Create a virtuous cycle for your knowledge base. Use AI to analyze closed support tickets, identify the core issue and solution, and propose a new FAQ entry if one doesn't exist. A human then reviews and approves the suggestion, continuously improving the AI's data source.
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Integrate AI agents directly into core workflows like Slack and institutionalize them as the "first line of response." By tagging the agent on every new bug, crash, or request, it provides an initial analysis or pull request that humans can then review, edit, or build upon.
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.
To find high-impact AI opportunities, reframe the goal from speed to quality. Ask what a perfect team with unlimited time would do. This helps identify transformative workflows, like analyzing every support ticket to improve documentation, rather than just doing existing tasks faster.
To manage immense feedback volume, Microsoft applies AI to identify high-quality, specific, and actionable comments from over 4 million annual submissions. This allows their team to bypass low-quality noise and focus resources on implementing changes that directly improve the customer experience.
When an AI tool makes a mistake, treat it as a learning opportunity for the system. Ask the AI to reflect on why it failed, such as a flaw in its system prompt or tooling. Then, update the underlying documentation and prompts to prevent that specific class of error from happening again in the future.
To create resonant content, move beyond guessing customer problems. Analyze transcripts of past sales calls with an AI tool to identify recurring pain points, common questions, and the exact language your audience uses to describe their challenges.
Prioritize using AI to support human agents internally. A co-pilot model equips agents with instant, accurate information, enabling them to resolve complex issues faster and provide a more natural, less-scripted customer experience.
Building a comprehensive context library can be daunting. A simple and effective hack is to end each work session by asking the AI, "What did you learn today that we should document?" The AI can then self-generate the necessary context files, iteratively building its own knowledge base.
Instead of guessing where AI can help, use AI itself as a consultant. Detail your daily workflows, tasks, and existing tools in a prompt, and ask it to generate an "opportunity map." This meta-approach lets AI identify the highest-impact areas for its own implementation.
Instead of manually maintaining your AI's custom instructions, end work sessions by asking it, "What did you learn about working with me?" This turns the AI into a partner in its own optimization, creating a self-improving system.
