Why AI adoption in financial regulators depends on governance, not technology

The study shows that failures in public-sector AI adoption are often rooted in organizational design rather than technological limitations. Fragmented pilots, delayed oversight, and unclear ownership can erode trust even when AI systems perform well technically.

Government financial regulators are under pressure to use artificial intelligence (AI) to keep pace with increasingly complex and fast-moving markets. Yet most AI initiatives stall at the pilot stage, blocked by governance gaps, legal risk, and institutional inertia rather than technological limits. A new academic case study published in Platforms shows that successful AI adoption in this environment depends less on algorithms themselves and more on how institutions organize, govern, and scale innovation.

The study, titled Driving Strategic Innovation Through AI Adoption in Government Financial Regulators: A Case Study, examines how a national financial regulator transformed fragmented AI pilots into a coherent governance model capable of scaling innovation without undermining legitimacy or oversight.

Sensing AI opportunities under legal and public trust constraints

The research shows that AI adoption in government begins with the ability to sense opportunities and risks in a way that aligns with public mandates rather than market incentives. For the regulator studied, sensing extended far beyond scanning technological trends. It required systematically evaluating whether the institution itself was ready to adopt AI at all.

The regulator implemented internal readiness auditing to assess legacy systems, workforce skills, and organizational culture. While staff surveys revealed strong confidence in adopting AI with appropriate training, technical assessments uncovered major infrastructure gaps, including limited real-time data integration and uneven AI proficiency across teams. These findings shaped early decisions, preventing premature deployment of systems the organization was not equipped to govern.

In parallel, the regulator formalized a use-case intake and triage process. Instead of allowing ad hoc AI experimentation, proposed applications were evaluated against strategic priorities, legal mandates, and ethical considerations. This approach filtered out initiatives that posed disproportionate risk or lacked clear public value, ensuring that AI adoption remained mission-aligned.

Crucially, sensing also operated at the ecosystem level. The regulator convened cross-agency and industry meetings to identify shared challenges and emerging risks. Public problem signaling mechanisms, including requests for information and innovation calls, were used to attract solutions aligned with supervisory needs rather than vendor-driven agendas. Civil society input was incorporated to surface equity and trust concerns early, reinforcing the legitimacy of AI initiatives.

The study notes that in high-accountability settings, sensing is inseparable from legitimacy management. Identifying opportunities without understanding institutional readiness or societal risk can accelerate innovation failure rather than success.

Seizing AI value through controlled experimentation, not speed

Once opportunities were identified, the regulator faced the challenge of mobilizing resources without exposing markets or citizens to unacceptable risk. The study finds that traditional private-sector approaches based on rapid iteration and deployment are poorly suited to this context. Instead, the regulator adopted a model of controlled experimentation centered on governed sandboxes.

These sandbox environments allowed AI systems to be tested using anonymized or synthetic data under strict oversight conditions. Rather than functioning as isolated technical trials, the sandboxes were embedded in formal governance processes with defined entry and exit criteria. This structure enabled learning while preserving accountability and data protection.

Iterative feedback loops were institutionalized as part of the seizing process. User evaluations, supervisory input, and performance monitoring informed continuous refinement of AI tools before any move toward production use. Improvements in usability, response time, and reliability were documented, demonstrating how learning could occur without compromising public trust.

At the ecosystem level, seizing required reducing friction in public-private collaboration. The regulator established standard application interfaces, shared data schemas, and reference datasets to enable partners to develop and test solutions against common benchmarks. Collaboration contracts were standardized to specify audit rights, transparency obligations, and data governance rules, preventing ambiguity over accountability once systems were operational.

Seizing AI opportunities in government is less about rapid scaling and more about disciplined coordination. By embedding governance into experimentation, the regulator was able to move beyond stalled pilots while avoiding the reputational and legal risks that often derail public AI initiatives.

Reconfiguring institutions to make AI governance durable

The final and most consequential phase identified in the study is reconfiguring, the process by which successful AI initiatives are embedded into organizational structures and the broader regulatory ecosystem. Without this step, AI adoption remains temporary and fragile.

Internally, the regulator created new governance roles, including AI product ownership and model risk oversight functions. These roles clarified responsibility for AI performance, compliance, and lifecycle management, reducing reliance on informal decision-making. Model risk committees were established to oversee deployment decisions using criteria that balanced accuracy, explainability, efficiency, and trust.

Policies and metrics were also updated. Performance evaluation expanded beyond technical accuracy to include decision cycle time, user acceptance, explainability, and complaint rates. This shift reflected the regulator’s mandate to ensure not only effective supervision but also fairness and transparency in automated systems.

Reconfiguring extended outward to the ecosystem the regulator oversees. Multi-party governance structures were established, bringing together peer agencies, industry representatives, and public stakeholders to guide AI use over time. Open artifacts, including templates, checklists, and guidance materials, were published to codify lessons learned and stabilize expectations across the financial sector.

By formalizing these practices, the regulator moved from isolated innovation to platform-based governance. AI adoption became repeatable rather than exceptional, reducing duplication, lowering costs, and shortening the path from pilot to scale.

Implications for AI governance in the public sector

The study shows that failures in public-sector AI adoption are often rooted in organizational design rather than technological limitations. Fragmented pilots, delayed oversight, and unclear ownership can erode trust even when AI systems perform well technically.

The capability-based framework developed in the study offers a practical roadmap for public institutions. By aligning sensing, seizing, and reconfiguring routines at both internal and ecosystem levels, regulators can govern AI proactively rather than reactively. This approach transforms governance from a bottleneck into an enabling architecture.

For policymakers, the findings underscore the need to invest in organizational capabilities alongside digital infrastructure. Training, shared platforms, and clear governance roles are as critical as data and algorithms. Without them, AI initiatives risk reinforcing existing inefficiencies or introducing new forms of systemic risk.