Leaders must resist the temptation to deploy the most powerful AI model simply for a competitive edge. The primary strategic question for any AI initiative should be defining the necessary level of trustworthiness for its specific task and establishing who is accountable if it fails, before deployment begins.

In an era of opaque AI models, traditional contractual lock-ins are failing. The new retention moat is trust, which requires radical transparency about data sources, AI methodologies, and performance limitations. Customers will not pay long-term for "black box" risks they cannot understand or mitigate.

Product managers should leverage AI to get 80% of the way on tasks like competitive analysis, but must apply their own intellect for the final 20%. Fully abdicating responsibility to AI can lead to factual errors and hallucinations that, if used to build a product, result in costly rework and strategic missteps.

Implementing trust isn't a massive, year-long project. It's about developing a "muscle" for small, consistent actions like adding a badge, clarifying data retention, or citing sources. These low-cost, high-value changes can be integrated into regular product development cycles.

Treating AI risk management as a final step before launch leads to failure and loss of customer trust. Instead, it must be an integrated, continuous process throughout the entire AI development pipeline, from conception to deployment and iteration, to be effective.

Organizations must urgently develop policies for AI agents, which take action on a user's behalf. This is not a future problem. Agents are already being integrated into common business tools like ChatGPT, Microsoft Copilot, and Salesforce, creating new risks that existing generative AI policies do not cover.

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.

The success of your AI tool depends heavily on the vendor's human experts. Don't get stuck with a sales rep who doesn't understand the product. Demand access to their solution architects and onboarding specialists *before* you sign, ensuring you have a capable partner to guide your implementation.

In traditional product management, data was for analysis. In AI, data *is* the product. PMs must now deeply understand data pipelines, data health, and the critical feedback loop where model outputs are used to retrain and improve the product itself, a new core competency.