AI Design Tools: What Actually Works in Canva, Adobe, and Figma

I was knee-deep in a website redesign last month when my client asked if I'd "tried that AI design stuff yet." The question made me smile—because I'd already used three different AI tools just to create her initial mockups. She had no idea. The tools were invisible, which is exactly how it should be. Good design assistance doesn't announce itself with robotic artifacts. It just makes the work faster, better, and occasionally more surprising.

The Question Everyone Gets Wrong

People ask me which AI design tool is best. It's like asking which brush is best for painting—the answer depends entirely on what you're painting and how you work. Canva's AI thinks differently than Adobe's, which approaches problems differently than Figma's. Understanding these differences is the key to using any of them well.

What unites all these tools is that they've crossed a threshold in the past year. They're no longer tech demos or gimmicks bolted onto existing features. They're genuinely useful capabilities that save real time and expand creative possibilities. But they require understanding when to use them and when to trust your own hands.

Canva: Design for the Rest of Us

I have a complicated relationship with Canva. As someone trained in traditional design, part of me wants to dismiss it as the tool for people who don't know design. But I've watched it democratize visual communication in remarkable ways. The small business owner creating her own Instagram posts, the teacher making engaging classroom materials, the nonprofit coordinator producing event flyers—these aren't jobs that can afford a designer, and Canva's AI makes them dramatically better than they'd be otherwise.

Canva's Magic Design feature feels like having a design intern who's studied every template you've ever used. You feed it your content and tell it what you're making, and it assembles surprisingly coherent starting points. Not groundbreaking design, but competent and on-brand. For someone without design training, that's transformative.

Where Canva's AI Shines

The background remover is genuinely impressive. I've thrown messy product photos at it—images where I expected to do manual refinement—and it nailed the extraction. Same with the Magic Eraser for removing unwanted objects. These aren't features you'd use for high-end retouching, but for social media and web graphics, they're fast and effective enough to replace manual work.

Magic Write generates placeholder copy that's contextually appropriate. It understands that Instagram captions need different pacing than LinkedIn posts. Again, not replacing a copywriter, but useful for roughing in content while you focus on visual composition.

The Limitations Matter

The text-to-image generation produces serviceable images with a distinct "AI stock photo" aesthetic. You know it when you see it—slightly too perfect, compositionally safe, with that telltale smoothness. Fine for internal presentations, questionable for client-facing work where you want visual distinction.

What I appreciate about Canva's approach is that the AI features genuinely match the user base. They're not trying to give non-designers pixel-perfect control—they're giving them good-enough results with minimal decision-making. That's thoughtful product design.

I use Canva for speed projects where the output quality bar is "professional enough" rather than "distinctive." Social media quote graphics, internal slide decks, quick mockups for client feedback loops. The AI features make these faster, and importantly, they don't demand the kind of detailed input that slows you down when you're moving fast.

Adobe Firefly: Professional Power with Safety Rails

Adobe's integration of Firefly into Creative Cloud feels like the tool I've been waiting for. Not because it's the most creative AI—it isn't—but because it solves real problems in my existing workflow without forcing me to learn a new interface or leave the apps I already live in.

Generative Fill in Photoshop is the standout feature. I was working on a product campaign that required hero images in both 16:9 and 4:5 aspect ratios. The source photos were shot for landscape orientation. Normally this means reshooting or aggressive cropping. Instead, I selected the areas that needed extension, described the environment, and Photoshop generated perfect continuations. The lighting matched, the textures were consistent, the results were indistinguishable from the original photography.

This isn't party trick territory anymore. This is "fundamentally changes what's possible in post-production" capability. I've extended backgrounds for banner ads, removed distracting elements from lifestyle shots, even composited new objects into scenes. The quality is high enough for commercial work, which matters when clients are paying for final-ready assets.

The Text to Vector feature in Illustrator is more hit-or-miss. I use it for exploring icon directions and generating starting points, but I've never used a Firefly-generated vector as final output. The results need refinement—anchor points aren't where I'd place them, curves need adjustment, proportions require tweaking. But as a way to quickly visualize an idea before committing to manual illustration, it's useful.

What sets Firefly apart is the commercial licensing story. It's trained exclusively on licensed content—Adobe Stock images, public domain works, content Adobe has rights to. For enterprise clients and risk-averse brands, this matters enormously. I have clients who won't touch Midjourney or Stable Diffusion because of IP concerns. Firefly gives them an AI option they're comfortable with legally.

Figma: AI That Respects Your Workflow

Figma's approach to AI is refreshingly restrained. They're not trying to generate entire designs for you or replace your creative decisions. Instead, they're using AI to eliminate tedious parts of the design process—the organizational tasks, the repetitive formatting, the placeholder content creation.

The Auto Layout suggestions genuinely save time. Figma analyzes your component structure and suggests responsive layout configurations. It's not making aesthetic choices—it's handling the technical implementation of layout patterns you already understand. That's the right division of labor between AI and designer.

Similarly, the AI-powered layer renaming seems minor until you're working on a complex file with hundreds of layers. Figma looks at layer content and suggests descriptive names. Does this fundamentally change design? No. Does it save fifteen minutes of tedious organization on every project? Absolutely.

