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Artificial intelligence is changing how businesses create visuals for their marketing. One of the most popular tools today is AI image generation, which lets marketers produce images quickly and at a low cost. For businesses in the consideration stage, those comparing different marketing solutions, AI can look like a simple fix for creating more content in less time. But like any tool, it comes with strengths and limits.

THAT Agency works with companies that want to grow their online presence, and many of them are curious about how AI can support their visual content. To use AI effectively, marketers need to understand what it can do well, where it struggles, and how to avoid common mistakes. This guide explains the do’s and don’ts of using AI for marketing so your visuals stay clear, accurate, and on-brand.

Why AI Image Generation Is Important for Marketers

Today’s digital world requires brands to produce visuals at a fast pace. Websites, social media channels, ads, emails, and blogs all need strong images to capture attention. AI image generation helps marketers deal with this growing demand by offering:

• Quick brainstorming and concept creation
• Low-cost visual testing
• Faster production of images
• Easier experimentation with layouts and styles

These benefits appeal to many types of businesses, from dealerships to e-commerce stores to B2B service providers. But to get the most value out of AI, marketers need to use it carefully and pair it with a strong, human-led strategy. For a deeper look at how AI impacts strategy, review this guide on AI in digital marketing.

What Marketers Can Do With AI Image Generation

AI image generation gives marketing teams new ways to create visuals fast. It does not replace designers, but it helps teams explore ideas, test visuals, and produce content more quickly. Below are the main tasks AI handles best, with detailed steps, examples, and tips for each.

1. Create Fast Visual Concepts

AI is great for generating many concept ideas quickly. Instead of waiting days for drafts, you can get dozens of directions in minutes.

How to use it:

• Write a short prompt describing what you want (see example prompts below).
• Ask for variations (different moods, colors, or layouts).
• Save the best versions and give them to a designer to polish.

This workflow pairs well with testing approaches like those explained in this article on data driven marketing.

What to ask AI for:

• Different styles (minimal, retro, modern, hand-drawn)
• Backgrounds (studio, outdoor, abstract, textured)
• Colors (brand palette, high-contrast, muted tones)
• Layout ideas (left-aligned text, full-bleed image, product on the right)

Example prompts:

• “Create five hero image concepts for a software homepage in a modern flat style using blue and white.”
• “Generate three poster ideas showing teamwork, bright colors, and a bold headline space.”

Quick tips:

• Ask for multiple aspect ratios (square for social, wide for web, tall for stories).
• Use images as inspiration by uploading an existing graphic if your tool supports it.

2. Make Images for Blogs and Other Content

AI can produce visuals that support long-form content like blog posts, whitepapers, and guides. These visuals help readers understand ideas and break up long text. For more guidance on improving long form content, review this resource on SEO content strategy.

Common uses:

• Simple illustrations that explain a concept
• Diagrams and flowcharts for processes
• Header images for blog posts and pages
• Metaphorical visuals (e.g., a plant growing to show business growth)

Best practices:

• Keep it simple and clear, readers should understand the image at a glance.
• Match the style to your brand (formal, friendly, technical).
• Add captions and alt text for accessibility and SEO.

You can also reference this tutorial on responsive web design to improve formatting.

File tips:

• Export high-resolution PNG or JPEG for web use.
• Save a vector or layered file (if possible) for future edits.

3. Build Social Media Graphics

Social media needs lots of visuals. AI lets you create templates and many post variations fast so you can test what works. For stronger results, review these insights on social media trends.

What to create:

• Post designs (quotes, announcements, tips)
• Backgrounds for stories and short videos
• Ad image variations for paid campaigns
• Campaign-specific visuals with consistent branding

How to test:

• Generate 3–6 variations of the same message with different colors or photos.
• Run a small test ad or post them organically and track clicks, likes, and shares.
• Keep the best-performing designs and refine them.

Example prompt:

• “Create four Instagram post templates for a summer sale, including one with a large product photo and one focused on the discount percentage.”

For teams that want to improve performance measurement, this guide on social media conversions and KPIs is helpful.

4. Create Product Mockups

If you sell physical products, AI can create quick mockups. These let you test ideas before investing in photos or manufacturing.

Types of mockups:

• Packaging concepts (box, label, bottle)
• Lifestyle images (products in a home or office)
• Product variants (color or material changes)
• Seasonal themes (holiday packaging or limited editions)

How to use mockups:

• Show them to stakeholders to get fast feedback.
• Use the best mockups for ads or landing pages while you plan a photoshoot.
• Confirm details like scale and color with a designer before production.

Prompt example:

• “Generate a mockup of a coffee bag in a bright kitchen setting with a natural brown label.”

Mockups can be useful when combined with landing page best practices like those shown in this article on high converting landing pages.

5. Support A/B Testing

AI makes it fast to create image variations for A/B testing. Testing helps you learn what visuals actually drive clicks or conversions. For a full breakdown of testing concepts, explore this guide on marketing funnel stages.

Simple A/B test plan:

1. Pick one element to change (color, headline placement, photo vs. illustration).
2. Use AI to create 2–4 versions that each change only that element.
3. Run a small campaign or post and measure performance (CTR, conversions, time on page).
4. Keep the winner and iterate.

