It has become too easy for companies to use the term AI in and around what their core competency may be. What does that mean? Does it help or hurt the consumer of said services? Does it do anything at all? Is it just today’s buzzword and in order to try to stay relevant, they’re ‘keeping up with the Jones’ and feel compelled to add the term AI to their repertoire? I believe that using the term AI with no direct correlation to someone’s business, economic needs and goals is disingenuous and misleading. Too often, I’ve seen media technology ‘leaders’ say something to the effect where ‘NOW is the time for you to talk to us about how AI will transform your business.’ Rhetoric like this only serves to confuse media executives. Why? They either see through the cotton candy nature of such a statement or aren’t able to isolate the signal from the noise and understand why, where and specifically how AI can and will help improve their business processes and potentially streamline their opex and personnel needs.
Artificial intelligence (AI) is at the center of nearly every industry conversation today—hailed as both a transformative force and a source of confusion. The video production and content management landscape is no exception. From promises of automated workflows to fears of job displacement, the dialogue around AI is often clouded by ambiguity and buzzwords.
But beneath the hype lies a tangible opportunity: AI-first video processing. This approach doesn’t just sprinkle AI into existing workflows—it fundamentally reimagines them, driving efficiency, scalability, and cost-effectiveness in ways that manual processes simply cannot match.
The Confusion Around AI in Video Workflows
The confusion around AI stems from a mix of oversimplification and overcomplication. On one hand, there’s the perception that AI will magically handle everything. On the other, there’s the misconception that it requires complex infrastructure and expertise beyond reach.
In reality, effective AI implementation in video workflows requires neither blind faith nor excessive complexity. It demands a **strategic, outcome-driven approach**—one that targets the most time-consuming, repetitive, and error-prone tasks.
What Does AI-First Really Mean?
An AI-first approach isn’t about selectively applying AI to small parts of the process—it’s about **reimagining the entire workflow** through an automation-first lens. In video processing, this means:
- Automated Scheduling & Monitoring:
AI can handle the scheduling and distribution of video assets without manual intervention. Through automated content triggers and programmatic workflows, videos can be routed, queued, and published across platforms in real time—saving hours of manual coordination. - AI-Powered Clip Cutting & Highlight Generation:
Instead of relying on editors to sift through hours of footage, AI can instantly detect and extract key moments based on contextual signals, speaker changes, or visual cues. This dramatically reduces the time spent on **manual clip creation** and ensures consistency in highlight generation. - Real-Time Video Metadata & Indexing:
Manual tagging and metadata assignment is slow and prone to inconsistency. AI-first workflows use **automated metadata enrichment** to generate contextual tags, descriptions, and searchable attributes—turning video libraries into dynamic, easily navigable content hubs. - Proactive Quality Control & Issue Detection:
AI-driven monitoring tools can flag technical issues (e.g., audio drift, frame drops, or content anomalies) in real time, reducing the need for manual quality assurance. - The Impact: Efficiency, Scale, and Cost Reduction
Adopting an AI-first model for video processing has immediate and long-term benefits:
✅Faster Turnaround Times:
Automating repetitive tasks like clip generation, tagging, and monitoring slashes production timelines—enabling teams to meet distribution windows without sacrificing quality.
✅ Reduced Costs Through Automation:
Minimizing manual intervention reduces labor-intensive bottlenecks. This not only cuts direct costs but also improves operational scalability without requiring proportional headcount increases.
✅ Consistent Quality at Scale:
AI eliminates inconsistencies caused by human fatigue or oversight. Automated metadata tagging and clip selection ensure uniform standards across large video libraries.
✅ Greater Creative Focus:
By removing the burden of repetitive tasks, creative teams can focus on **higher-value work**—such as crafting compelling narratives, refining visual aesthetics, and strategizing distribution.
Embracing AI-First, Not AI-Only
It’s important to clarify that an AI-first approach does not mean removing human involvement—it means optimizing human creativity by **offloading the grunt work** to AI. The result? A more efficient, agile, and cost-effective video production pipeline.
As AI capabilities continue to evolve, video production teams that embrace AI-first workflows will be positioned to **scale faster, reduce costs, and maintain a competitive edge** in the increasingly dynamic media landscape.
Matt Smith, Chief Evangelist, Akta