AI Governance and the new logic of commercial relationships in the legal market

You may be using artificial intelligence in a technically impeccable way and still lose a contract. Not because of price. Not because of quality. Not because of delivery capacity. But because of something harder to notice at first glance and, precisely for that reason, far more dangerous. The inability to objectively prove how your organization uses AI.

That is the point many people still have not understood. This year, AI governance stops being a backstage topic, restricted to legal, compliance, or information security teams. It starts occupying a much more sensitive space. It becomes diligence, a procurement criterion, a filter in vendor selection, a question in RFPs, and, increasingly, a condition for onboarding, contract renewal, and scope expansion.

The era of “trust us, we use AI responsibly” is coming to an end. The market is moving toward a much less tolerant and far more objective logic.

• Show me the controls.
• Show me the records.
• Show me who approves.
• Show me what happens when AI gets it wrong.

It may seem like a subtle shift, but it is not. The moment AI governance enters the language of risk, procurement, and contracting, it stops being an abstract value and becomes part of the company’s competitive capacity.

In Europe, the AI Act enters a more concrete enforcement phase on August 2, 2026. And the effects will not remain limited to the European market. They never do. That was the case with privacy. That was the case with information security. And it will be the case, perhaps even more intensely, with AI.

When a relevant regulation truly moves into enforcement, the impact does not stop at the regulator. It travels through the supply chain. Multinationals adjust internal standards. Risk teams update questionnaires. Security teams tighten criteria. Procurement turns best practices into minimum requirements. What was a differentiator yesterday becomes an obligation tomorrow. And what today still seems desirable may, with little warning, become an exclusion filter.

That is exactly where one of the most underestimated risks of this moment lies. Many organizations still believe they will only face pressure when there is explicit local regulation, direct supervision, or a concrete sanction. In practice, the pressure usually arrives earlier, through the client. And the client rarely sends an email saying you were rejected because your AI governance was weak. They simply move forward with another partner.

I have been following this topic in conversations outside Brazil, and one thing has caught my attention. In more mature markets, the AI discussion is no longer centered only on adoption, productivity, or efficiency. The conversation has moved forward. Today, the focus is much more on the ability to demonstrate control, consistency, and governance. The question is no longer whether the organization uses AI, but whether it can sustain that use when someone decides to take a closer look.

In Brazil, I still see part of the market somewhat disconnected from this shift, as if governance were a detail to be solved later. It is not. In many cases, “later” has already arrived.

The problem is that governance has not kept pace with adoption. Most organizations advanced in usage before maturing their management discipline. In other words, many people already use AI in their daily routines, but few can clearly, consistently, and with evidence explain which tools are being used, for what purposes, with which data, under which controls, with what supervision, and with what traceability.

This kind of gap goes unnoticed while the topic remains in the experimentation phase. But it becomes very visible when the conversation moves from technological enthusiasm to the contracting table.

And here there is an interesting irony. For a long time, part of the market sold the idea that AI would reduce friction, eliminate bureaucracy, and make everything more fluid. To some extent, that is true. But for AI to scale in corporate environments, it demands the exact opposite of the improvisation that marked its initial adoption. It demands method, criteria, and operational governance. Not the kind of governance that looks good on a PowerPoint slide, but the kind that survives real diligence.

In Brazil, this movement takes on its own characteristics. Here, the pressure will likely come from a combination of regulatory scrutiny, reputation, contractual requirements, and professional responsibility. The moment artificial intelligence enters the regulatory supervision agenda connected to personal data processing, the discussion stops being conceptual. It becomes evidential.

If AI is being used with data, decision-making contexts, automation, analytical support, or any interaction with sensitive information, the center of the discussion is no longer simply stating a commitment to responsibility. It is demonstrating that proportionate controls exist, that they work, and that someone is accountable for them.

In the legal market, this adds another layer of sensitivity. It is not enough to adopt AI. Firms must be able to sustain, before clients, partners, audits, and potential future scrutiny, that their use of AI respects parameters of transparency, responsibility, human supervision, and confidentiality preservation.

I have argued for some time that AI inside organizations cannot be treated as an isolated tool. It affects client service, legal services, risk analysis, and knowledge management at the same time. That is why governance cannot come at the end, as if it were a compliance adjustment. It needs to be born together with the architecture. When that does not happen, adoption scales before maturity. And that is exactly where noise, invisible risks, and, in many cases, losses that no one notices immediately begin to appear.

