Creating Illustrations Using AI Software: A Practical Guide for Artists and Designers
When I first experimented with AI illustration tools back in late 2022, I was skeptical. As someone who spent years learning traditional illustration techniques and mastering Adobe Creative Suite, the idea of typing words and watching a machine generate artwork felt almost offensive. Fast forward to today, and my perspective has shifted considerably—not because AI replaced my skills, but because I discovered how these tools could amplify what I already do.
This isn’t a breathless endorsement of every AI art generator on the market. Instead, it’s a grounded look at how professionals and hobbyists alike can actually use this technology, where it genuinely helps, where it falls short, and how to integrate it thoughtfully into creative workflows.
Understanding What AI Illustration Software Actually Does
Before diving into the practical stuff, it helps to understand what happens behind the scenes. AI illustration software analyzes vast collections of images and learns patterns—shapes, colors, styles, compositions, and relationships between visual elements. When you provide input, the software generates new pictures based on those learned patterns.
Think of it like this: if you asked a well-traveled artist to paint a “cozy cabin in Norwegian mountains during sunset,” they’d draw from memories, references they’ve seen, and their understanding of light, architecture, and landscape. AI illustration tools work similarly, except their “memory” spans millions of images and can synthesize combinations human artists might never consider.
This distinction matters because understanding it shapes expectations. You’re not commanding a perfectly obedient drawing machine. You’re collaborating with a system that interprets and creates based on probability and pattern recognition.
Popular AI Illustration Tools Worth Knowing
The landscape of AI illustration software has evolved rapidly. Here’s an honest breakdown of the major players based on my hands-on experience:
Midjourney
This remains my go-to for stylized, artistic illustrations. Midjourney excels at creating evocative, often painterly images that carry genuine artistic sensibility. The Discord-based interface threw me off initially, but I’ve grown to appreciate how the community Aspect sparks ideas.
Where it shines: concept art, fantasy illustrations, architectural visualizations, and mood boards.
Where it struggles: precise text rendering, exact specifications, and photorealistic human hands (though version 6 has improved dramatically).
DALL-E 3
OpenAI’s offering integrates directly with ChatGPT, making it incredibly accessible. The natural language understanding is genuinely impressive—you can write conversational descriptions rather than learning specific syntax.
DALL-E 3 is particularly useful for commercial projects that require quick concept iterations. The style tends toward cleaner, more commercial aesthetics compared to Midjourney’s artistic interpretations.
Adobe Firefly
For designers already embedded in the Adobe ecosystem, Firefly feels like a natural extension. The integration with Photoshop and Illustrator means generated elements can flow directly into existing projects. Adobe’s emphasis on commercially safe training data also addresses some licensing concerns that worry professional designers.
Stable Diffusion
This open-source option offers unmatched flexibility for technically inclined users. Running it locally means more control, no subscription costs after setup, and the ability to train custom models on specific styles. The learning curve is steeper, but the customization potential is enormous.
Leonardo.AI
A platform I initially overlooked but now recommend regularly, especially for game assets and character design. The consistency features help maintain character appearance across multiple images—something that frustrated me endlessly with earlier tools.
Getting Started: Your First Illustrations
Let’s get practical. Here’s how I approach creating illustrations with AI software, broken into manageable steps.
Step 1: Define Your Purpose
This sounds obvious, but it saves enormous time. Are you:
- Creating finished artwork for publication?
- Generating reference images for traditional work?
- Building mood boards for client presentations?
- Designing elements to incorporate into larger compositions?
Your purpose determines which tool you’ll use, how much refinement you’ll need, and whether AI-generated imagery works as final output or as part of your process.
Step 2: Gather References and Inspiration
Even with AI assistance, traditional reference gathering matters. Before I type anything, I usually collect:
- Examples of styles I want to emulate
- Color palettes that fit the mood
- Compositional references showing the layout I’m imagining
This preparation dramatically improves results. Walking in blind typically produces generic output.
Step 3: Write Effective Descriptions
The text you provide—often called prompting—significantly impacts output quality. After generating thousands of images, here’s what I’ve learned works:
Be specific about style. Rather than “make it look nice,” try “oil painting style with visible brushstrokes, warm color temperature, dramatic lighting from upper left.”
