Board Oversight in AI-Driven Companies

Generative AI has moved from labs and pilot programs to core business strategy. Boards of directors—whether in tech, healthcare, finance, or industrials—are now expected to provide meaningful oversight on how artificial intelligence is developed, deployed, and used across the enterprise. That includes not only customer-facing applications, but also internal tools that support decision-making, operations, and analysis.

But AI isn’t just another disruptive technology. It challenges traditional assumptions around intellectual property, data usage, model transparency, and regulatory risk. As AI continues to evolve, boards must adapt their oversight practices to stay aligned with both legal obligations and stakeholder expectations.

Internal AI Use Deserves Equal Attention

While customer-facing AI applications often attract board scrutiny, internal uses can fly under the radar—despite carrying significant legal and reputational risk.

From HR screening tools and pricing algorithms to operations dashboards and internal analytics, many companies are rapidly embedding AI into their day-to-day workflows. These systems may rely on third-party models or custom tools developed by internal teams, sometimes without full legal or compliance review.

Boards should be asking:

• What internal functions are already being shaped by AI?

• Who oversees the selection, training, and auditing of these models?

• Are there safeguards in place to prevent bias, protect sensitive data, and ensure accountability?

Ignoring internal AI use can leave organizations exposed to legal claims, regulatory scrutiny, and operational failures. Good governance requires a clear view of not just what the company is building—but what it’s quietly relying on behind the scenes.

Legal risk starts at the foundation

Much of the legal complexity around AI begins before deployment—during data collection, model training, and IP structuring.

Does the company have the right to use the data it’s training on? Were licenses properly secured? Are there third-party models embedded into core systems? These aren’t hypothetical questions.

Recent lawsuits have spotlighted companies that trained large language models on copyrighted material without consent, or scraped data in ways that violate platform terms or privacy regulations. Even if the intent was innovation, the legal exposure—and reputational fallout—can be significant.

Boards should be asking whether internal teams understand the risk profile of their AI assets, and whether legal counsel has reviewed the end-to-end process—not just the output.

IP ownership in the age of machine learning

Unlike traditional software, generative AI introduces new ambiguity around intellectual property. Who owns the outputs of a model? Is a fine-tuned version of an open-source model considered a derivative work—or a new creation?

These are evolving questions, but they have real implications for valuation, partnerships, and monetization. Boards overseeing AI-driven companies should expect regular reporting on IP strategy and enforcement. That includes understanding how models are protected, what outputs are considered proprietary, and where potential infringement risks may lie.

In some cases, IP protections may need to be restructured entirely to account for the unique attributes of model-based systems.

Sovereign models and data localization are changing the AI landscape

As countries move to assert digital sovereignty, the governance of AI is no longer purely technical—it’s geopolitical.

The rise of sovereign models (such as those developed or regulated at a national level) is reshaping how companies train and deploy AI in different markets. From India’s Digital Personal Data Protection Act to the EU’s AI Act, regional frameworks are becoming both more assertive and more fragmented.

For multinationals, this means one AI governance framework may not be enough. Boards should ensure their companies are actively tracking regulatory developments and adapting policies to stay compliant—not just at the product level, but at the architectural level.

Board engagement doesn’t require technical depth—but it does require legal fluency

Not every board needs an AI expert, but every board overseeing an AI-dependent company needs a structured approach to risk and accountability.

That includes clear definitions of oversight responsibilities, ongoing education around emerging legal trends, and regular check-ins with legal and operational teams on AI-related risk posture.

Whether the company is building models in-house, partnering with third-party platforms, or embedding AI into customer-facing products, the board should have visibility into how decisions are made—and how they’re governed.

Final thought

Oversight of AI isn’t about mastering the algorithms. It’s about asking the right questions, ensuring the right protections are in place, and building structures that can evolve alongside the technology.

Boards that treat AI as a legal and strategic issue—not just a technical one—will be far better positioned to guide companies through what’s coming next.

How is your board preparing for the risks and opportunities AI presents?