What to test:

• Image vs. illustration
• Close-up product images vs. lifestyle scenes
• Dark background vs. light background
• Human faces vs. abstract shapes

You can also reference this walkthrough on pay per click optimization if you plan to test ad creatives.

Measurement tips:

• Test during similar times and audiences to reduce bias.
• Aim for clear differences in metrics before declaring a winner.

AI Marketing Visuals: Workflow Checklist

Use this checklist to make AI work well with your team’s design process:

• Define the goal: What is the image for? (ad, blog, mockup)
• Create a clear prompt: Include style, colors, aspect ratio, and audience.
• Generate multiple options: Ask for at least 3–6 variations.
• Screen for problems: Check for strange details, biases, or errors.
• Edit and brand: Have a designer refine the chosen images.
• Optimize for web: Resize, compress, and add alt text before publishing.
• Test and measure: Run A/B tests and track results.

What makes a good AI prompt? A good prompt is clear and specific. Include the style (e.g., “flat design”), colors (e.g., “use brand blue”), subject (e.g., “laptop on desk”), and format (e.g., “1200 x 628”). Adding mood words (e.g., “friendly,” “professional”) helps too.

Do I need a designer if I use AI? Yes. AI is best for ideas and drafts. Designers make sure visuals match brand rules, scale correctly, and meet quality standards.

Are AI images safe to use for ads? Often yes, but review them first. Avoid images that resemble real people or copyrighted characters. Check for odd details or errors and confirm they meet legal rules for your industry.

What file formats should I use? For most web use, PNG or JPEG works well. Use SVG for simple illustrations or logos when possible. Keep a high-resolution master file for future edits.

How do I avoid bias or mistakes in AI images? Carefully review outputs for stereotypes or inaccuracies. Edit them or regenerate with clearer prompts. Prioritize diversity and realistic representation when needed.

Can AI replace photoshoots? Not completely. AI mockups and concept images are great for early stages or quick needs. For product accuracy, brand authenticity, and high-value assets, real photos are often better.

• Use AI for speed and volume, not final polish.
• Always review images for brand fit and accuracy.
• Keep a simple approval step before publishing.
• Track results so you know which visuals perform best.

For more optimization support, this guide on website user experience provides helpful direction.

AI Marketing Visuals: Opportunities and Risks

AI can help teams produce more visuals faster than ever. But it also brings safety, legal, and quality challenges. Below we break down where AI marketing visuals help most, where they fall short, and exactly what teams should do to protect their brand and their customers. For more insight into content creation limits, this article on AI model comparison provides useful context.

Where AI Helps With Marketing Visuals

AI image generation is best when it speeds up idea work and creates many options quickly. Teams that combine AI and human design get the best results.

Main benefits:

• Fast volume: Generate many image options in minutes.
• Idea exploration: Try different styles, colors, and moods without a big time investment.
• Cost savings: Create draft assets without paying for every concept from a designer or photographer.
• Quick tests: Produce multiple ad or social variations to test performance.
• Prototyping: Mock up packaging or product concepts before a photoshoot.

For teams exploring design trends, this guide on modern website design can provide inspiration that pairs well with AI concepts.

How teams usually use it:

• Marketing creates many AI drafts.
• Designers pick the best options and refine color, type, and composition.
• Legal or compliance reviews sensitive images (if needed).
• Final assets are edited, optimized for web, and published.

Why the hybrid approach works:

• AI gives breadth (lots of ideas).
• Humans give depth (brand sense, legal judgment, and polish).
• The mix saves time and keeps quality high.

Where AI Falls Short

AI is powerful but imperfect. Below are the main problems you will see and how they show up. Review this article on website security best practices to understand how technical risks can overlap with visual risks.

1. AI Should Not Replace Final Design Work

AI can mimic styles but it does not truly understand your brand’s rules or goals. Common AI mistakes include:

• Off-brand colors that don’t match your palette.
• Incorrect proportions (people or products look oddly sized).
• Inconsistent styles across images in the same campaign.
• Images that resemble copyrighted work or feel too “stocky.”

Performance issues can affect SEO too. This resource on technical SEO audits can help you identify issues beyond just visuals.

What to do:

• Always have a designer review and adjust AI outputs.
• Use AI drafts only for ideation or low-risk content.

2. Legal and Ethical Concerns

AI models learn from huge image sets. That can create legal risks if outputs copy or too closely resemble protected content.

Watch out for images that:

• Look like real people or celebrities.
• Mimic a living artist’s signature style.
• Use recognizable logos, characters, or branded elements.
• Recreate famous artwork or trademarked designs.

How to reduce risk:

• Avoid prompts asking to “copy” a specific artist or brand style.
• Use internal legal review for ads, product labels, or anything commercial.
• Keep records of prompts and tool versions in case you need to show provenance.

3. Output Errors and Inaccuracies

AI sometimes makes weird mistakes. These mistakes can harm credibility and user trust.