Many people still treat AI governance as a legal protection layer. I see it differently. It is already becoming a commercial asset. Because the client does not just want to hear that you take the topic seriously. The client wants to know whether you can evidence it quickly, coherently, and in an auditable way. The client is not buying only technical capability. The client is buying predictability, reliability, and reduced relationship risk.

So when I talk about governance evidence, I am not talking about institutional discourse or generic statements. I am talking about concrete, reusable, and verifiable artifacts.

Do you have an inventory of AI use cases already in operation or, at the very least, under controlled experimentation? Do you know which tools are being used, including those outside the officially approved environment? Do you classify the type of data entering these workflows? Do you know what is allowed, what is not allowed, and what requires additional approval? Do you maintain policies, training records, defined ownership, and review criteria? Do you test quality, accuracy, adherence, and usage limits? Do you record relevant decisions, configuration changes, versions, and approvals without compromising confidentiality? Do you assess AI vendors with the same seriousness you would apply to a strategic infrastructure provider in your operation?

Frameworks such as the NIST AI RMF help a lot because they take the conversation out of the “moral field” and place it in the “operational field.” The question stops being “are you responsible?” and becomes something far more serious. How do you govern, map, measure, and manage this risk? ISO/IEC 42001 pushes the discussion in the same direction by structuring AI governance as a management system.

And the market has already begun to signal this new standard. When global companies start highlighting auditability, compliance, transparency, and supervision in their AI offerings, they are not simply doing institutional branding. They are codifying procurement language. They are teaching the market how this topic will be evaluated. In other words, they are helping build the new eligibility checklist.

In practice, this changes the strategy for law firms and legal departments. The question is no longer “which tool are we going to buy?” That question remains important, of course, but it is no longer the first one. The question that comes before it is different. What is our minimum management system for AI?

Without that answer, the organization becomes exposed to three well-known weaknesses.

1. AI spreads across teams, routines, and deliverables, but no one has a consolidated view of actual use, the data involved, and the level of exposure.
2. The team wants to move forward, but risk, security, compliance, or leadership teams block progress. And they block it for good reason. No one should scale something they cannot explain.
3. The company only discovers that governance was a decision criterion after the opportunity has already gone. This may be the most damaging weakness of all.

This point deserves special attention because it is still rarely discussed. The market loves to talk about the legal risk of using AI without governance. But the commercial risk, in many cases, is greater and more immediate. You may not suffer a sanction tomorrow. But you may be rejected today by a more mature, more exposed, or simply more demanding client.

That is the reality many people still prefer to avoid. In several sectors, AI governance will not be demanded first by the regulator. It will be demanded by the client who does not want to take the risk of hiring a provider that turned technological improvisation into strategy.

And no, this does not mean bureaucratizing everything. Quite the opposite. The mistake is assuming that the only possible answer is to create a heavy, slow structure that is incompatible with innovation. That is not the point. The smarter path is to build minimum viable governance. Simple enough not to kill speed. Robust enough to sustain scale, diligence, and trust.

I would summarize this logic in three principles.

1. Before discussing brand, interface, or commercial promise, it is necessary to understand the use case, decision-making context, data type, and process criticality.
2. Only what the organization can record, test, supervise, and explain without embarrassment should scale.
3. The initial driver of this agenda is not only avoiding future litigation. It is strengthening competitive positioning now. Transparency, records, supervision, and control are not just protection mechanisms. They are sales assets.

There is also a detail that many people still have not realized. In many markets, a company will not need to have the most sophisticated governance in the industry to win. At first, it may be enough to be the one that can answer better. The one that has the material organized. The one that can send a coherent package within 48 hours, while competitors are still trying to figure out who approves what.

That is where governance starts generating real advantage. Not because it is glamorous. Let’s be honest, it is not. But because it reduces friction, accelerates trust, and professionalizes the narrative. And in the current environment, professionalizing the narrative matters a lot.

At the end of the day, this discussion is not just about technology. It is about organizational maturity. Mature organizations are not the ones that adopt the newest tool first. They are the ones capable of absorbing new technology without losing control, without increasing opacity, and without transferring risk to the client.

This integrated architecture view starts from a simple premise. AI only generates sustainable value when governance, data, security, human supervision, and decision-making move together. Outside of that, what looks like progress is often just disorganization with a nice interface.

So for those who truly want to be prepared, I see three necessary moves.

1. Create an AI inventory.
2. Structure an evidence package.
3. Turn governance into a commercial argument, not as empty marketing, but as a concrete demonstration of capability.

Because in the next cycle, many organizations will discover too late that AI governance was not a legal appendix. It was part of the value proposition.

And the market, as it almost always does, will not warn anyone when the bar changes. It will simply start valuing those who understood it first.