In design, time has always been more than just a resource — it’s a key metric of competitiveness, and today, it matters more than ever. With generative AI accelerating every phase of product development, the stakes are rising fast, triggering a sharp rise in demand for AI-driven design tools.
According to Precedence Research, the global generative AI in design market is projected to grow from $741.11 million in 2024 to over $13.9 billion by 2034, expanding at a CAGR of 34.11%. Businesses are not just adopting AI, they’re rebuilding how they design, iterate, and launch.
This transformation is already underway at ORIL. In our latest webinar, the Head of Design, Oleh Kravets, explained how AI tools are reshaping product workflows, enabling companies to cut time to market by more than 50%. In this article, we’ll unpack those insights, tools, and strategies so you can implement them in your next release.
What is AI in Design?
AI in design refers to the integration of machine learning and generative models into the creative workflow, augmenting how teams ideate, validate, and execute. It powers tasks once considered manual or intuition-driven, such as generating wireframes, drafting interface copy, simulating user interactions, and analyzing usability patterns in real time.
However, these AI-powered tools do not replace designers; they function as a creative accelerator. They handle repetitive, early-stage work, freeing teams to focus on refinement, alignment, and high-impact decisions. This shift is helping companies move from idea to execution with greater speed, confidence, and clarity.
Why AI is Gaining Traction in Product Teams
AI is gaining ground in product teams for one simple reason: it saves time.
Design work often stalls early — wireframes take too long, layouts need rework, and UX copy falls out of sync. AI shortens that entire cycle. What used to take days now takes hours.
Take Figma AI, for example. It can generate interface layouts in seconds from just a few prompts. Beyond speed, AI brings sharper insight. Tools like Attention Insight and Maze can let teams test user behavior before building anything. This helps catch issues early, like confusing flows or weak visual hierarchy, before they become expensive problems later.
AI also helps teams work better together. When key design decisions are made earlier, the handoff to engineering is smoother. There’s less back-and-forth, fewer revisions, and faster build cycles.
However, if AI tools are faster, smarter, and widely available, why do product teams still fall behind? Let’s explore.
Why Design Bottlenecks Still Exist
The answer lies in how teams work, not the tools they use. Here are four reasons these bottlenecks continue to surface:
- Outdated processes: Most teams still follow a traditional, sequential workflow — brief, design, review, repeat. AI demands a shift to faster, parallel decision-making, and few teams are structurally ready for that.
- Poor inputs, poor outcomes: Generative tools can simulate user flows, generate copy, or visualize screens in seconds, but only when given precise, contextual prompts. Most teams don’t prompt well, so they get average outputs and blame the tool.
- Stakeholder misalignment: AI accelerates design. However, misdirection is accelerated if product, design, and engineering are not aligned up front. Consensus still matters, and too often, it comes too late.
- Misuse of automation: Treating AI as a shortcut leads to shallow design. The teams that win are those that interrogate AI output, simulate user extremes, and guide the system, not follow it.
The bottleneck has shifted from creativity to clarity. Success in modern design hinges on combining AI tools with critical thinking, effective prompting, and decisive collaboration.
The Benefits of AI in Design
AI in product design delivers measurable performance gains. The key industry benchmarks demonstrate how AI transforms design velocity and effectiveness:
- 54% faster time to market with AI-integrated workflows
- 50% shorter prototyping cycles using AI-assisted tools
- 30% increase in A/B test conversion rates through AI-driven design iterations
These improvements extend far beyond time savings. By accelerating mockup generation, layout refinement, and UX copy development, teams gain the capacity to focus on strategic priorities, like achieving product-market fit, understanding user behavior earlier, and scaling design systems with confidence.
Where AI Still Falls Short in Design
AI-driven output is not the finish line; it’s a starting point. While AI tools can take design 80% of the way, they often miss brand nuance and emotional context. Teams can bridge this by prompting AI to simulate different user personas, like an anxious user or a cynical customer. This technique, sometimes called prompting with “AI deamons,” surfaces usability issues you may overlook from a single perspective.
The takeaway for product teams is clear: Use AI to explore, draft, and analyze. Let it surface patterns and possibilities. But never ship its work untouched. The winning approach isn’t delegation, it’s collaboration. Treat AI not as a designer but as a fast, tireless co-pilot who still needs your hands on the wheel.
AI Tools Making a Real Difference
Here’s how leading AI platforms are changing the way design work gets done across the industry:
- Figma AI: Generates complete UI layouts, text blocks, and interactive components from simple prompts. Teams report cutting early-stage wireframing and mockup cycles by up to 50%, enabling faster validation and stakeholder feedback.
- Attention Insight: This tool uses predictive heatmaps to simulate user attention before launch. Designers can catch layout issues and optimize visual hierarchy long before usability testing begins, turning subjective decisions into data-informed ones.
- Chroma: Trains on a brand’s visual identity to produce consistent palettes, gradients, and creative directions. This allows teams to iterate rapidly without constantly circling back to brand review.
- Jasper: Assists UX writers by drafting microcopy and interface text in the right tone of voice. It’s handy during sprints when content must be generated, edited, and aligned in parallel with design.
These tools don’t replace design work; they reframe it, freeing teams from low-leverage tasks so they can focus on refinement, insight, and impact.
How to Start Integrating AI Into Design
- AI adoption is less about tooling and more about strategy. Here’s what high-performing teams do differently:
- Identify friction points first. Before choosing a tool, pinpoint where your design cycle slows down, whether wireframing, UX copy, or stakeholder reviews. Adopt AI with intent: use it to unblock specific bottlenecks.
- Pilot with one focused use case. Don’t overwhelm your process with tools. Start small — use Figma AI to generate layout variants or Maze to validate early-stage usability. Prove value before scaling.
- Build prompt literacy. Train your team to write precise, contextual prompts and critique AI outputs effectively. Prompting isn’t a soft skill; it’s a core design competency. It determines how helpful or hollow the AI’s contribution will be. Prompting tips to get more value:
- “Ask ‘why,’ not just ‘what’ — dig for rationale behind AI’s output”
- “Simulate edge cases (e.g., “What would confuse a first-time user?”)”
- “Frame prompts like you would a user interview.”
The risk isn’t that AI will do too much, it’s that we’ll ask it to do the wrong things without checking.
Appoint an AI lead. Assign someone to own AI experimentation — testing tools, refining prompts, and supporting team adoption. Without ownership, experimentation loses momentum. - Build a feedback loop. Treat AI outputs as drafts. Create a feedback system — short sprints to test, review, and refine. This ensures alignment with brand standards, user needs, and design intent.
- Reassess monthly. The AI landscape evolves rapidly. Teams that pause to evaluate what’s working (and what’s obsolete) every few weeks stay ahead of the curve — and avoid wasting time on outdated tools.
Most importantly, don’t wait for perfection. The AI tools available now are already saving time and elevating quality for teams willing to experiment.
Conclusion
In 2025, design defines product velocity. Teams that move from idea to execution with clarity and speed outperform those stuck in traditional cycles. AI now plays a central role in achieving that momentum.
At ORIL, we apply AI to elevate design quality, sharpen decision-making, and accelerate delivery. From MVPs to enterprise platforms, every product benefits when strategy, creativity, and intelligent tooling align. As tools execute more, designers are shifting toward oversight roles, guiding AI output with critical thinking rather than replacing it.
Explore how our UI/UX design services equip your team to build faster and build right.
