Amplifying creativity with AI tools for designers in 2026

AI has moved well beyond novelty in the design field, and, in 2026, many designers are actively exploring how it might support parts of their creative practice. 

Rather than replacing creative thinking, today’s AI tools are increasingly being tested as ways to assist with ideation, refinement, production and evaluation, when used thoughtfully and responsibly.

From AI-assisted moodboarding to image editing guided by sketches and UX analysis tools that surface potential friction points, the current generation of platforms is designed to integrate into existing workflows rather than override them. This article does not aim to recommend or endorse specific tools. Instead, it highlights examples of AI-enabled platforms designers may encounter or choose to explore, depending on their individual practice, context and ethical considerations.

As with any technology, AI is most effective when it supports human judgment, creativity and accountability, not when it replaces them.

Adobe Firefly

Adobe Firefly continues to evolve as one example of generative AI embedded within professional design software. Integrated across Adobe Creative Cloud, it allows designers to generate images, vectors, textures and colour variations from text prompts in multiple languages. Some designers use Firefly during early exploration phases, for instance testing surface patterns or visual directions, while retaining full control over refinement and final output within familiar Adobe tools.

onbeacon AI

onBeacon.ai is an example of how AI is being applied to UX and experience evaluation. It provides automated feedback on user flows, interfaces and content, highlighting potential usability, accessibility or conversion issues before a design is finalized. Rather than replacing usability testing or professional judgment, tools like this are sometimes used as an early signal to help designers identify areas that may benefit from closer review during onboarding flows, product journeys or experience audits.

Canva Magic Studio

Canva’s Magic Studio illustrates how generative AI is being integrated into high-volume content creation workflows. Features such as Magic Design, Magic Edit, Beat Sync and Translate allow layouts, visuals, video timing and language variations to be generated or adapted quickly. For some teams, these tools are used to prototype ideas or adapt assets across formats, particularly in social, presentation or campaign contexts where speed and flexibility are priorities.

Midjourney

Midjourney remains a widely used example of text-to-image generation for conceptual and exploratory visuals. Designers may use it to generate illustration styles, abstract concepts or stock-like imagery as part of early ideation. Outputs typically require critical evaluation and refinement, and the tool is often treated as a source of inspiration rather than finished artwork.

Runway ML

Runway demonstrates how AI is being explored in motion and video workflows. With tools for text-to-video generation, clip transformation and automated scene detection, designers and content creators can experiment with motion concepts or storytelling formats more quickly. Common use cases include early motion studies, social video adaptation or visualizing narrative directions before full production.

Figma AI

Figma’s expanding AI features reflect how automation is being incorporated into collaborative design environments. From layout assistance and accessibility suggestions to copy support through plugins such as FigGPT, these tools can help streamline repetitive tasks. Many designers use them selectively to speed up iteration while maintaining responsibility for structure, hierarchy and interaction decisions.

Khroma

Khroma is an example of AI applied to colour exploration. By training the system on preferred colours, designers can generate palette combinations, gradients and pairings aligned with a particular aesthetic direction. It is commonly used as a discovery tool during branding or visual identity exploration rather than as a final decision-maker.

Mixboard

Mixboard illustrates how AI is entering the ideation and moodboarding phase of design. By allowing designers to describe a feeling, theme or concept in natural language, it generates visual moodboards that help externalize early thinking. Some designers use tools like this to support creative alignment with teams or clients before committing to detailed design work, while intentionally keeping the process open-ended.

Nano Banana

Nano Banana represents a more hands-on approach to AI-assisted image editing. Designers can draw directly on images to indicate areas for change, add text instructions for specificity and allow the AI to interpret and refine those edits. The ability to resize assets for different formats without cropping key details and to generate short video clips with native audio through Veo 3.1 makes it a tool some designers explore for rapid iteration across platforms.

Recraft

Recraft stands out for its focus on vector-based AI generation. It produces scalable, editable graphics such as icons, illustrations and abstract motifs, which can be useful for designers who require flexibility across print and digital applications. Rather than replacing vector design, it is often explored as a way to generate starting points that can then be refined within established brand systems.

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Ethics, Responsibility and AI Use

As AI becomes more embedded in design workflows, ethical use is increasingly important. Designers are encouraged to align their practice with the RGD's Code of Ethics, which now includes specific guidance on the responsible use of Artificial Intelligence. This includes being transparent about how AI is used in professional work, maintaining an AI statement or policy and respecting intellectual property, licensing and the rights of creators whose work may be reflected in AI systems.

Because AI tools vary widely in how they are trained, licensed and governed, designers are also encouraged to review the terms of use, data policies and licensing conditions of each tool individually before using them in client or public-facing work. Ethical responsibility ultimately sits with the designer — not the tool.

In practice, this may include documenting AI use in project notes, disclosing AI involvement when appropriate and ensuring outputs are reviewed and refined by a human designer before release.

Considerations for designers exploring AI tools

• Some designers begin by using AI for ideation and exploration, where experimentation carries lower risk.
• Precision-focused tools may be useful when polish, consistency or scalability are required.
• UX-oriented tools can help surface potential friction early, complementing (not replacing) user research and testing.
• Across all uses, human judgment, authorship and accountability remain central.