How Marketers Are Using AI (Real Examples)

Every marketing conference talks about AI. But what are marketers actually doing with it?

I've talked to dozens of marketing teams over the past year. Here's what's working, what's not, and the specific tools and workflows people are using.

No hype, just practical applications.

Content Creation: The Most Common Use Case

This is where most marketing teams start, and frankly, where most get it wrong.

What Doesn't Work

"Write me a blog post about X" → Publish

This approach creates generic content that:

• Sounds like every competitor's AI content
• Lacks unique insights
• Performs poorly in search (Google can smell it)
• Damages your brand voice

What Actually Works

AI + Human Expertise = Content that ranks

Here's the workflow I see from successful teams:

1. Research phase: Use Perplexity to understand what's ranking and why. "What questions about [topic] are people asking? What gaps exist in current content?"

2. Outline with subject matter experts: Have humans with real expertise provide insights, examples, and opinions. AI doesn't have original thoughts.

3. Draft assistance: Use Claude to help structure and draft, but with heavy human input throughout.

4. Editing and enhancement: AI helps with clarity, SEO optimization, and format suggestions.

5. Human review and voice injection: Final pass to ensure it sounds like your brand, not like everyone else.

Time saved: About 40% compared to fully manual process. Quality: Equal or better than pre-AI content.

Email Marketing: Personalization at Scale

This is where AI is genuinely transforming marketing.

Subject Line Optimization

Tools like Phrasee and Jasper generate and test email subject lines at scale.

The workflow:

1. Describe your email content and goal
2. AI generates 10-20 subject line variants
3. Human selects the best ones for A/B testing
4. AI learns from results and improves over time

Results I've seen: 15-30% improvement in open rates over human-only subject lines.

Personalized Copy

The real power is personalization that humans can't do at scale.

Example: A SaaS company sends different email copy based on:

• User's industry
• Stage in funnel
• Previous interactions
• Feature usage patterns

AI generates these variants from a core template. A human writes the core, AI creates the variations.

SEO: Research and Optimization

Keyword Research

Tools like Surfer SEO, Clearscope, and Frase use AI to:

• Analyze what top-ranking content includes
• Suggest topics and keywords to cover
• Score your content against competitors

Practical workflow:

1. Choose target keyword
2. AI analyzes top 10 ranking pages
3. Creates a brief: topics to cover, questions to answer, terms to include
4. Writer uses brief as a guide (not a rigid script)
5. AI scores the draft and suggests improvements

Content Briefs

This used to take hours. Now:

Prompt: "Create a content brief for an article targeting [keyword]. Analyze the top-ranking content and tell me what topics I need to cover, what questions to answer, and what unique angle might differentiate us."

The brief is done in minutes. The writing still requires human expertise.

Social Media: Ideation and Scheduling

Content Ideation

What works: "Give me 20 LinkedIn post ideas about [topic] that would resonate with [audience]. Include hooks and key points for each."

You won't use all 20, but you'll probably find 3-5 worth developing.

Caption Writing

AI excels at generating caption variants. Write one version, ask for 5 reformats for different platforms.

What Doesn't Work

Full automation of social media. AI-generated posts with no human personality perform worse than authentic content. The algorithm rewards genuine engagement.

Paid Advertising: Copy Testing

Ad Copy Generation

Google and Meta both have AI tools for generating ad variations. But third-party tools like Jasper and Copy.ai often produce better results.

Workflow:

1. Define target audience, pain points, and offer
2. Generate 20-30 ad copy variations
3. Human reviews and selects best options
4. A/B test in platform
5. Feed results back to AI for future improvements

Landing Page Copy

AI can generate multiple headline and body copy variations quickly. The key is testing relentlessly.

One team I talked to generates 50 headline variations, tests the top 10, then uses AI to create variations on the winners. Continuous optimization.

Customer Research: The Hidden Gem

Survey Analysis

Dump survey responses into Claude and ask:

• "What are the top 5 themes in these responses?"
• "What complaints appear most frequently?"
• "Identify any surprising insights"

Works incredibly well for open-ended responses that are hard to analyze manually.

Competitive Analysis

Prompt: "Analyze these competitor websites and identify their key messaging, positioning, and apparent target audiences."

AI does the initial analysis, humans draw the strategic conclusions.

Review Mining

Paste Amazon reviews or G2 reviews and ask: "What do customers love? What do they complain about? What features are most mentioned?"

This used to require hiring someone. Now it takes 10 minutes.

Tools Marketing Teams Actually Use

Based on conversations with ~50 marketing teams:

Content Creation:

• Claude (writing quality)
• Jasper (marketing-specific)
• Copy.ai (ad copy)

SEO:

• Surfer SEO
• Clearscope
• Frase

Research:

• Perplexity
• ChatGPT
• NotebookLM

Social:

• Later (AI scheduling)
• Taplio (LinkedIn-specific)

Email:

• Jasper
• Lavender (for sales emails)

Any-purpose:

• ChatGPT Plus
• Claude Pro

What's Not Working (Yet)

To be fair, here's where AI marketing still falls short:

Brand voice consistency: AI struggles to maintain consistent brand voice across all content. Requires heavy human oversight.

Strategic thinking: AI can execute tactics but can't develop strategy. It doesn't understand your market position or competitive dynamics.

Creative breakthrough: The "big idea" for a campaign still needs to come from humans. AI can iterate on ideas but rarely originates them.

B2B expertise content: For deeply technical or industry-specific content, AI still lacks the expertise. You need a human SME driving the content.

My Recommendations for Marketing Teams

Start here:

1. Pick one workflow (content creation is usually best to start)
2. Test AI assistance for 30 days
3. Measure time saved and quality impact
4. Expand to other areas

Don't skip:

• Training your team on prompting
• Creating brand voice guidelines for AI use
• Establishing quality review processes

Avoid:

• Replacing your team with AI (augment, don't replace)
• Publishing AI content without human review
• Expecting AI to do strategy

The Bottom Line

AI is genuinely useful for marketing in 2026. But the best results come from humans and AI working together, not AI working alone.

The marketers who win are those who:

• Use AI for research and efficiency
• Keep humans in charge of strategy and creativity
• Maintain quality through review and refinement

Start experimenting. Measure results. Iterate.

That's how marketing has always worked. AI is just a new tool in the kit.