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This checklist is designed to help product and engineering teams assess their readiness before integrating AI into their product. It covers key areas such as security and compliance, cost control, deployment best practices, monitoring, user experience, and architectural alignment. The goal is to ensure AI features are implemented responsibly, efficiently, and in line with broader product strategy.

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🔒 1. Security & Compliance

• [ ] Ensure that the AI provider's data centers are compliant with local regulations.
• [ ] Implement Data Retention policy in case you need to store/analyse inputs/outputs (fine-tunning/improvements…).
• [ ] All AI providers are accessible via APIs. When the API is accessible with a temporary token. The token should be requested by the backend, meaning the frontend should never call directly an AI API.

💸 2. Cost Management

• [ ] Continuously monitor and track the costs associated with AI usage.
• [ ] Log and track AI-specific metrics: response time, token usage, latency, etc.
• [ ] Set up alerts to detect and respond to cost anomalies promptly.
• [ ] Develop cost forecasts based on analysis from beta users.
• [ ] Implement product limitations on AI usage, such as maximum pages or tokens, restricting usage to top-tier customers, enforcing rate limits, or setting daily budgets.
• [ ] Evaluate different AI models — not just the latest — as older or smaller models are often more cost-efficient and sufficient for many use cases.
• [ ] Consider developing a caching strategy for common AI outputs to reduce redundant token usage.

⚙️ 3. Deployment & DevOps

• [ ] Deploy with a progressive rollout.
• [ ] Disable AI in non-production environments once the feature is live.
• [ ] Ensure AI can be easily toggled on or off, for example, using feature flags or Add-On at user level.
• [ ] Use version control systems to manage updates to AI models and APIs.