Instead of focusing on AI for generating final assets, Amazon applies it to solve specific workflow bottlenecks. For one campaign, they used a custom AI tool to curate millions of customer reviews, identifying the most poetic ones in a fraction of the time it would take humans, thus using AI for insight discovery.

AI can analyze a customer's support history to predict their behavior. For instance, if a customer consistently calls about shipping delays, an AI agent can proactively contact them with an update before they reach out, transforming a reactive, negative interaction into a positive customer experience.

Customer reviews are not just for marketing. A parking company analyzed feedback to optimize employee scheduling, improving service and customer experience. This demonstrates how review data can drive core operational improvements far beyond the marketing department.

Effective AI moves beyond a simple monitoring dashboard by translating intelligence directly into action. It should accelerate work tasks, suggest marketing content, identify product issues, and triage service tickets, embedding it as a strategic driver rather than a passive analytics tool.

Go beyond simple prospect research and use AI to track broad market sentiment. By analyzing vast amounts of web data, AI can identify what an entire audience is looking for and bothered by right now, revealing emerging pain points and allowing for more timely and relevant outreach.

Go beyond just generating documents. PM Dennis Yang uses an AI agent in Cursor to read comments on a Confluence PRD, categorize them by priority, draft responses, and post them on his behalf. This automates the tedious but critical process of acknowledging and incorporating feedback.

Raw customer feedback is noise. To make it actionable for Product, organize it along two dimensions: impact and frequency. This simple framework separates signal from noise, distinguishing high-priority, high-impact issues from niche requests and creating a clear basis for roadmap decisions.

Personalization often begins as an isolated experiment. Microsoft successfully integrated it into their core operations by using AI to manage the complexity. This transformed personalization from a side project managed by a few people into an embedded, company-wide capability driving measurable results.

For companies wondering where to start with AI, target the most labor-intensive, process-driven functions. Customer support is an ideal starting point, as AI can handle repetitive tasks, leading to lower costs, faster response times, and an improved customer experience while freeing up human agents for more complex issues.