Include mood and atmosphere. Terms like “melancholic,” “energetic,” “serene,” or “ominous” meaningfully influence results.
Reference artistic movements or creators. Phrases like “in the style of Art Nouveau” or “reminiscent of Studio Ghibli backgrounds” provide clear direction. Some platforms handle artist references differently for ethical reasons; check the guidelines.
Specify technical details. Aspect ratios, color schemes, time of day, and weather conditions—these concrete details reduce ambiguity.
Describe what you don’t want. Many platforms support negative specifications. Excluding elements such as “no text,” “no watermarks,” or “avoid dark shadows” helps refine the output.
Step 4: Iterate and Refine
Your first generation rarely nails it. Treat initial outputs as starting points. I typically:
- Generate multiple variations from the same description
- Identify which elements work across versions
- Refine my description based on what the tool misunderstood
- Use upscaling and enhancement features for promising candidates
- Repeat until something clicks
Most platforms allow variations from existing images, remixing elements you like while changing others. This iterative process mirrors traditional creative work—rough sketches before finished pieces.
Step 5: Post-Processing
AI-generated illustrations almost always benefit from post-work. Common adjustments include:
- Color correction and grading
- Adding or refining details that the AI missed
- Compositing multiple generations together
- Removing artifacts or inconsistencies
- Adjusting composition through cropping
- Adding text or graphic elements manually
Photoshop, Procreate, or similar tools bridge the gap between AI output and polished final work.
Real-World Applications: Where AI Illustration Actually Helps
Let me share some scenarios where I’ve found genuine value:
Book and Editorial Illustration
A children’s book author I collaborated with needed 32 illustrations on a tight budget and timeline. Traditional commissioning wasn’t feasible. We used AI to generate initial concepts, refined favorites through iteration, then I painted over the most promising ones in Procreate to add consistency and detail the AI couldn’t capture.
Total production time dropped by roughly 60%, and the final illustrations maintained the warmth and intentionality that pure AI output often lacks.
Marketing and Advertising
Quick-turnaround social media graphics are now far more manageable. When a client needs Instagram carousel concepts by tomorrow morning, AI tools provide starting points that would have previously required stock photography or quick sketches.
One campaign for a local coffee shop needed “cozy autumn morning” imagery. Twenty minutes with Midjourney produced options that informed the entire visual direction, which our team then developed with photography and graphic design.
Game Development
Indie game developers especially benefit from AI illustration. Creating concept art for characters, environments, and assets traditionally required either significant artistic skill or a budget for freelance artists. AI tools democratize this early conceptual phase.
A friend developing a roguelike game generated dozens of enemy concepts, selected favorites, then commissioned a traditional artist to create final sprite sheets, maintaining consistent style. The AI phase clarified vision before spending significant money on final assets.
Architectural and Interior Visualization
Before building presentations for clients, interior designers I know use AI to quickly visualize concepts—”mid-century modern living room with teal accents and natural wood,” “minimalist Scandinavian kitchen with brass fixtures.” These visualizations spark conversations and align expectations before detailed planning begins.
Personal Creative Projects
Beyond commercial work, AI illustration tools have rekindled creative experimentation for many artists I know. The low barrier to visualizing ideas encourages exploration that traditional methods might discourage due to their time investment.
I’ve personally used AI-generated landscapes as a reference for oil paintings, resulting in unusual compositions and lighting scenarios I wouldn’t have imagined otherwise.
Workflow Integration: Making It Practical
The question isn’t whether to use AI illustration tools—it’s how to integrate them effectively. Here’s what works in practice:
Hybrid Approaches Beat Pure AI
The strongest results I’ve seen combine AI generation with human refinement. Consider:
- AI-generated backgrounds with hand-drawn characters
- AI concept exploration followed by traditional execution
- AI texture generation applied to manual compositions
- AI color studies informing traditional palette decisions
This hybrid model preserves human intentionality while leveraging AI’s rapid iteration capabilities.
Maintain Organized Archives
AI tools generate volumes of images quickly. Without organization, you’ll lose track of promising work. I maintain project folders, save my descriptions alongside images, and flag favorites for easy retrieval.
