Organizations are embracing AI for its potential to reduce repetitive work, streamline workflows, and improve efficiency. But as adoption grows, organizations are faced with a new set of questions: What should AI be used for? How should tools be evaluated? Who is responsible for oversight? How can innovation move forward responsibly.
Those questions are the foundation for AI governance. At the American Arbitration Association, AI governance is a business, technology, and legal priority that the organization is building into the full lifecycle of AI use, from early evaluation and design through deployment, monitoring, and review.
To help lead that work, the AAA recently hired Jennifer Reeves as its first Vice President, AI Governance and Integration Lead. In this role, Reeves is focused on making AI governance practical, operational, and integrated across the organization. Below, she discusses what the new role involves, why cross-functional governance matters, and how the AAA is building a responsible foundation for AI adoption.
Jennifer, what does this new role involve at the AAA?
My role is focused on making AI governance practical and operational across the American Arbitration Association. My position sits within the legal team under the General Counsel, but I work closely with the technology team and stakeholders across the organization. That cross-collaboration is essential because the people developing, evaluating, approving, and using AI-enabled tools exist in all departments throughout the organization. Every person brings different perspectives and responsibilities to the process, and it’s incredibly important to have an open line of communication with all of them.
I also chair our AI governance committee, helping guide governance across internally developed systems, third-party tools, employee-facing tools, case management systems, and client-facing technologies.
One of my major priorities is advancing an AI risk registry. The registry will help us document AI systems and use cases, assess them against global governance and regulatory frameworks, such as the NIST framework and EU AI Act, and create a clear record of how decisions were made. It will also provide a consistent way to evaluate AI and manage risk as our use of the technology continues to grow.
I’m fortunate to be stepping into an organization that is already thinking deeply about AI governance and has a strong framework in place. My goal is to build on that foundation, strengthen our approach, and ensure the American Arbitration Association remains ahead of emerging regulations and governance standards as this space continues to evolve rapidly.
What experience are you bringing to this role?
Before joining the AAA, I worked on generative AI and innovation initiatives at a variety of AmLaw 100 law firms, and most recently at Quinn Emmanuel. There, I evaluated AI tools, supported implementation and adoption strategies, developed governance frameworks, and helped attorneys understand how to use new technologies responsibly.
That experience showed me that AI adoption is not just about technology. It requires clear processes, strong oversight, practical training, and governance that people can actually use.
When did you first realize AI governance needed to become a central conversation?
Early conversations about AI were focused on its potential: how it could reduce repetitive work, support research, assist with drafting, or help people work more efficiently. Those conversations were important – and they still are – but they are no longer enough.
There are so many tools available now, and organizations need to understand not only what a tool can do, but whether it is the right tool, what risks come with its use, what data it relies on, and what oversight is required.
The conversation has shifted, and asking the right questions about AI use and monitoring allows innovation to continue responsibly.
What does it mean to make AI governance operational?
Making governance operational means taking the policy as written and turning it into something people can actually use. It is not enough for only a few people to understand what the policy says. Employees need to understand how governance applies to their work, whether they are evaluating, building, buying, or using AI.
For us, that means giving teams a clear understanding of where to go for guidance, who needs to be involved, what questions to ask, what information to provide, and how decisions will be reviewed and documented.
At the American Arbitration Association, our goal is for governance to support responsible decision-making without slowing people down unnecessarily.
Why does AI governance need to be cross-functional?
AI governance works best when it is cross-functional, with input from the right teams and clear ownership for moving the work forward. While governance should not sit with one group or operate in a silo, it also needs a defined owner or decision-maker to keep the process consistent, accountable, and actionable. AI-related risks can touch legal, technology, security, operations, and business areas, often at the same time. Involving the right people early makes it easier to evaluate whether a use case is appropriate, understand the risks, put the right safeguards in place, and monitor the system over time.
The AAA’s recent survey, “From Principles to Practice: A Benchmark Study in AI Governance,” reinforces why that cross-functional approach matters. While 80% of respondents said IT or technology teams contribute to AI governance, only 35% reported involvement from legal and compliance teams. The report also found gaps in escalation pathways and audit readiness, including that only 33% of respondents said they have defined escalation pathways when AI systems misbehave, and just 22% said they are very confident they could produce evidence of governance decisions for regulators or auditors if required.
Those findings show why AI governance needs clear accountability and collaboration. It is not enough for an organization to have a policy. Teams need to know who is responsible, who needs to be involved, when issues should be escalated, and how decisions are documented.
What does responsible innovation mean to you in this role?
Responsible innovation means moving forward thoughtfully, understanding the risks, designing the right safeguards, and making sure innovation is aligned with the organization’s values, obligations, and the people it serves.
In AI, responsible innovation requires asking more than “Can we build this?” or “Can this make us more efficient?” It also means asking whether a tool is appropriate, whether it is explainable enough for its purpose, whether humans remain meaningfully involved, whether the data is protected, whether the system can be monitored, and whether the organization can explain and support the decisions it has made.
AI will continue to change, and the rules around it will continue to evolve. The most important thing organizations can do now is build governance programs that are clear, usable, and flexible enough to grow with the technology.