Overwhelmed by Slack messages and internal documents? Build a Zapier agent connected to your company's knowledge base. Feed it your job description and current projects, and the agent can proactively scan all communications and deliver a weekly summary of only the updates relevant to your specific role.
Marketers often buy specialized SaaS tools for tasks like lead routing. These are often just a database, workflows, and an AI model, which can be replicated for a fraction of the cost using an orchestration platform like Zapier. This approach provides more control and customization over your marketing stack.
Marketers mistakenly believe implementing AI means full automation. Instead, design "human-in-the-loop" workflows. Have an AI score a lead and draft an email, but then send that draft to a human for final approval via a Slack message with "approve/reject" buttons. This balances efficiency with critical human oversight.
Don't wait for large corporate campaigns to get audience feedback. Marketers should be "religiously" creating content on their personal social channels to micro-test messaging, language, and program ideas. This provides a direct, rapid feedback loop on what the audience actually cares about, enabling content-led innovation.
Instead of spending time trying to craft the perfect prompt from scratch, provide a basic one and then ask the AI a simple follow-up: "What do you need from me to improve this prompt?" The AI will then list the specific context and details it requires, turning prompt engineering into a simple Q&A session.
As AI tools become operable via plain English, the key skill shifts from technical implementation to effective management. People managers excel at providing context, defining roles, giving feedback, and reporting on performance—all crucial for orchestrating a "team" of AI agents. Their skills will become more valuable than pure AI expertise.
Don't just set and forget your lead scoring AI. Create a separate, time-based agent that analyzes recent closed-won deals. This "meta-agent" can then identify new success patterns and suggest updates to the primary scoring agent's prompt, ensuring your qualification model evolves with live data.