The Transformation of Legal Counsel: From the Trusted Advisor to the Strategic AI Adopter
As organizations accelerate the adoption of artificial intelligence (AI) to improve efficiency and reduce costs, in-house legal and compliance teams are encountering a growing structural challenge. While AI tools have become increasingly easy to deploy, governance mechanisms capable of managing accountability, fiduciary duties, and regulatory exposure have not evolved at the same pace.
For legal leaders advising organizations that operate globally or in highly regulated environments, the central question is no longer whether AI will be used, but how it can be implemented in a manner that remains defensible, measurable, and sustainable at scale.
A Shift Toward Execution-Focused AI Governance
In response to these pressures, many organizations are moving beyond policy-based AI oversight toward operational governance models embedded directly and holistically into workflows. Rather than treating AI governance as a one-time compliance exercise, organizations are asked to design scalable mechanisms that function throughout the AI lifecycle: from use-case selection and vendor diligence to deployment, monitoring, and escalation.
This shift toward operational AI governance is increasingly visible in the work of in-house legal teams tasked with translating legal principles, including the EU AI Act, into operational practice. Industry observers have pointed to the work of in-house legal leaders operating at the intersection of corporate governance and privacy as illustrative of this broader transition. An example is Chiara Imelda Wirz, Corporate Counsel and AI Ambassador at eBay, whose work has focused on translating AI governance principles into operational frameworks.
Operational AI Governance and the Role of In-House Legal and Compliance Teams
Operational governance approaches have been discussed across professional forums over the past year, reflecting a broader shift away from policy-only AI oversight toward measurable outcomes. Legal leaders are increasingly emphasizing compliance controls that translate AI governance expectations into day-to-day operations. These controls often include risk assessments and mitigation plans, clearly defined approval and escalation paths, and human-in-the-loop safeguards designed to preserve human judgment and oversight in consequential decisions.
However, across large enterprises, in-house legal and compliance departments are increasingly expected to move beyond issuing AI policies and conducting risk assessments. They are becoming business enablers in designing governance mechanisms that function at scale. A common characteristic of operations-focused AI governance is the move away from “checkbox” compliance toward measurable outcomes. Instead of relying on one-time approvals or static risk assessments, these teams are beginning to define success metrics for AI use cases, such as accuracy thresholds, efficiency gains, and error rates.
This approach aligns AI governance with business objectives while preserving legal accountability. It also enables legal and compliance functions to engage more effectively with business stakeholders, who increasingly expect legal teams to facilitate responsible AI adoption rather than act solely as gatekeepers.
Being “Fluent” in Translating European and U.S. Regulatory Expectations Helps
The complexity of AI governance is amplified for multinational organizations operating across jurisdictions with divergent regulatory expectations. Legal teams must often reconcile globally emerging AI-specific rules with existing privacy, consumer protection, and corporate governance frameworks. Experience across multiple legal systems has become increasingly relevant in this context.
Practitioners with experience across multiple legal systems have been increasingly relied upon to translate European and U.S. regulatory expectations into governance procedures capable of operating consistently across global legal teams. Wirz’s background in both European and U.S. law reflects the type of cross-border legal experience increasingly required to translate European and American regulatory expectations into globally workable governance procedures. “Being ‘fluent’ in translating European and U.S. regulatory expectations helps guide through the challenges multinational enterprises are currently facing,” Wirz says.
Practical Frameworks
As operational AI governance has become a recurring focus within the legal and compliance community, professional forums have increasingly emphasized practical frameworks over abstract principles. In this environment, applied perspectives grounded in real-world legal operations have gained prominence, particularly those that translate governance principles into repeatable processes and decision frameworks that legal teams can apply consistently across AI use cases.
Such applied approaches are exemplified in practice by work focused on operationalizing AI governance frameworks within in-house legal and compliance functions, including the development of structured models for use-case evaluation, vendor diligence, and post-deployment oversight. Legal professionals working in this area have contributed to the development of operational models that help in-house teams assess AI tools, document risk decisions, and demonstrate accountability to regulators and boards.