Common errors:

• Wrong number of fingers, misplaced eyes, or odd facial features.
• Objects floating, incorrect shadows, or strange reflections.
• Inaccurate props (e.g., wrong tools for a profession).
• Unintended stereotypes or biased portrayals.

For those working in legal marketing, this guide on law firm marketing offers helpful direction.

How to catch them:

• Never publish an AI image without a careful visual check.
• Have at least one team member who did not generate the image review it.

4. Misleading Images in Regulated Industries

If your business is in healthcare, finance, legal services, or another regulated field, a wrong image can be risky.

Potential problems:

• Images that imply real patient outcomes or guaranteed results.
• Visuals that misrepresent a service or product capability.
• Graphics that contradict compliance rules or industry guidance.

Best practices:

• Use factual, realistic visuals only.
• Run compliance reviews for any ad, landing page, or brochure that makes claims.
• Avoid images that could be read as medical advice, legal promises, or financial guarantees.

For more structured planning, review this framework on strategic planning best practices.

Practical Risk-Reduction Checklist

Before publishing any AI-generated visual, run through this checklist:

• Goal check: Does the image match the campaign goal?
• Brand check: Colors, fonts, and logo placement follow brand rules?
• Accuracy check: Are people, props, and settings realistic and correct?
• Legal check: Could this image infringe on a trademark, a public figure, or an artist’s style?
• Bias check: Is representation fair and inclusive?
• Technical check: Resolution, aspect ratio, and file format are correct?
• Accessibility check: Alt text is ready and descriptive?
• Documentation: Save the prompt, tool name, and version used.

If any item fails, pause and fix it before publishing.

How to Build Safe AI Policies for Your Team

Create simple rules so everyone knows how to use AI safely:

• Approved tools list: Only allow vetted AI services.
• Prompt rules: No requests to mimic living artists or use trademarked characters.
• Review steps: Designer and legal (if needed) must sign off on final images.
• Storage rules: Save prompts, exports, and approval notes for auditing.
• Training: Teach staff how to spot AI errors and bias.

A short policy document (one page) goes a long way. Make it part of onboarding for new hires.

Can I use AI images for paid ads? Yes, but only after careful review. Ads often reach many people, so legal and quality checks are more important for ads than for internal drafts.

How do I know if an image copies someone else’s work? If the image includes a famous character, logo, or a distinct artistic style, avoid it. When unsure, reword prompts and run the image by legal.

Will AI always produce biased images? Not always. But bias can appear. Always check for stereotypes, underrepresentation, or offensive content before publishing.

How much should designers edit AI images? Usually some editing is needed: color correction, type placement, compositional fixes, and quality cleanup. Plan for 10–60 minutes of editing per image depending on complexity.

Should we document every prompt?: Yes. Keep a simple log with the prompt, date, tool used, and who approved the final image. This helps with accountability and any future legal questions.

AI marketing visuals can speed up creative work and open new possibilities. But the smart path is to pair AI with human review, clear policies, and a simple checklist. That way you get the speed without sacrificing brand trust, legal safety, or quality. 

How to Use AI Image Generation the Right Way

To get the best results from AI, follow these best practices.

1. Start With a Clear Goal

Before using AI, marketers should decide:

• What the image is for
• Who the audience is
• What message the image supports
• Which brand colors or styles must be included

The clearer the direction, the better the AI output.

2. Always Follow Brand Guidelines

AI cannot automatically understand your brand voice or design rules. After generating images, marketers should check:

• Colors
• Fonts
• Spacing
• Visual consistency

Minor edits can turn an AI image into something that fits perfectly within your brand.

3. Use AI for Ideas, Not Final Results

AI is strongest in the early stages of creative work. It is best used for:

• Brainstorming
• Producing variations
• Exploring visual themes

Human designers should finalize important images, such as homepage banners, ad creatives, and printed materials.

4. Add AI to Your Workflow, Not in Place of It

AI is most helpful when used as a tool, not a replacement. It can support:

• Ideation
• Content scaling
• Prototyping
• Testing

But final approval should always come from a marketer or designer.

5. Review Every Image Carefully

Before using any AI-generated image, marketers should check:

• Accuracy
• Quality
• Brand fit
• Potential legal issues

A quick review step can prevent costly mistakes.

The Future of AI Image Generation in Marketing

AI tools continue to grow more advanced each year. They are becoming better at creating realistic photos, editing details, and producing more accurate visuals. As this technology improves, marketers will have more opportunities to speed up their workflows and test new ideas. You can explore trend forecasting in this article on digital marketing trends.

However, human creativity and judgment will always be needed. The best results come from combining AI’s speed with a marketer’s understanding of the brand, the audience, and the campaign goals. This guide explains how SEO works.

Using AI Image Generation to Strengthen Your Marketing

AI image generation can help marketers produce content faster, experiment with new styles, and support creative work. When used wisely, it can improve your visual strategy without replacing the important role of human designers. With the right balance, AI marketing visuals can help your brand stay competitive, flexible, and consistent.

If your business wants help using AI tools or improving your overall digital marketing strategy, THAT Agency is here to guide you. Contact us today to learn how we can support your visual and digital goals.