Meet Roger, a content marketer driven by his love for online search, digital marketing, and performance marketing. When he's not immersed in the latest updates on Google, AI and social media, you'll find him passionately crafting strategies to simplify online searches for people, sparing them the frustration of navigating through endless pages. As a marketer, Roger Match has turned into the perfect match for Superside, helping us showcase our purpose, objectives and essence to the world.
Imagine running a modern Formula 1 team with a 1990s pit crew strategy. The mechanics are talented, and the car is powerful. But every tire change takes ages because the process itself is outdated.
That’s the situation many enterprise teams face with video production today. The talent is strong, and the creative vision is there. Yet the workflows still reflect how video production worked before AI tools came along.
Picture the moment leadership approved rolling out AI across the company. The budget was set, the tools selected and IT given the green light. Someone likely said, “This is going to change how we work.”
Six months later, most employees haven’t adopted AI. The few who use it do so sporadically and don’t fully trust the results. Fixes take as long as the work itself.
Enterprise creative teams simply don’t have the time to turn poor-quality AI outputs into on-brand creative. They’re under too much pressure already.
Unfortunately, the AI productivity paradox plagues many creative teams. They adopt tools like Midjourney to achieve faster workflows, only to end up prompting away the generic “AI aesthetic.”
Your company licensed several AI tools months ago. The rollout launched, training went well and a few early adopters experimented. Yet most teams haven’t touched the tools, and the ones who have aren’t seeing results.
Sound familiar? Many organizations deploy AI tools and training, but miss a key detail: the real problems these tools solve. Without use cases, creative teams know what the tech does but have no idea where it fits into their existing workflows.
The promise of AI-powered creative processes is speed and the ability to produce creative at scale. Creative teams can generate hundreds of images or dozens of ad variations in minutes.
In reality, much of this output simply doesn’t meet the mark. The AI systems teams rely on may be fast, but their results are often generic, off-brand and unmistakably machine-generated.
The most effective digital brand experiences don’t feel like marketing. They create meaningful moments grounded in a deep understanding of the audience and what the brand wants them to think and feel.
There are many great digital brand experience examples out there. But the thinking, strategy and creative choices behind the ones that truly connect are rarely shared.
AI has raised expectations, with many enterprises pushing for higher ad creative turnarounds, more volume and variations and continuous testing to beat competitors.
But the rush to do more has created a dangerous trap. Many teams now generate hundreds of ad variations in the hope that sheer volume will compensate for a lack of strategy.
Thanks to AI, what used to take days to produce now takes minutes, and what used to require a full creative team can now be generated in a few clicks. The result is an internet flooded with content and brands competing harder than ever for the same slice of customer attention.
In this environment, your brand can’t afford to stay in one lane. Audiences move fluidly between social channels. If you don’t show up on LinkedIn, X, Instagram or the next big platform, someone else will. A HubSpot study found that marketers use an average of four social platforms, and those who diversify consistently outperform those who don’t.
We’re at a point where enterprise video teams no longer debate whether to use AI. Now, they focus on where to apply it, how to implement it and how far they can scale it.
Wondering what separates teams that make real progress from those stuck in an inefficient, experimental phase? Structure.
