AI Governance for Companies Building and Using AI
Accord & Shield Legal drafts the contracts and policies that govern how a company builds with AI and buys it — vendor and API agreements, rights in training data, customer-facing AI terms, internal use policies, and IP ownership — for companies in Arizona, California, and Texas.
Most companies did not decide to adopt AI. It arrived. An engineer wired an API into the product, a support lead started drafting replies with it, a vendor shipped a model into a tool the company already paid for. By the time anyone asks the legal questions, the practices are already in production.
The Decisions Your Contracts Have to Make
The questions are not abstract. Enterprise buyers now send AI riders alongside their security questionnaires. Investors ask what the product was trained on. Acquirers ask who owns code written with an assistant. Each is answerable — but only if someone decided in advance and wrote it down. The work is making those decisions explicit and putting them where they will actually be read. Not sure where you stand? Our AI Governance Readiness Check is a short self-assessment.
AI vendor and API agreements
Before a model is in your product, someone should be able to answer what the provider may do with what you send it: whether your inputs train their model, who owns the outputs, what happens to your data when you leave, how much notice you get before a model version is deprecated underneath you, and what you are owed when the service degrades. These terms vary widely between providers and change often — the version you agreed to eighteen months ago is frequently not the version you are operating under now. We have written about the questions worth asking before signing an AI vendor agreement.
Rights in your training and reference data
If your product learns from data — customer data, licensed data, your own historical records — the question is what you were permitted to do with it. That means the terms you accepted when you collected it, the promises in your own privacy policy, and the actual scope of any data licence you signed. Companies most often discover a gap here during diligence, which is the most expensive moment to discover it.
Customer-facing AI terms
What are you telling customers about the AI, and what have you committed to? Whether you disclose AI involvement and where, who owns the output, what happens when the output is wrong, what customers may not use it for, and how responsibility is allocated when something goes wrong downstream. Terms of use written before the AI feature existed usually do not answer any of these. See our note on AI terms in customer contracts.
Internal AI use policy
Your team is already using these tools. A workable policy answers which tools are approved, what categories of information never go into them — customer data, source code, unreleased plans, anything under NDA — who reviews AI-assisted work before it leaves the company, and what happens when the policy is not followed. Short and enforced beats comprehensive and ignored. Related: employee NDAs and AI tools.
AI provisions in enterprise contracts
Procurement teams have started asking. Expect questions about training-data use, human review, model changes, subprocessors, and audit rights. The work is deciding in advance what the company can actually commit to and holding that line consistently — rather than conceding a different position in each deal because nobody wrote down the standard one. This sits alongside the rest of your SaaS and software agreements.
IP ownership when the product is built with AI
If engineers, contractors or designers used AI assistance, your contributor agreements and assignment documents should still produce a clean answer about what the company owns. This is the question investors and acquirers ask, and the answer is far easier to establish before the work ships than after. See intellectual property and IP risks for founders building with AI.
EngagementsHow We Work
Most engagements start with a review of what is already in place: your vendor agreements, your customer-facing terms, whatever internal policy exists, and how the product actually uses AI in practice. That produces a short list of decisions the company needs to make, ranked by which ones a customer or investor will ask about first.
From there the work is drafting — the terms, the policy, the contract language, and the assignment documents that make the answers hold up. For companies that want this handled continuously rather than project by project, see outside general counsel. If you are still forming, start with corporate formation.
Nadine Deeb is licensed in Arizona, California, and Texas, and works with technology companies across all three.
Frequently Asked Questions
We are a small team. Is this premature?
The cost of deciding early is a conversation and some drafting. The cost of deciding late is renegotiating with a customer who has already asked, or explaining a gap during diligence.
We only use AI internally — we do not sell an AI product.
Then the vendor terms and the internal policy matter, and the customer-facing questions mostly do not. The engagement is smaller.
Our vendor says its terms are non-negotiable. Can they be changed?
Sometimes, depending on the provider and your spend. Where they cannot, the decision becomes what you tell your own customers given what your vendor’s terms actually allow.
Do you handle AI-related disputes?
Our focus is transactional — contracts, policies, and documentation. Where a dispute does arise, we generally work toward resolution by demand letter or negotiation before litigation is considered. See business disputes.
Do you work with companies outside Arizona, California, and Texas?
Our attorneys are licensed in Arizona, California, and Texas, and we advise on matters governed by the law of those states. If your matter involves another state’s law, we can discuss whether we are the right fit or help you find counsel who is.
Decide It Now, Not During Diligence.
Whether you are building with AI or buying it, a short conversation now is cheaper than renegotiating later.