discipline, and providing registrants with education, training, guidance and other resources.
AI could assist in drafting guidance documents, refreshing and improving training materials, and summarising research papers. Chatbots could provide interactive learning, gather feedback from registrants about their training needs, or help users locate relevant guidance or sections of regulatory codes. AI can also act as a constructive reviewer, identifying missing information or suggesting improvements to existing policies and guidance.
2. Research and data management
Professional regulators hold significant amounts of data relating to registrants through registration processes and years of fitness to practise investigations.
At the same time, regulators often operate under considerable financial and staffing constraints while remaining accountable to registrants for how resources are used.
AI could analyse large datasets far more quickly than would be possible manually. This could support analysis of registrant demographics, trends in applications or deregistration, themes emerging from complaints, financial performance and budgets, or outcomes of fitness to practise decisions for quality assurance purposes. AI could also be used to assess the effectiveness of sanctions, such as conditions of practice orders, by analysing review outcomes.
There is also potential for AI to assist with the early assessment of risk, whether by triaging complaints or identifying cases where interim restrictions may need to be considered while investigations are ongoing. However, such applications are more controversial and would require transparent processes and appropriate safeguards to address concerns about “black box” decision-making.
3. Administrative support
Regulators face substantial administrative workloads in managing registration and renewal processes, stakeholder engagement and, in many cases, hundreds of active fitness to practise investigations simultaneously (see, for example, data published by the General Medical Council, the Health and Care Professions Council and the Teaching Regulation Agency).
Scheduling hearings requires coordinating the availability of panel members, legal advisers, registrants, legal representatives and multiple witnesses. This is often managed manually through extensive correspondence and diary management. AI could assist by gathering availability through chatbots or integrated calendar systems and identifying suitable hearing dates, significantly reducing administrative burdens.
4. Interacting with the public
The primary purpose of professional regulation is to protect the public by investigating concerns about registrants and taking appropriate action where necessary. Regulators therefore interact extensively with members of the public, both through complaints and witness engagement, and indirectly through published decisions, consultation documents and public guidance.
AI can already serve as an effective editor or reviewer of draft communications. Regulators could use AI to make correspondence clearer and more accessible for members of the public, particularly those who may be vulnerable. AI could also suggest wording for sensitive correspondence, provide translation services, or act as an initial point of contact for prospective complainants through chatbots that explain whether a concern falls within the regulator’s remit.
5. Fitness to practise investigations
As noted above, many regulators face increasing caseloads while operating with limited resources. They are also likely to receive increasing volumes of AI-generated material, which, as my colleague Sarah Atkinson explains in her related article, may place further demands on their time.
Used appropriately, AI offers numerous opportunities to improve efficiency throughout fitness to practise investigations. It could assist with triaging and summarising complaints, preparing hearing transcripts and chronologies, identifying missing information, organising disclosure material, suggesting potentially relevant regulatory provisions, and analysing witness evidence for inconsistencies.
There may come a time when AI plays a more direct role in regulatory decision-making, for example in assessing levels of risk posed by individuals. However, the prospect of AI making final decisions in fitness to practise proceedings remains some way off.
Conclusion
Given the breadth and pace of AI development, there are likely to be many further opportunities for professional regulators to improve efficiency through its adoption. This is particularly important given the resource pressures many regulators face alongside growing complaint volumes.
However, successful adoption depends upon careful management of the associated risks. AI should enhance, rather than replace, sound regulatory judgement, and appropriate governance, oversight and safeguards will remain essential. This is an area in which Kingsley Napley is well placed to advise and support regulators.
About the author
Laura was called to the Bar in 2012 and joined the Regulatory Department in May 2017. She leads investigations across the full range of fitness to practise cases brought by the Teaching Regulation Agency and the Education Workforce Council of Wales, covering matters including fraud and examination malpractice, safeguarding concerns, sexual misconduct and inappropriate behaviour, bullying and intimidation, and criminal convictions.
