Advanced generative media workflows are not simple text-to-video prompts. Top customers chain an average of 14 different models for tasks like image generation, upscaling, and image-to-video transitions. This multi-model complexity is a key reason developers prefer open-source for its granular control over each step.

The most significant productivity gains come from applying AI to every stage of development, including research, planning, product marketing, and status updates. Limiting AI to just code generation misses the larger opportunity to automate the entire engineering process.

Building a single, all-purpose AI is like hiring one person for every company role. To maximize accuracy and creativity, build multiple custom GPTs, each trained for a specific function like copywriting or operations, and have them collaborate.

For marketing, resist the allure of all-in-one AI platforms. The best results currently come from a specialized stack of hyper-focused tools, each excelling at a single task like image generation or presentation creation. Combine their outputs for superior quality.

Most users get poor results from creative AI due to complex prompting. AI agent tools act as an intermediary layer, handling the expert-level prompting and workflow automation. This makes advanced, professional-quality results accessible to beginners without a steep learning curve.

Tools like Descript excel by integrating AI into every step of the user's core workflow—from transcription and filler word removal to clip generation. This "baked-in" approach is more powerful than simply adding a standalone "AI" button, as it fundamentally enhances the entire job-to-be-done.

Instead of relying on a single, all-purpose coding agent, the most effective workflow involves using different agents for their specific strengths. For example, using the 'Friday' agent for UI tasks, 'Charlie' for code reviews, and 'Claude Code' for research and backend logic.

To maximize AI's impact, don't just find isolated use cases for content or demand gen teams. Instead, map a core process like a campaign workflow and apply AI to augment each stage, from strategy and creation to localization and measurement. AI is workflow-native, not function-native.

Top performers won't rely on a single AI platform. Instead, they will act as a conductor, directing various specialized AI agents (like Claude, Gemini, ChatGPT) to perform specific tasks. This requires understanding the strengths of different tools and combining their outputs for maximum productivity.