In the rush to capture the perceived benefits of AI, we are continuing to see corporate leaders who want to dive straight into management or compliance simply focused on “this” product or “that” service, asking for a quick review, and wanting to avoid anything that looks like bureaucratic friction. We get it! But with every passing day, AI is incorporated into more functions, the technology becomes more capable (and risky) – and like it or not, it is just untenable to continue trying to apply AI governance as a series of isolated, one-off events that address only the system directly in the pre-launch seat at that moment. It is not practical, and definitely not sufficient, to spin up a new risk assessment approach for every system about to be procured, developed, or deployed.
First – to restate the obvious – AI is now in pretty much every service we use, in some form or fashion. So independent, uncoordinated processes simply will not scale. But also, AI is not static software; it is a dynamic, evolving infrastructure. Attempting to manage it for your business through piecemeal review and oversight is a recipe for operational disaster. And that’s before we even talk about emerging laws and compliance obligations.
The World Economic Forum reports that fewer than 1% of organizations have fully operationalized responsible AI practices, and that it is holding back those who don’t prioritize it. They also point out that “responsible, ethical and trustworthy AI strengthens customer confidence, regulatory readiness and long-term competitiveness.” AI governance is a “growth strategy, not a restraint,” if that governance is built into the corporate culture. Yes, it takes people, time, and money, both up front and into the future. But it is the literal minimum necessary to control the risks, to scale, and to ensure safe operations for the future.
Effective AI governance requires an intentional, comprehensive framework that connects strategy, policy, and process. To understand why such a holistic infrastructure is not optional, we should look at the operational risks of ad-hoc approaches, the rapidly tightening regulatory regimes, and the emerging commercial demands of the marketplace.
The “One-Off” Risk Assessment
Until now, many companies have assumed they can evaluate AI risks for systems “as they come,” without needing a centralized corporate approach. Unfortunately, this view completely ignores longstanding best practices and regulatory guidance (NIST), practical outcomes (consistency and record-keeping), and how modern AI models operate (evolving capabilities), particularly as we enter the era of agentic and increasingly autonomous systems.
As of today, the reality is that organizations are deploying AI, whether “bought or built,” across high sensitivity functions that bring along significant business risks and potential legal liability. When these systems are put into place without controlling institutional governance, risk identification is spotty, documentation is inconsistent, and monitoring is ad hoc with insufficient accountability when errors or failures occur. With AI, GenAI, or agentic functions, every part of the AI lifecycle requires intentional consideration and analysis:
- Risk-based evaluations and approval should be made on clear and consistent bases
- Documentation should reflect a prescribed set of reviews and assessments reflecting internal priorities, use case specifics, and jurisdictional requirements
- On-going monitoring and controls must be put in place relative to system risk and applicable regulatory context, with clear accountability
Existing and traditional lines of institutional responsibility can provide the backbone – most companies already have security reviews, data governance and privacy assessments, vendor screening, and program management milestones, but every kind of AI creates new risks and wrinkles for each of these. Taking the time to think through the implications at an objective level tailored to your business and industry particulars will make each individual product evaluation more efficient, complete, consistent, and defensible.
If you need motivational ammunition, see how often (and how wrong) things can go by checking the AI Incident Database. Or just read the headlines! But if the operational risks of ad-hoc compliance are not enough to convince corporate boards, the evolving industry and legal landscapes should be.
The Baseline: The EU AI Act
The European Union’s AI Act has been steadily impacting corporate behavior since its passage, even as full implementation has been delayed. In fact, that delay proves the point of the need for common standards. Because the EU Commission had not yet issued sufficiently comprehensive or detailed guidance that can be applied across high risk systems, it was not practical to hold companies accountable yet for their individual applications. Large scale regulatory alignment requires the corollary, underlying infrastructure that each organization will need to demonstrate. The threshold requirement of the EU AI Act for companies providing or deploying high risk systems is to have (and be able to demonstrate) a comprehensive AI governance process.
Because the EU AI Act has extensive extraterritorial reach, it effectively acts as a baseline for many companies operating internationally. While some of the specific requirements, such as centralized registration or particular templates, may be limited to the EU, the overall approach of risk assessment followed by testing, documentation, and on-going oversight for high risk systems will effectively apply almost anywhere. Comprehensive AI laws in South Korea and Vietnam, as well as other global approaches, mirror this general structure. Having a foundational governance system in place allows for adaptable variations across jurisdictions to ensure the details of each are not overlooked.
