When buying AI solutions, demand transparency from vendors about the specific models and prompts they use. Mollick argues that 'we use a prompt' is not a defensible 'secret sauce' and that this transparency is crucial for auditing results and ensuring you aren't paying for outdated or flawed technology.

Beyond data privacy, a key ethical responsibility for marketers using AI is ensuring content integrity. This means using platforms that provide a verifiable trail for every asset, check for originality, and offer AI-assisted verification for factual accuracy. This protects the brand, ensures content is original, and builds customer trust.

While a general-purpose model like Llama can serve many businesses, their safety policies are unique. A company might want to block mentions of competitors or enforce industry-specific compliance—use cases model creators cannot pre-program. This highlights the need for a customizable safety layer separate from the base model.

Shift the view of AI from a singular product launch to a continuous process encompassing use case selection, training, deployment, and decommissioning. This broader aperture creates multiple intervention points to embed responsibility and mitigate harm throughout the lifecycle.

As AI tools become more accessible, the primary risk for established brands is a loss of control. Ensuring AI-generated content adheres to strict brand guidelines and complex regulatory requirements across different regions is a massive governance challenge that will define the next year of enterprise AI adoption.

The market reality is that consumers and businesses prioritize the best-performing AI models, regardless of whether their training data was ethically sourced. This dynamic incentivizes labs to use all available data, including copyrighted works, and treat potential fines as a cost of doing business.

Simply providing data to an AI isn't enough; enterprises need 'trusted context.' This means data enriched with governance, lineage, consent management, and business rule enforcement. This ensures AI actions are not just relevant but also compliant, secure, and aligned with business policies.

For enterprises, scaling AI content without built-in governance is reckless. Rather than manual policing, guardrails like brand rules, compliance checks, and audit trails must be integrated from the start. The principle is "AI drafts, people approve," ensuring speed without sacrificing safety.

While an AI model itself may not be an infringement, its output could be. If you use AI-generated content for your business, you could face lawsuits from creators whose copyrighted material was used for training. The legal argument is that your output is a "derivative work" of their original, protected content.