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AI tools entering established industries should mirror the existing, multi-step professional workflow. Coil, an AI video platform, implements distinct stages for casting, costume design, and location scouting. This familiar structure makes the powerful new technology feel intuitive and less threatening to industry veterans.

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To overcome employee fear of AI, don't provide a general-purpose tool. Instead, identify the tasks your team dislikes most—like writing performance reviews—and demonstrate a specific AI workflow to solve that pain point. This approach frames AI as a helpful assistant rather than a replacement.

A systematic approach to AI video can reduce production time by over 90%. The process involves: 1) Finalizing the core idea, 2) Creating a detailed storyboard with scenes and dialogue, 3) Generating static reference images for each scene, and 4) Generating video clips and performing a final edit.

Past tech solutions for fragmented industries like logistics often failed because they required universal adoption of a new platform. AI can succeed by meeting users in their existing, messy channels—email, texts, calls. It automates work within current workflows rather than forcing a difficult behavioral change, lowering adoption barriers.

Getting traditional companies to adopt AI for their entire production process is a big ask. A "land and expand" strategy is more effective: start by offering the tool for pre-visualization. This provides immediate value with low perceived risk, building trust for deeper integration later.

ElevenLabs' CEO predicts AI won't enable a single prompt-to-movie process soon. Instead, it will create a collaborative "middle-to-middle" workflow, where AI assists with specific stages like drafting scripts or generating voice options, which humans then refine in an iterative loop.

To move beyond solo creation and produce high-quality AI video at scale, adopt a structured workflow with five core roles: writer, director, cinematographer, animator, and editor. This mirrors traditional animation and allows for collaboration and specialization.

The path to enterprise AI adoption follows a typical change curve. To bypass initial fear and rejection, organizations should first apply AI to transform familiar, high-friction workflows. This strategy builds momentum and demonstrates value before tackling entirely new, innovative business models.

To get mainstream users to adopt AI, you can't ask them to learn a new workflow. The key is to integrate AI capabilities directly into the tools and processes they already use. AI should augment their current job, not feel like a separate, new task they have to perform.

To penetrate traditional industries like Hollywood, AI companies should avoid a "disrupt and destroy" narrative. Instead, frame the product as a tool that enhances existing creators' abilities—"replacing the camera, not the filmmaker"—to lower resistance and encourage adoption by incumbents.

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.