How to Make AI Images?

Why AI Image Generation Is Now a Business Skill

Five years ago, generating a professional image meant hiring a designer or buying stock photos. Today, Adobe Firefly alone has produced 24 billion assets in under two years, and AI image generation has quietly become part of the standard business toolkit — not a novelty, but infrastructure.

Yet for most professionals, the landscape feels more confusing than empowering. Dozens of capable tools compete for attention, headline statistics contradict each other, and the risks around copyright and consumer trust are real. The hard part is no longer generating an image. It's knowing which tool to use, how to prompt it, and when AI imagery is safe to publish.

Consider the scale of the confusion. Popular figures like "15 billion AI images created" trace back to a dated 2023 estimate, and several "updated" 2025 statistics circulating online have been exposed as fabricated attributions. Meanwhile, market-size forecasts range wildly depending on methodology. Anyone making tool or budget decisions based on these numbers is building on sand.

The tool landscape adds another layer of complexity. Hands-on testing by Zapier found that the top 50 or so models are all excellent — meaning raw quality is no longer the differentiator. Instead, each tool has distinct strengths that matter for specific business tasks:

• ChatGPT for general-purpose generation and Google's Nano Banana for editing existing images
• Midjourney for artistic, textured visuals — though its images are public by default and the company faces an ongoing Disney and Universal lawsuit
• Ideogram for rendering readable text inside images
• Adobe Firefly for brand-safe commercial work, since it trains on licensed content and explicitly guarantees safe commercial use

This is why 60% of creators now use more than one generative AI tool, cycling between platforms to match capabilities to tasks. Multi-tool fluency, not loyalty to a single product, has become the practical norm.

Then there's the trust problem. Peer-reviewed research found that customers avoid services advertised with AI-generated images, perceiving the companies behind them as less professional and potentially misleading. And the legal picture remains unsettled: courts are still working through copyright questions, with the UK High Court recently rejecting Getty's core claim against Stability AI while leaving broader questions open.

The skill that separates professionals from dabblers, then, isn't the ability to type a prompt. It's judgment — matching tools to use cases, writing prompts that survive each platform's quirks, compositing generated elements in design tools, and knowing when real photography should stay in the frame. Once that workflow is running, agent skills can automate the downstream steps like batch processing and publishing; SkillzBench is building a free directory and MCP connector to help you find and verify skills worth trusting. The rest of this guide walks through exactly how to build that judgment, step by step.

Match the Tool to the Task (Not to the Hype)

The best question in AI image generation is no longer "which tool is best?" — it's "which tool is best for this task?" That shift happened because quality stopped being the differentiator.

According to Zapier's hands-on testing, "the top 50 or so models are all excellent, so other features and usability matter more than they used to." When every leading generator produces impressive output, your decision should rest on the specific job in front of you — not on which tool has the loudest hype cycle.

Match the tool to the job

Here's how the strengths break down, based on Zapier's use-case testing:

• ChatGPT (GPT Image 2) — best overall for general-purpose generation; free tier available, Plus at $20/mo for more headroom.
• Nano Banana (Gemini 3.1 Flash Image) — the standout for editing existing images; limited free use, paid tiers from $4.99/mo.
• Midjourney — the pick for artistic results, with "great textures and colors"; from $10/mo for roughly 200 images.
• Ideogram — the leader for rendering legible text inside images; from $20/mo for 1,000 priority credits.
• FLUX and Adobe Firefly — FLUX for deep customization and control (pricing varies by platform); Firefly from $9.99/mo for brand-safe commercial work.

Expect to use more than one

This isn't just theory. Industry data shows that 60% of creators used more than one generative AI tool in the past three months — cycling between platforms to match capabilities to tasks, because no single tool covers the full creative workflow. Notably, the most common professional use case is editing and enhancement (55%), which explains why an editing-focused tool like Nano Banana belongs in the mix alongside a generator.

The business-use caveat

Midjourney's artistic results come with a warning label for business users. Its images are public by default, and the platform remains entangled in ongoing lawsuits — including one from Disney and Universal — while copyright law for AI images stays unsettled. Adobe Firefly is the exception: it's trained on licensed content and explicitly guarantees safe commercial use, which is why it's the default recommendation for brand-facing work.

The same principle applies to your broader AI stack: tools should earn their place through tested performance on your specific tasks, not marketing claims. That's the philosophy behind SkillzBench's approach to agent skills — verify, benchmark, then equip — and it's a discipline worth applying to every AI tool you adopt.

Prompting and Post-Editing: Where the Real Skill Lives

Here's the counterintuitive truth about AI images: the generation button is the easy part. The most common professional use case is now editing, upscaling, and enhancement at 55%, ahead of generating new assets at 52% — proof that the real skill lives in what happens before and after the prompt.

Prompt structure matters more than most beginners realize. Practitioner guidance from Journalism.co.uk's platform-by-platform playbook shows that each tool responds differently:

• Front-load your prompts in Canva's Magic Media, which may ignore instructions placed later in the prompt.
• Use negative prompts in Stable Diffusion — phrases like "no extra fingers, no text" tell the model what to avoid.
• Be direct and explicit with DALL-E: spell out numbers, roles, and actions, then edit in stages rather than expecting one perfect output.
• Break complex multi-person scenes into separate generations of individuals or pairs, then composite them in a design tool — a standard professional workflow, especially in Midjourney, which struggles with realistic faces and literal accuracy.

That last technique deserves emphasis. Trying to force five people interacting in one prompt usually produces wrong group counts, merged figures, or anatomy errors. Generating each element separately and assembling in design tools gives you control that no prompt can match.

