People struggle with AI prompts because the model lacks background on their goals and progress. The solution is 'Context Engineering': creating an environment where the AI continuously accumulates user-specific information, materials, and intent, reducing the need for constant prompt tweaking.

Users who treat AI as a collaborator—debating with it, challenging its outputs, and engaging in back-and-forth dialogue—see superior outcomes. This mindset shift produces not just efficiency gains, but also higher quality, more innovative results compared to simply delegating discrete tasks to the AI.

The most effective way to use AI in product discovery is not to delegate tasks to it like an "answer machine." Instead, treat it as a "thought partner." Use prompts that explicitly ask it to challenge your assumptions, turning it into a tool for critical thinking rather than a simple content generator.

The tendency for AI models to "make things up," often criticized as hallucination, is functionally the same as creativity. This trait makes computers valuable partners for the first time in domains like art, brainstorming, and entertainment, which were previously inaccessible to hyper-literal machines.

AI-generated "work slop"—plausible but low-substance content—arises from a lack of specific context. The cure is not just user training but building systems that ingest and index a user's entire work graph, providing the necessary grounding to move from generic drafts to high-signal outputs.

To avoid generic, creatively lazy AI output ("slop"), Atlassian's Sharif Mansour injects three key ingredients: the team's unique "taste" (style/opinion), specific organizational "knowledge" (data and context), and structured "workflow" (deployment in a process). This moves beyond simple prompting to create differentiated results.

While AI offers efficiency gains, its true marketing potential is as a collaborative partner. This "designed intelligence" approach uses AI for scale and data processing, freeing humans for creativity, connection, and building empathetic customer experiences, thus amplifying human imagination rather than just automating tasks.

Don't use AI to generate generic thought leadership, which often just regurgitates existing content. The real power is using AI as a 'steroid' for your own ideas. Architect the core content yourself, then use AI to turbocharge research and data integration to make it 10x better.

Contrary to fears that AI averages out creativity, it can act as a partner to challenge a writer's habitual thinking, suggest alternative phrasings, and identify blind spots, ultimately leading to more original output.