To move beyond basic AI tasks, chain multiple skills together. A "skill chain" runs a sequence of specialized AI skills—like drafting, copywriting, and quality assurance—to produce a complex output with higher fidelity and less human intervention.

After a document is drafted, lawyers ask an AI tool to review it for missed points or alternative angles. The tool acts like an impartial third party with vast analytical recall, offering suggestions that refine and improve the final work product at a minimal cost.

The podcast team used Claude Code to cross-check every number and chart in a 50+ page report against the source data, as well as proofread the text. This is a powerful use case for AI in tedious verification tasks where human attention wanes and errors can easily slip through.

To adopt AI without sacrificing accuracy, BlackRock established a "first draft principle." AI can generate the initial version of any document—from client presentations to prospectuses—but it must then pass through the rigorous, multi-layered human review process already in place, ensuring control and quality.

Instead of solely focusing on AI fallibility, a major application is using AI agents to audit human work. Perplexity's "Final Pass" feature analyzes documents for factual errors and internal inconsistencies, finding glaring mistakes in things like Gartner's earnings press releases and work done by professional accountants.

Instead of pursuing full automation, a powerful use case for internal agents is augmenting workflows. For example, a 'legal review' agent can screen marketing copy, approve standard material, and flag ambiguous content for human lawyers, accelerating the process without removing necessary oversight.

AI is seen not as a replacement but as a tool to handle repetitive tasks like checking abbreviations, style guides, and grammar. This automation allows human editors to focus on higher-value work: shaping the narrative, ensuring audience comprehension, and partnering on strategic messaging.

The goal for AI isn't just to match human accuracy, but to exceed it. In tasks like insurance claims QA, a human reviewing a 300-page document against 100+ rules is prone to error. An AI can apply every rule consistently, every time, leading to higher quality and reliability.

Structure your AI team with a 'junior' agent for execution (e.g., writing copy) and a 'senior' manager agent for review. This mimics a human workflow, allowing the senior agent to catch errors and provide feedback to the junior agent, improving the quality and reliability of the final output.