75% of marketers say generative AI helps them create more content than they would without it. That doesn't mean the content is good.
After watching dozens of advertisers experiment with AI copy tools, here's the honest assessment of what works, what doesn't, and where you should actually spend your time.
The Real Value Proposition
AI ad copy generators do three things well:
Speed through writer's block. When you're staring at a blank headline field for the fifteenth time today, having a tool that generates five mediocre options is better than having zero options. You're not using the output directly—you're using it as raw material.
Generate variations at scale. Testing 20 headline variations manually takes hours. AI can generate them in minutes. The quality varies, but the volume advantage is real for A/B testing programs.
Surface angles you wouldn't consider. Good tools trained on high-performing ad data occasionally suggest approaches you wouldn't think of. Not consistently, but often enough to be useful.
That's it. If you're expecting AI to replace your copywriting skills or generate ready-to-publish ads, you'll be disappointed.
What the Tools Actually Do
Most AI ad copy generators work the same way: you provide inputs (product info, target audience, tone, platform) and the tool generates variations using large language models. The differences are in the templates, training data, and integration features.
Dedicated ad copy tools (Anyword, Copy.ai, Jasper, AdCreative.ai) offer pre-built templates for Google Ads, Facebook Ads, product descriptions, and email subject lines. They're optimized for short-form marketing copy and often include predictive performance scores.
General-purpose AI (ChatGPT, Claude) can generate ad copy with the right prompting but lack ad-specific templates and performance prediction. They're more flexible but require more guidance.
Platform-native tools (Google's AI-powered asset suggestions, Meta's Advantage+ creative) generate copy within the advertising platforms themselves. They're convenient but offer less control.
The Honest Performance Assessment
Here's what practitioners actually report:
The good:
AI-generated variations can surface winning performers. Some advertisers report finding headline variations through AI testing that outperform human-written versions. The tools are genuinely useful for quantity—generating multiple options quickly.
The mediocre:
Most AI copy is generic. It hits the right structure but lacks the specific insights, emotional hooks, or brand voice that make copy compelling. One practitioner noted that automated creative "misses the mark for about 99% of our clients' products."
The bad:
Predictive performance scores are often unreliable. A tool might flag something as "high-performing" based on metrics that don't align with your actual conversion goals. And AI regularly generates copy that's technically correct but tonally wrong for your brand.
Where AI Copy Works Best
- High-volume testing environments. If you're running significant ad spend and need to test dozens of variations, AI-generated copy provides raw material faster than manual writing.
- Commodity products with straightforward value propositions. "50% off running shoes" doesn't require creative genius. AI can generate variations of straightforward offers effectively.
- Long-tail keyword ad groups. Writing unique ad copy for hundreds of specific search terms is tedious. AI can help generate relevant variations at scale.
- Initial brainstorming. When you're stuck, AI suggestions can break the logjam—even if you don't use them directly.
Where AI Copy Still Fails
- Brand voice consistency. AI can mimic tone, but it doesn't understand your brand's actual voice, history, or positioning. The copy often sounds generic even when technically correct.
- Emotional sophistication. Great ad copy connects with specific pain points, desires, and emotional triggers. AI generates surface-level emotional language but rarely hits the deep resonance that drives action.
- Competitive differentiation. AI trained on existing ads tends to generate copy that sounds like existing ads. It won't create the distinctive positioning that sets you apart.
- Complex B2B messaging. Technical products with nuanced value propositions require understanding that AI typically lacks. The output often oversimplifies or misses key differentiators.
- Regulatory compliance. Healthcare, financial services, and other regulated industries have specific claim requirements. AI doesn't reliably navigate these constraints.
The Workflow That Actually Works
The most effective practitioners treat AI as a starting point, not a destination:
Step 1:Generate volume
Use AI to create 10-20 variations quickly. Don't edit during generation—just collect raw material.
Step 2:Evaluate ruthlessly
Most AI output goes in the trash. Look for the 1-2 variations that have a kernel of something interesting—an angle, phrase, or structure worth developing.
Step 3:Rewrite substantially
Take the interesting elements and rewrite them with your brand voice, specific proof points, and emotional precision. The final copy might share only 20% DNA with the AI original.
Step 4:Test systematically
Run the human-refined variations against each other. Let performance data—not AI prediction scores—determine winners.
Step 5:Feed learning back
Document what works. Your understanding of your audience should grow with each test cycle. AI doesn't learn from your specific results unless you explicitly train it.
The Tool Selection Question
If you're going to use AI copy tools, here's the practical hierarchy:
- For Google Ads specifically: Tools like Anyword or platform-native suggestions work reasonably well because Google Ads copy is highly structured (headlines, descriptions, character limits). The constraint makes AI more effective.
- For Meta/social ads: AI struggles more because effective social copy is less formulaic. The emotional and visual integration requirements exceed what most tools can handle.
- For general copywriting: ChatGPT or Claude with well-crafted prompts often outperform dedicated tools because you have more control over the output. The premium tools' templates can actually constrain useful variation.
- For volume operations: If you're generating hundreds of product descriptions or ad variations, dedicated tools with batch processing (Copysmith, Narrato) save significant time despite quality tradeoffs.
What Not to Outsource to AI
Some copy elements should remain human-written:
- •Brand taglines and core messaging
- •High-stakes landing page headlines
- •Competitive positioning statements
- •Customer testimonials and case study narratives
- •Anything requiring specific expertise or technical accuracy
The risk of AI copy in these contexts isn't just poor performance—it's brand damage from generic, inaccurate, or tonally inappropriate messaging.
The Honest Bottom Line
AI ad copy generators save time. They don't save thinking.
The advertisers getting value from these tools are using them to accelerate workflows they already understand—generating variations, breaking writer's block, scaling testing programs. They're not using AI as a substitute for understanding their customers, crafting compelling positioning, or developing brand voice.
If you're struggling with ad copy, the solution probably isn't better AI tools. It's deeper customer research, clearer positioning, and more rigorous testing of human-generated ideas.
AI is a multiplier. It multiplies what you bring to it. Bring shallow understanding, get generic copy. Bring deep customer insight and clear positioning, get useful raw material that you can refine into something effective. The tools are getting better. But they're not getting better fast enough to replace the strategic thinking that makes copy actually work.