Develop Style Consistency Systems
For ongoing projects requiring consistent visuals, document what works. Keep records of successful descriptions, seed numbers (if your platform uses them), and settings that produce desired styles. This prevents reinventing the wheel for each session.
Set Realistic Time Expectations
AI illustration is faster than traditional methods for many tasks, but “faster” doesn’t mean “instant.” Expect:
- 5-15 minutes for simple concept generations
- 30-60 minutes for refined, iterated illustrations
- Several hours for complex projects requiring multiple elements
- Additional time for post-processing and integration
Limitations You’ll Encounter
Honest assessment of limitations prevents frustration:
Consistency Challenges
Getting the same character to look identical across multiple images remains difficult. While platforms have improved, maintaining consistent facial features, clothing details, and proportions across a series requires workarounds—such as reference images, seed locking, or post-generation editing.
Fine Control Issues
Precise specifications often don’t translate. Asking for “exactly five birds flying in V formation” might yield three, seven, or a vaguely bird-shaped cloud. Complex spatial relationships and exact quantities remain challenging.
Text and Typography
Despite improvements, AI-generated text in images frequently contains errors. Plan to add text manually in post-processing rather than relying on AI for legible typography.
Cultural and Contextual Gaps
AI tools reflect patterns in their training data. Requests for imagery from underrepresented cultures, historical periods, or specific regional aesthetics may produce stereotyped or inaccurate results. Careful review and cultural sensitivity remain essential.
Resolution and Detail Limits
While upscaling has improved, AI illustrations may lack the fine detail of high-resolution traditional digital artwork. For large-format printing or close examination, post-processing enhancement is often necessary.
Ethical Considerations Worth Thinking About
The rise of AI illustration has generated legitimate controversy that deserves thoughtful engagement rather than dismissal.
Training Data and Artist Consent
Most AI models are trained on images online, often without explicit permission from the creators. This raises valid concerns about whether generating images “in the style of” specific living artists respects their work and livelihoods.
My personal approach: I avoid direct references to living artists, use commercially licensed platforms like Adobe Firefly when client work requires clear provenance, and remain transparent with clients about AI involvement in projects.
Disclosure and Honesty
When AI significantly contributes to illustrations, transparency matters. Clients deserve to know what they’re paying for. Contest submissions should follow guidelines about AI use. Publications often have policies worth respecting.
The illustrator community has been understandably worried about AI’s implications. Regardless of where you land on these debates, engaging thoughtfully with concerns rather than dismissing them builds trust.
Economic Impact
AI illustration tools do affect the market for certain types of work. Quick concept sketches, stock illustrations, and simple graphic elements are facing competition from AI-generated content. This doesn’t mean traditional illustration is dying—bespoke, high-quality, intentional artwork retains value—but acknowledging industry changes helps navigate them honestly.
Tips From Extended Practice
After significant time with these tools, some lessons stand out:
Embrace imperfection. The slightly unexpected results often spark better ideas than the exact ones you requested. Stay open to happy accidents.
Study what works. When a generation impresses you, analyze why. What in your description contributed? What style elements emerged? This reverse-engineering improves future work.
Combine approaches. Use one platform’s strength for specific elements, another’s for different needs. I regularly generate landscapes in Midjourney, characters in Leonardo, and composites in Photoshop.
Develop personal description libraries. Phrases that work well for your preferred styles become reusable assets. Document them.
Stay current. These platforms update frequently. Features that frustrated you six months ago may have improved. Check release notes and community discussions.
Practice critical evaluation. AI output can be impressive at first glance, but reveals issues upon examination—anatomical problems, logical inconsistencies, weird artifacts. Train your eye to catch these quickly.
Looking Forward
The AI illustration landscape continues evolving rapidly. Features such as improved consistency, video generation, 3D asset creation, and enhanced control mechanisms are advancing rapidly.
What seems clear is that these tools aren’t going away. They’re becoming more capable and more integrated into the creative software we already use. The professionals thriving with AI illustration aren’t those who resist all change or those who abandon traditional skills—they’re the ones developing thoughtful hybrid approaches that leverage AI capabilities while maintaining human creativity, intentionality, and artistic judgment.
For illustrators, designers, and creative professionals, the practical path forward involves honest experimentation, clear ethical frameworks, and a willingness to adapt workflows as these tools mature. The technology is impressive, but it remains a tool—the artistry still comes from you.
