Overcoming 4 Challenges When Implementing AI Video Generation in Your Content Strategy
AI video generation offers powerful possibilities for content strategies, but comes with specific implementation challenges. Leading experts share practical solutions for managing expectations, preparing quality data, streamlining processes, and maintaining consistent brand voice. These insights provide a clear roadmap for organizations looking to successfully incorporate AI video capabilities into their content workflows.
Manage Expectations for AI Video Integration
When implementing AI video generation tools like Runway, Kling and Veo into our content strategy, our biggest challenge was overcoming the misconception that these tools would provide instant, high-quality results with minimal effort. We addressed this by restructuring our workflow to integrate AI as just one component of a comprehensive process that still required substantial human planning, scripting, design work, and oversight. Looking back, I would have established more realistic expectations with our team from the beginning about the role AI would play in our content creation pipeline rather than positioning it as a revolutionary solution that would dramatically reduce production time and costs.
Clean Data First for Better AI Results
One of the biggest challenges we faced when adding AI video generation to our content strategy was dealing with insufficient or low-quality data. We expected the system to instantly produce engaging training videos, but the reality was different. The AI tool needed structured, high-quality content, and what we had at the start was scattered across different formats and not always up to date. The early outputs looked generic and at times even misleading, which reminded me of how critical it is to feed AI the right material from day one.
To overcome this, we pulled together a small team to clean and organize the data before running it through the AI tool again. We created a process where raw scripts were reviewed by our staff before the AI converted them into video form. I remember Elmo Taddeo mentioning in one of our conversations that treating AI as a "partner" instead of a shortcut was the mindset shift that made his own projects more successful. That advice stuck with me and helped us slow down, refine the workflow, and train our team to spot weak outputs early.
Looking back, I would start smaller next time. Instead of trying to build a full library of AI-driven video content right away, I'd begin with just a handful of test topics and refine the process step by step. This would have reduced costs and allowed us to iron out integration issues before scaling. For anyone exploring AI video generation, my advice is simple: prepare your data carefully, train your people on how the system works, and start small to avoid being overwhelmed.
Simplify Field Processes for Practical Adoption
A roofing contractor doesn't use "AI video generation." The closest thing we use is simple video and photo documentation. The challenge we faced with implementing a new visual system was getting the crew to consistently capture the high-quality footage after a job, because they saw it as unnecessary paperwork.
The problem was complexity. I initially required my foremen to shoot, trim, and upload the footage through a multi-step process, which failed immediately. It added ten minutes of office work to a physical job. The solution was simple delegation: we took all the technical work out of their hands and gave it to the office manager.
The most effective approach was making the process less than a minute for the field crew. Their only job is to snap a series of raw photos and short videos with their phones and send them to the office. The office manager handles the rest—the organizing, editing, and posting. This respects the crew's focus on physical labor.
What I would do differently next time is simplify the process before introducing it. My key lesson is that in a hands-on business, you have to value the worker's time more than the efficiency of the software. If a new tool adds more than two minutes of paperwork to their day, they will simply stop using it.
Customize AI Inputs to Match Brand Voice
One challenge I faced when implementing AI video generation in my content strategy was ensuring that the AI-generated videos were aligned with my brand's tone and voice. Initially, while the videos were technically impressive, they lacked the personal touch and nuances that resonate with my audience. The scripts, while well-written, sometimes felt too generic or robotic, which didn't match the authentic, conversational style I usually aim for.
To overcome this, I started customizing the input more carefully, providing the AI with specific details about my brand voice and tone before generating the videos. I also incorporated more detailed, human-written scripts to guide the AI's output, ensuring the final product felt more genuine. Additionally, I used AI video tools that allowed for greater post-production editing to add personality through custom visuals, voiceovers, and music, which helped bring the content to life.
Next time, I would experiment with interactive elements like allowing my audience to engage with the video content in real-time or using their feedback to further refine the video generation process. I would also spend more time up front fine-tuning the AI's understanding of my brand's voice, so the content could be even more aligned with my messaging from the start, potentially saving time in the editing phase.