The Patchwork of US State Legislation
In the United States, federal legislative ambiguity has left room for an explosion of state-level regulations. Over 1,100 AI-related bills were introduced across state legislatures in 2025/2026 alone. Even counting only those that passed, it creates a fragmented compliance challenge. Some of the key categories of AI commonly addressed include:
- AI Safety Laws – Foundation models, or other AI systems with large operating-power capabilities must have additional testing, controls, transparency and reporting.
- AI Chatbot/Companion App laws – Eleven new chatbot-related laws passed this year that create disclosure and transparency requirements, as well as protections around user self-harm, restrictions for use by minors, and other testing and reporting controls.
- Use Case Specific AI laws – AI laws or regulations targeted at employment, biometrics, deep fakes/social media, age-based protections, and other contexts abound.
- Automated Decision-making/Privacy Laws – New privacy laws as well as new amendments or guidance for existing laws around ADMT use cases continue to appear (many complementing the use case laws noted above).
In our experience, employment applications represent the single highest-scrutiny AI use case. Not only because this is an area fraught with historical bias and current risk and sensitivity, but because all companies – no matter what their primary business model is – are employers. AI tools used in hiring, promotion, performance evaluation, and workforce management are subject to strict legal obligations in many jurisdictions. If your business uses third-party AI vendors to screen resumes, analyze video interviews, score candidates, or optimize assignments and shift scheduling, you are likely operating high-risk systems. Operationalizing these systems without a foundational, risk-based approach will be insufficient for both legal compliance and business effectiveness.
Beyond regulatory compliance, comprehensive AI governance is also becoming a core commercial requirement for doing business. Organizations that fail to build proper infrastructure are discovering they struggle to get insured, may be unable close enterprise deals, and face intensifying shareholder scrutiny.
AI Insurance Impacts
In 2026, the insurance market continues to respond to AI liabilities. Insurance carriers have introduced specialized “AI Security Riders,” and underwriting these riders requires documented evidence of adversarial red-teaming, model-level risk assessments, and AI-specific security controls. Documented alignment with recognized risk management frameworks is the baseline requirement for “reasonable security” coverage.
Shareholder Activism and Corporate Liability
We’ve also seen that for small and mid-sized businesses to close enterprise deals, demonstrating a mature, formalized AI governance posture is increasingly a contractual requirement.
Shareholders are becoming alarmed by the growing liability risks present with the unstructured development and adoption of AI. Some investors have filed formal shareholder proposals demanding stringent, transparent oversight policies for responsible AI to assure the market that risks, ranging from biased outputs and false information to autonomous agents deleting code, are being actively managed. The SEC’s Investor Advisory Committee has formally recommended enhanced corporate disclosures regarding board-level AI governance.
Operationalizing the NIST AI RMF
So, what does real, systematic AI governance look like in practice? It cannot be a top-down mandate layered over organization policies as cover or a token. It must be a usable operating system embedded directly into how daily business decisions are made. A complete, defensible architecture comprises some variation or combination of at least these components:
Pragmatism: “Start Where You Are”
We recognize that real-world time and financial resources are finite. The goal of a mature governance posture is to establish a clear guide for repeated, safe innovation, enabling organizations to access the benefits while avoiding or mitigating the risks. We routinely work with clients to customize this process, encouraging them to “start where they are” and build out infrastructure incrementally. Smaller or slower steps are better than no steps.
As a place to begin, you cannot govern what you have not classified, and you cannot classify what you have not inventoried. Remedying this is the single, most important, low-cost first step for any business. Following that:
- If your company primarily purchases software rather than building proprietary models, your initial processes should focus heavily on a Vendor Screening Tool with associated contractual and internal use controls.
- If you lack the depth of engineering resources to run complex algorithmic bias testing yourself, you can engage external support combined with leaning on robust contract language to shift key performance assessment and validation burdens to the developer.
- If your immediate focus is managing internal staff use, start by deploying a straightforward Acceptable Use Policy to stop unmonitored “Shadow AI” from exposing corporate data or otherwise increasing business risk.
Conclusion: Governance as a Growth Strategy
The current legal complexity is the new reality of the modern business environment. Putting off full-scale AI compliance by trying to manage everything “as you go” is certain to fail. It leaves your organization exposed to loss of customer confidence, functional compromise, regulatory fines, corporate liability, uninsurable operational risks, and the loss of a competitive advantage.
True AI governance should not be a bureaucratic burden designed to slow you down, even though it will admittedly require some commitment of time and effort to do well. But it is a critical growth strategy. By building a reliable foundation, you create the institutional confidence to address AI initiatives faster, safer, and more profitably than your competitors, and you proactively prepare yourself for regulatory enforcement.