Frequently Asked Questions
Preparing your business to use AI responsibly, efficiently, and defensibly depends upon two components: governance, which sets authority, risk boundaries, and accountability; and policy, which translates those decisions into practice. Governance is the structure. Policy makes it operational.
Because AI-assisted output is already entering your workflows whether oversight is in place or not. Governance ensures that use is authorized, verified, and accountable. Without it, your exposure is real and your ability to defend it is not.
Not uniformly, but the direction is clear. Every regulated industry carries its own ethical, professional, and compliance obligations that governance directly addresses. In the US, over 1,100 AI-related bills were introduced at the state level in 2025 alone. Organizations treating governance as optional are taking a position they may not be able to defend.
Governance is the structure. Policy is the document. One sets the decisions. The other records them. A policy written without governance already in place is a document without a foundation. See the AI Policy FAQ for how the two work together.
Governance is not a checklist. It is a set of leadership decisions that determine how AI use runs and how errors get owned. At its core: defined authority over AI use, clear risk boundaries, output verification standards, and accountability at each decision point.
Leadership. Not IT. Not legal. Not the employee who first raised the issue. IT manages tool access. Legal validates policy language. Neither makes the governance decisions that give your policy its authority. That belongs to the people who own the work.
Employees make their own decisions about which tools to use and what data to put into them. Output enters client deliverables without consistent verification. When errors surface, no one can document who authorized the use or how the output was checked. In law firms, that is an ethics exposure. In construction, it is a liability gap that standard professional indemnity may not cover. The question is not whether something will go wrong. It is whether your organization can defend what happened when it does.
An honest audit of where AI is already operating in your organization. Not where you think it is. Where it actually is. Shadow AI is the most common starting point. You cannot govern what you have not mapped.
Yes. A 12-person law firm using AI to draft correspondence carries the same ethical obligations as a large firm. A mid-sized construction company using AI for estimates carries the same liability exposure as a larger competitor. The consequences of ungoverned AI use do not scale down with headcount.
In law firms, governance maps to competence, confidentiality, and supervisory responsibility over every AI-assisted work product. In construction and AEC, it addresses the outputs that carry the most risk: estimates, safety documentation, contract language, and project deliverables. Both industries share the same core requirement: AI-assisted output must be authorized, verified, and traceable to a human decision-maker. The industry determines where the risk lives. Governance determines who owns it.
Organizational Readiness establishes the governance framework and policy that guide how AI is used across the company. Workforce Readiness equips employees to work effectively within that structure. Together, they create an environment of AI use that is responsible, efficient, and defensible.
Learn more about about AI Workforce Readiness.
Governance creates the foundation. Policy makes it operational. If you're ready to build both, start with a Discovery Session.