What I appreciate about Figma's philosophy is that the AI features feel like they're built by people who actually do UI design work. They're solving real friction points, not adding flashy features for marketing purposes. The tool respects your expertise and just tries to make the work faster.

The Specialists: When You Need Specific Capabilities

Beyond the major platforms, there's a growing ecosystem of specialized AI design tools. Each targets a specific use case where general-purpose tools fall short. I keep several of these in my workflow for situations where the mainstream options don't quite work.

How I Actually Work With These Tools

The workflow question matters more than the feature comparison. Here's how AI tools fit into my actual design process, not the idealized version, but what happens on real projects with real deadlines and real client constraints.

Early exploration happens in whatever tool generates options fastest. Usually that's Midjourney for image-heavy projects or Krea for interface concepts. I'm not looking for final-quality output—I'm looking for directions to pursue. Generating fifty variations in an hour lets me see possibilities I wouldn't have considered, and importantly, it lets me rule out directions quickly without investing time in manual execution.

Typical Project Flow

1

Ideation and exploration

Generate wide range of concepts quickly. Tools: Midjourney, Krea, Canva Magic Design depending on the project type.

2

Asset creation and refinement

Generate specific elements needed. Tools: Adobe Firefly for photo-based work, Recraft for vectors, sometimes back to Midjourney for illustrations.

3

Compositing and adjustment

Photoshop Generative Fill for extending backgrounds, removing objects, matching lighting. This is where AI saves the most time.

4

Manual refinement

The last 20 percent is always by hand. Typography adjustments, color refinement, compositional tweaks. AI gets you most of the way there, but craft still matters for the final output.

Once I've settled on a direction, the tools shift to production mode. Firefly for any photo manipulation or extension work. Figma's AI features for organizing and speeding up interface construction. Canva if I need to generate a high volume of variations quickly—social media graphics, ad variations, that kind of thing.

The final stage is always manual. Typography decisions, color adjustments, compositional refinement, ensuring everything aligns with brand guidelines. AI tools are remarkably good at getting you eighty percent of the way to final output, but that last twenty percent still requires human judgment and craft. I'm not convinced that ratio will change, honestly. The eighty percent is mechanical execution. The twenty percent is design thinking.

Choosing the Right Tool for Your Situation

The "best tool" question depends entirely on your context. What are you designing, who's it for, what's your skill level, and what constraints are you working within? Here's how I think about those decisions.

What I've Learned From Two Years of Daily Use

The relationship between designers and AI tools is still being figured out. We're early enough that best practices are still emerging, and late enough that the technology actually works reliably. That's a productive place to be. Here's what's become clear to me through constant use.

First, AI design tools amplify your existing design knowledge. If you understand composition, color theory, typography, and visual hierarchy, you'll get dramatically better results than someone who doesn't. The tools don't replace that knowledge—they accelerate your ability to test ideas and execute them. Think of them as multipliers on your existing skills, not replacements for learning design fundamentals.

Second, the learning curve is real but shorter than you'd think. The first week with any new AI tool feels clumsy. By week two, you start developing intuition for how it thinks and what prompts work well. By month two, it's an integrated part of your workflow. The initial awkwardness is worth pushing through.

Third, combining tools produces better results than relying on any single platform. I use Midjourney for initial concept exploration, Firefly for production asset refinement, Canva for high-volume variations, and Figma's AI for interface work. Each tool covers gaps in the others. Thinking in terms of a toolkit rather than finding the one perfect tool makes you more effective.

Finally, the technology is improving fast enough that revisiting tools you dismissed six months ago is worthwhile. Firefly's text rendering got noticeably better between versions. Canva's Magic Design became substantially more sophisticated. The landscape is moving quickly, and yesterday's limitations might be resolved today.

Getting Started: Practical First Steps

If you're just starting with AI design tools, the array of options can be overwhelming. Here's where to begin based on what you're actually trying to accomplish, not abstract comparisons of features you may never use.

Where This Goes Next

The trajectory of AI design tools is clear enough: they'll get better at understanding intent, produce higher-quality output, and integrate more seamlessly into existing workflows. The specific features will evolve, but the fundamental dynamic won't change. These tools accelerate execution. They don't replace creative thinking.

What I find most interesting is how this technology is changing what we consider valuable in design work. When asset generation is cheap and fast, the premium shifts to taste, strategic thinking, and understanding audience. The designers thriving with AI aren't the ones with the most technical skills—they're the ones with strong opinions about what makes design effective.

I'm spending less time on mechanical execution and more time on creative direction. Projects that used to require weeks for exploration and iteration now compress into days. That doesn't mean I'm working less—it means I'm exploring more directions, testing more ideas, and delivering better results. The bar for "good enough" has risen because generating multiple alternatives is trivial now.

The question isn't whether to adopt these tools. If you work in any design-adjacent field, that decision is already made for you by client expectations and competitive pressure. The question is how to adopt them thoughtfully—understanding where they add value, where they fall short, and how to integrate them into creative work that still requires human judgment.

That integration is the craft worth mastering now.