Ongoing AI counsel, without a full-time hire.

Why AI governance needs an ongoing relationship, not a one-time project

AI governance is uniquely unsuited to one-and-done legal work, for two reasons. First, the law changes faster here than in any other practice area — HB 3773 took effect in 2026, BIPA was amended in 2024, HB 4875 in 2025, new state and federal developments arrive constantly, and the EU AI Act phases in over time. A governance posture that was current last year may be out of date now. Second, the company's own AI use keeps expanding — new tools, new vendors, new use cases — each of which can change the risk picture. A static policy adopted once and filed away steadily drifts away from both the law and the company's reality. AI governance is a moving target, and hitting a moving target requires an ongoing relationship, not a single engagement.

What fractional AI counsel actually does

On a retainer, the role functions as the company's AI legal department: keeping the governance framework current as laws change, reviewing new AI tools and vendor contracts before they're adopted, advising on new use cases and product decisions as they arise, maintaining the human-in-the-loop and oversight design, handling employee-use policy, monitoring the regulatory landscape and flagging what matters to this specific company, and being on call for the AI questions that come up constantly when an organization is genuinely deploying AI. Having one advisor who already knows the company's systems is faster, cheaper, and more coherent than briefing a new lawyer from scratch each time an AI question arises — which is the same logic as outside general counsel, applied to the specialized and fast-moving domain of AI.

The advisor actually governs AI in production

What makes this credible rather than a label is the source. Adam is the architect of his own multi-agent AI operating system — he designed its governance, guardrails, and adversarial-review protocols and governs it daily — and holds the AIGP credential. A company on retainer is getting ongoing counsel from someone who lives the discipline he advises on, who recognizes a governance gap because he has had to close them in his own system, and who understands new AI tools because he deploys and governs them in a production system of his own. The recurring theme of this entire practice — the difference between advising from a textbook and advising from operational experience — is most valuable precisely in an ongoing relationship, where that operational instinct is applied to the company's real, evolving AI footprint over time.

Who this is for

Fractional AI counsel fits organizations past the experimental stage — companies genuinely deploying AI in their products, operations, or workforce, where AI questions arise often enough that ad hoc legal help is slow and expensive, but not at a volume that justifies a full-time AI-specialized lawyer (a costly and uncommon hire). That includes AI-forward startups scaling their product, established companies integrating AI across operations, and platforms whose core offering is AI-driven. The retainer follows the company's AI footprint wherever it operates: the firm provides governance counsel where it is authorized to practice and, where a matter requires advising under another jurisdiction's law or a dedicated specialty like IP, coordinates with local and specialist counsel — the way an outside general counsel brings in and quarterbacks specialists rather than claiming every lane. As a practical risk posture, a company deploying AI to the public is generally best served building to the high-water mark across its footprint rather than its home state's minimum.

How it works

Engagements are structured as a governance retainer scaled to the company's AI footprint and the volume of questions, with the scope agreed in advance so the cost is predictable. It can run alongside the firm's traditional outside-general-counsel work for companies that want both their AI and their general corporate legal needs handled coherently. The aim is the same as the best general-counsel relationship: an advisor who knows the business, prevents problems before they happen, and keeps the company's fast-moving AI governance continuously current rather than periodically rebuilt.

What usually goes wrong

The recurring failure is treating AI governance as a one-time project — a framework built once, then left untouched while the law shifts and the company's AI footprint expands, until the posture quietly stops matching either reality and a gap is discovered the hard way. A close second is the reactive scramble: calling a lawyer only when an AI problem has already surfaced, paying to brief someone from scratch each time, and getting advice that arrives after the decision instead of before it. The third is the company scaling AI rapidly with no consistent legal oversight at all, accumulating vendor contracts, use cases, and exposure faster than anyone is tracking them.

Related

• AI Governance Frameworks & Policy →
• AI Risk, Incident Response & Liability →
• AI Vendor & Procurement Contract Review →