Bias is a structural problem, not a prompt problem. In one firsthand test, even explicit diversity prompts produced mostly Caucasian subjects, wrong-facing figures, incorrect group counts, and figures without eyes. All major tools reflect training bias, so specify diversity deliberately and review every output critically before it reaches an audience.

This matters commercially. A peer-reviewed study found customers avoid services advertised with AI-generated images, so refined, carefully reviewed visuals aren't vanity — they're risk management.

The takeaway: treat generation as a draft, not a deliverable. Refine in stages, composite deliberately, and upscale the winners. And once your images are ready, agent skills can automate the rest of the pipeline — batch processing, asset management, publishing. SkillzBench is building a free directory where you can browse and verify agent skills for exactly these workflows; join the waitlist to be first in line when it launches.

Using AI Images Commercially Without Getting Burned

The most expensive mistake you can make with AI images isn't a bad prompt — it's using them where customers or the law will punish you. Generating an image takes seconds; rebuilding trust after a backlash takes far longer.

Start with the trust problem. A peer-reviewed study published in Information & Management found that customers actively avoid services advertised with AI-generated images, perceiving the companies behind them as impersonal, less professional, and potentially misleading. The rejection effect was strongest for hedonic services and highly involved customers — exactly the high-value bookings most businesses care about.

The practical takeaway: reserve AI imagery for conceptual and illustrative work — blog headers, abstract backgrounds, mockups, ideation boards — and use real photography anywhere trust is on the line, like your homepage hero, testimonials, or service pages. The researchers themselves conclude companies "should use AI-generated images with caution" and choose strategically between real and generated visuals depending on the service and customer involvement.

Then there's the legal layer, which remains genuinely unsettled. In November 2025, the UK High Court rejected Getty's core copyright claim against Stability AI, finding that model weights don't store underlying images — but that ruling is jurisdiction-specific and doesn't close the book. Midjourney, meanwhile, faces lawsuits from both Getty and Disney/Universal, and its images are public by default, which Zapier's hands-on review flags as a potential dealbreaker for business use.

A few ground rules will keep you out of trouble:

• Prefer tools trained on licensed content for commercial work. Adobe Firefly is the standout exception here — trained on licensed material, it explicitly guarantees safe commercial use.
• Check each tool's terms before using outputs in paid campaigns; commercial rights and privacy defaults vary widely between platforms.
• Label AI-generated visuals clearly with captions, watermarks, or metadata — transparency is both an ethical obligation and, increasingly, an expectation.
• Review outputs critically for bias and errors; practitioner testing shows even explicit diversity prompts often produce skewed results, wrong group counts, and distorted figures.

The stakes are higher than they look because detection is nearly impossible for audiences. Only 0.1% of participants in one iProov study could reliably distinguish real from AI-generated content, which cuts both ways: your audience can't tell, but neither can regulators, journalists, or competitors scrutinizing your brand.

Once you've settled on a compliant workflow, the surrounding work — batch processing, asset pipelines, publishing — can be automated with agent skills. SkillzBench is building a free directory where you can browse, verify, and benchmark those skills, with a free MCP connector to wire them into your agents; join the waitlist to get access at launch.

Build Your AI Image Workflow (and Automate the Rest)

A tool stack plus a repeatable process turns AI image generation from party trick into production infrastructure. Adobe Firefly alone has produced 24 billion assets in under two years, and 60% of creators now use more than one generative AI tool — so the question isn't which tool is "best," but which combination fits your workflow.

Start with a simple stack: one primary generator plus one editing tool. Zapier's hands-on testing found the top 50 or so models are all excellent, so choose by strength rather than raw quality — ChatGPT for general use, Nano Banana for editing existing images, Midjourney for artistic visuals, Ideogram for text rendering, and Adobe Firefly for brand-safe commercial work.

Then apply the prompting playbook. Be explicit and front-load your instructions (Canva's Magic Media may ignore later ones), use negative prompts in Stable Diffusion ("no extra fingers, no text"), and break complex multi-person scenes into individual generations you composite in a design tool — a standard professional workflow. Expect bias: even explicit diversity prompts produced mostly Caucasian subjects and wrong group counts in one firsthand test, so specify diversity and review every output critically.

Before any image ships commercially, verify your rights. Copyright law for AI images remains unsettled — the UK High Court rejected Getty's core claim against Stability AI in November 2025, but lawsuits involving Midjourney and Stable Diffusion continue, and Midjourney's images are public by default. Practitioner guidance singles out Adobe Firefly as the exception that explicitly guarantees safe commercial use.

Your pre-publish checklist:

• Pick a primary generator matched to your main use case, plus one editing tool for refinement.
• Apply platform-specific prompting: explicit, front-loaded, staged, with negative prompts where supported.
• Confirm commercial rights before use — prefer licensed-content tools for business assets.
• Label AI-generated images clearly via captions, watermarks, or metadata.
• Reserve AI imagery for conceptual or illustrative work; peer-reviewed research found customers avoid services advertised with AI images, perceiving them as less trustworthy.

Once the images themselves are handled, the surrounding work — batch processing, resizing, naming, asset pipelines, publishing — is exactly what agent skills automate. That's why we're building SkillzBench: a free directory where you can explore and vet agent skills with security scanning and benchmarked Lift scores, plus a free MCP connector to wire chosen skills into any agent. It's pre-launch, so joining the waitlist now puts you first in line when it goes live.

Skills, proven. Browse free agent skills for your image workflow — with security scans and benchmarked Lift — on SkillzBench, and join the waitlist ahead of launch.