4 Common Mistakes Creative Teams Make With Gen AI Workflows (And How to Avoid Them)
The potential for AI in creative workflows is huge, but many teams are still figuring out how to integrate it in a way that actually works.
If implemented poorly, AI can create more problems than it solves: from inaccurate or “hallucinated” outputs to inconsistent results and wasted time – not to mention budget!
If you’re exploring AI workflow opportunities – or are already using or testing it – here are some of the common mistakes we see creative teams make, and how to avoid them.
Mistake #1: Treating AI Like a ‘Magic Button’
Too often, teams expect AI to replace entire processes. But AI isn’t a one-click solution. On its own, it can produce results that are generic, inaccurate, or off-brand.
How to avoid it:
Think of AI as an accelerator, not a replacement. Use it to handle secondary creative challenges like generating new backgrounds for editorial, or the heavy lifting of bulk ecommerce edits, while your creative team focuses on refinement and brand polish. The best results come from a hybrid approach that’s AI-assisted and human-refined.
Mistake #2: Missing Product Accuracy
AI-generated models or assets can look great at first glance – until you notice a sixth finger or a floating limb! They also tend to alter important product details – colors, textures, dimensions, and construction often render incorrectly. These errors will mislead customers and damage trust.
How to avoid it:
Make quality control a mandatory step in your post-production process. AI can generate assets fast, but every output still needs human review. Experienced retouchers ensure product details are accurate, models look natural, and that final imagery reflects your brand’s unique aesthetic and tone across every channel.
Mistake #3: Skipping Legal & Ethical Checks
Using AI-generated models without considering likeness rights, or repurposing photography into new formats not covered by the original contract, can expose your brand to legal and reputational issues.
How to avoid it:
Work with partners who understand the legal side of AI adoption. Opt for custom, exclusive avatars to avoid shared likenesses or digital twins generated from consenting, paid models to avoid contract breaches. And make sure your contracts, licensing, and disclosures are up to date before scaling AI content.
Mistake #4: Failing to Scale Responsibly
It’s fun to experiment with AI – and there’s definitely a place for that. But when it comes to officially integrating it into your workflow, it’s time to get serious. Many teams scale too quickly without clear guardrails or defined use cases, leading to inconsistent and inaccurate outputs.
How to avoid it:
Build a framework before you scale. Define where AI should be used, how quality will be checked, and who has sign-off. Start with internal or low-risk use cases before deploying AI outputs publicly – this gives your team time to refine workflows and catch issues early. Measure results, refine, then expand. It’s better to scale gradually and confidently than rush and risk brand or compliance issues down the line.
The Bottom Line
Gen AI has the potential to add real value to creative production – improving efficiency and opening up new streams for content creation. But it takes time and thoughtful planning to build workflows that are responsible and aligned with your brand.
At VMG, we’re actively testing AI workflows – from automating internal production processes to responsibly producing AI-generated models to creating stills-to-video conversions. But here’s the key: we never leave it to AI alone. Every output is reviewed and refined by our creative experts, using our traditional post-production experience, to ensure each asset meets the same high-quality, human standard we all expect.
Thinking about adding AI into your creative workflows? We’ve been seeing what’s working across leading brands, and would be happy to share where it has the biggest impact. Get in touch if you’d like to learn more.
