7 AI Governance Questions Every Growing Business Should Answer Before Scaling AI
Matthew Fornaro is a business law attorney, commercial litigator, mediator, arbitrator, and founder of Fornaro Legal. Practicing since 2003, he advises entrepreneurs and established businesses on contracts, transactions, disputes, governance, and growth.
AI adoption rarely begins with a policy decision. It usually begins with convenience: an employee drafts a customer response, summarizes a contract, or uses a tool to organize information. As those uses spread, a growing business needs clear answers about authority, confidential information, human review, accountability, and the records behind important decisions.
Why AI governance matters
AI can make a business faster without making it more disciplined. For a growing company, that distinction matters. A growing company is already delegating more decisions to employees, managers, vendors, and systems. AI adds another layer of speed. It can generate language, recommendations, summaries, and customer-facing work in seconds. If the company has not decided who may rely on AI output, what information may be used, and which decisions still require human approval, technology can scale ambiguity as efficiently as it scales productivity.
The goal is not to slow AI down. It is to give people enough structure to use it responsibly. In my work with entrepreneurs and businesses, I increasingly see AI governance as a business strategy issue, not merely a technology or legal issue. The central question is straightforward: where should technology accelerate the work, and where must human judgment remain in control?
Here are 7 questions to answer:
1. What can AI do?
Start by defining AI's role. Is the tool assisting an employee, or is it effectively making or communicating a decision?
There is a meaningful difference between using AI to prepare an internal outline and using it to recommend pricing, revise contract language, respond to a customer, evaluate an employee, or trigger an automated workflow. The more consequential the use, the clearer the company should be about whether the technology is advisory or operative.
A useful framework is to create categories of permission. Low-risk internal assistance may require limited oversight. Customer-facing communications, financial commitments, legal terms, employment decisions, and other material actions may justify a higher level of review.
Good governance does not prohibit experimentation. It tells employees where they are free to move quickly and where the business expects another level of judgment.
2. What data may be used?
Employees often use AI because it saves time. That convenience can obscure the value of the information being entered into the system.
Customer data, financial information, confidential communications, contract language, source code, internal strategy, and proprietary methods may all carry obligations that existed before anyone adopted an AI tool. A business should therefore decide what categories of information are permitted, restricted, or prohibited.
It should also understand the terms of the systems it uses. A company's agreement with an AI vendor may matter if the business has already promised customers, employees, or partners that certain information will be handled in a particular way.
A simple practical test is this: would the company be comfortable explaining that use to the person or business that supplied the information? If the answer is no, the use deserves closer review.
3. Who approves the output?
AI can generate polished work that looks ready to use. That appearance is precisely why approval rules matter.
A salesperson may be able to generate proposed contract language without having authority to change the company's legal obligations. A customer-service employee may be able to draft a response without having authority to waive a contractual right. A manager may use AI to compare options but still need approval before making a financial commitment. The ability to generate content and the authority to adopt it are different powers.
As a company scales, this distinction becomes more important. Founders often begin by personally reviewing major commitments. Eventually, that becomes impossible. The answer is not to keep every decision at the top. The answer is to define who can approve what, where that authority ends, and when AI-assisted work must be escalated.
4. Where is human review required?
Not every AI output deserves the same level of scrutiny. Governance works better when review is tied to consequence.
Material contracts, significant pricing exceptions, employment decisions, intellectual property ownership, customer promises that change standard terms, regulatory representations, and communications involving confidential information may all justify mandatory human review.
The National Institute of Standards and Technology's (NIST) AI Risk Management Framework offers a useful model for thinking about AI risk according to context, impact, and responsibility. A growing business does not need to reproduce an enterprise compliance program to learn from that principle.
Human review should sit where the downside justifies it. That keeps governance proportionate and prevents the policy from becoming so burdensome that employees simply work around it.
5. Can AI change the deal?
One of the quieter risks appears when AI is used inside existing customer and vendor relationships. A tool may revise a proposal, suggest a customer response, summarize an agreement, or draft language that sounds reasonable but changes the commercial bargain. The written contract may say one thing while later emails, purchase orders, exceptions, and AI-assisted communications begin to say something else.
Businesses already struggle with what I call the contract stack: several documents and communications that no longer tell one clean story. AI can make that stack grow faster.
The practical fix is not to ban AI from commercial work. It is to define when generated language must be compared against the governing agreement and when a proposed change requires formal approval. A company should know when it is communicating the deal and when it is changing the deal.
6. Can you reconstruct the decision?
Fast decisions are useful only if the business can later explain what happened. Important decisions are now distributed across email, text messages, collaboration platforms, cloud systems, and AI tools. The final document may survive while the reasoning and approval trail disappear.
A useful test is to ask what would happen if the person who made the decision left tomorrow. Could the company still identify what information was considered, whether AI was used, who reviewed the result, who approved the commitment, and which agreement controlled?
That record can matter in litigation, but its value is broader. It may become important during financing, due diligence, an insurance claim, a leadership transition, or a sale.
This does not mean preserving every prompt forever. It means identifying AI-assisted decisions whose consequences outlast the conversation in which they were made and preserving enough context to explain them.
7. When should legal review begin?
Not every AI use requires a lawyer. The better question is when a routine productivity tool begins to change legal or business risk.
Useful triggers may include adopting an AI vendor that will handle confidential or customer information, allowing AI to communicate externally without meaningful review, using AI in employment decisions, modifying standard contracts through AI-assisted drafting, or relying on automated recommendations for material financial decisions. AI use in regulated or contractually restricted processes may also justify closer review.
The trigger should be consequence, not novelty. A familiar technology can create substantial risk, while a new tool may be relatively harmless in a narrow internal use.
AI use also changes quickly. A policy written six months ago may describe a business that no longer exists. Review should therefore follow the business as it changes, not sit untouched on a shared drive.
Accountable speed wins
The businesses that use AI well will not necessarily be the ones with the most tools or the longest policies. They will be the ones that can move quickly while still knowing who has authority, what information is protected, where human judgment belongs, and how important decisions are documented.
That is the larger leadership point. Technology can increase capacity, but it does not automatically improve judgment. Leaders still have to decide what matters, which risks are acceptable, and who is accountable for the result.
AI governance should therefore be designed as part of operations, not bolted on after adoption has already occurred. Clear rules can make innovation easier because employees know where they have room to experiment and where they need another level of review.
For a growing business, the goal is not slower decision-making. It is better-controlled speed.
Start with consequence
If your company already uses AI, do not begin by trying to write a fifty-page policy. Start with the places where AI touches customers, contracts, confidential information, employees, money, or other decisions that can materially affect the business.
For each use, ask three questions: who may use it, who must review it, and who remains accountable for the result. That exercise will usually reveal where formal rules are needed and where ordinary business judgment is enough.
For more practical guidance on contracts, governance, AI use, and legal readiness as businesses grow, visit Fornaro Legal.
Read more from Matthew Fornaro
Matthew Fornaro, Founder of Matthew Fornaro, P.A.
Matthew Fornaro is a business law attorney, commercial litigator, mediator, arbitrator, and founder of Fornaro Legal in South Florida. Practicing since 2003, he advises entrepreneurs and established businesses on contracts, transactions, governance, disputes, and growth. He is admitted in Florida, New York, and the District of Columbia and has been recognized in The Best Lawyers in America® for Commercial Litigation since 2022. His work increasingly focuses on practical business law, AI governance, legal readiness, and the challenges companies face as they grow.
Disclaimer: This article is for general informational purposes and does not constitute legal advice.