Troubleshooting the “Uncanny Valley”: When Logic Breaks
Even with the best prompts and the most advanced models, you will inevitably encounter the “logic gap.” This is where the software understands the texture of reality but fails to grasp the physics of it. I’ve generated beautiful architectural renderings in which staircases lead directly into solid ceilings, or bicycles in which the chain fuses into the wheel frame. The AI knows what a bike looks like, but it doesn’t understand how a motorcycle works.
To create illustrations using AI software successfully, you must become a master of “photobashing”—a technique borrowed from concept art well before AI existed. Photobashing involves combining different photographic elements to create a new whole.
When I get a generation that is 90% perfect but has a nonsensical background or a distorted limb, I don’t throw it away. I generate a second image specifically to fix the error. For example, if the hand is wrong, I will prompt specifically for “hand holding a coffee cup, close up, high detail” until I get a good hand. Then, I bring both images into Photoshop, layer them, and mask the good hand onto the original body. This hybrid approach—stitching together the best parts of multiple generations—is the secret to creating complex scenes that actually make sense to the human eye.
The Typography Problem
One specific area where beginners struggle is text. While engines like DALL-E 3 have improved at rendering text, they are still unreliable for professional layout. If you try to force the AI to create illustrations using AI software that includes the headline and body copy, you are setting yourself up for frustration. The kerning will be off, the spelling will be hallucinatory, and the font choice will be generic.
My advice? Keep the disciplines separate. Let the AI handle the imagery and the atmosphere. Let a vector tool (like Illustrator or InDesign) handle the typography.
However, you can use AI to assist with type integration. I often use AI to generate “text containers”—ribbons, vintage signboards, or neon tubing shapes—left blank. This gives me a naturally lit, textured surface within the illustration where I can overlay my own vector text in post-production. This makes the text feel like it is truly sitting in the scene, affected by the environment’s lighting, without relying on the AI to be a typesetter.
Navigating Client Expectations and Pricing
The elephant in the room for freelancers and agencies is how to value this work. There is a dangerous assumption among clients that because you use AI, the work should be instantaneous and cheap. “You just typed a prompt, right? Why is this invoice $2,000?”
You must educate your clients on the process. When I create illustrations using AI software, I am not charging for the 30 seconds of generation time. I am charging for:
- The Curation: The expertise to reject the 49 bad options and select the one that fits the brand strategy.
- The Cleanup: The hours spent in Photoshop fixing the lighting, correcting anatomy, and colour-grading.
- The Legal Safety: The knowledge of which tools are safe for commercial use and how to ensure the final asset is distinct enough to be defensible.
- The Hardware: Running local models (like Stable Diffusion XL) requires powerful, expensive GPUs.
I have found it helpful to stop listing “AI Generation” as a line item on invoices. Instead, I list “Concept Development,” “Digital Compositing,” and “Art Direction.” The tool is irrelevant to the billing; the result is what matters. If a carpenter uses a power drill instead of a hand screwdriver, they don’t charge you less for the cabinet—they just finish it faster and with more precision.
The Human “glitch” as a Style
Finally, as we reach a saturation point where perfectly polished, hyper-real AI images are everywhere, I am noticing a fascinating trend: the embrace of the glitch. Just as vinyl crackle became a desirable aesthetic in the age of digital audio, the specific imperfections of AI are becoming an aesthetic choice.
Some of the most interesting work I am seeing right now doesn’t try to hide the AI; it leans into it. Artists are intentionally using lower settings or older models to create surreal, dreamlike distortions that feel alien and new. They are using the software not to mimic photography, but to explore the latent space of the machine’s “mind.”
To create illustrations using AI software is to collaborate with a non-human intelligence. Sometimes, the most creative move is to stop fighting the machine’s weirdness and let it take the wheel for a moment. It is in those weird, unplanned accidents—the “happy accidents,” as Bob Ross would say—that you often find a visual language that feels entirely fresh.
We are still in the Wild West of this technology. The maps are being drawn as we walk. But if you keep your artistic fundamentals strong and remain adaptable, you will find that AI is not an adversary, but the most potent creative assistant you have ever hired.