Best AI Mockup Generators for Product Visuals: The Honest Assessment for AI Design Professionals

Executive Summary: AI Reality Check for Product Mockup Generation

The AI mockup generator market presents a mature foundation with expanding capabilities, but buyers must navigate significant capability gaps between vendor marketing claims and delivery reality. Our analysis of 9 major platforms reveals no universal solution - instead, a specialized landscape where vendor choice depends critically on company size, use case, and technical requirements.

Adoption Timing: This is the right time for selective AI adoption in mockup generation. With 80% of new UI designs now AI-generated[31] and established vendors proving ROI in documented case studies, early adopter risk has diminished. However, success requires careful vendor matching to specific use cases rather than broad AI transformation approaches.

Vendor Landscape: The market demonstrates healthy competitive intensity with clear specialization patterns - Uizard dominates enterprise collaboration[19][49][54], Mockey leads SMB e-commerce[85][91][94], Dynamic Mockups specializes in bulk processing[114][117]. This fragmentation actually benefits buyers by preventing vendor lock-in while enabling precise solution matching.

Bottom Line: AI mockup generation delivers genuine business value when properly scoped and vendor-matched. Avoid "AI transformation" thinking - focus on specific use case solutions with realistic capability expectations and thorough vendor validation.

AI vs. Traditional Approaches: What the Evidence Shows

AI Success Areas: AI mockup generators genuinely outperform traditional design workflows in specific, well-defined scenarios. The strongest evidence comes from rapid prototyping, where conversational AI tools like Uizard enable teams to generate 200+ screens in 6 months versus traditional 18-month design cycles[49][54]. E-commerce product visualization shows consistent advantages, with sellers reporting 40% higher client approval rates using AI-generated mockups compared to traditional photography[2][13].

Template-based mockup creation represents AI's clearest victory over manual design work. Vendors like Mockey and Canva demonstrate that AI can intelligently place products into contextual scenes with realistic lighting and shadows in under 1 minute per mockup[78][85][91]. Traditional approaches require 15-30 minutes per mockup plus graphic design expertise, making AI's cost advantage undeniable for high-volume content creation.

AI Limitations: Current AI approaches fail consistently in complex 3D visualization and architectural mockups[4][5][12][14]. Despite vendor promises, complex product geometry, precise material rendering, and custom lighting scenarios still require traditional 3D modeling expertise. The technology also struggles with brand consistency across large mockup sets - 45% of "AI-powered" tools merely automate template placement without machine learning capabilities that could ensure visual coherence[7][12].

Performance limitations become critical at scale. While AI excels at generating individual mockups, processing large projects with complex requirements often results in generic outputs that require extensive manual refinement, negating time savings. Projects exceeding 50 screens consistently show performance degradation across platforms[25][33][50][52].

Implementation Reality: Successful AI mockup adoption requires 2-6 weeks for proper deployment even with user-friendly platforms[55][56][147][149]. Organizations typically need dedicated change management, workflow reconfiguration, and team training. The technology demands continuous human oversight - AI generates starting points that human designers refine rather than delivering finished assets.

ROI Truth: Documented customer outcomes show realistic 50-80% time reduction in mockup creation workflows[11][18][49], but this applies specifically to high-volume, template-suitable content. Custom, brand-specific, or complex mockups show marginal improvements over traditional approaches. The ROI timeline extends 6-12 months as teams develop AI-human hybrid workflows[49][23][29].

When to Choose AI: AI investment makes business sense for high-volume content creation (e-commerce catalogs, social media assets, rapid prototyping), cross-functional collaboration scenarios (product teams, stakeholder reviews), and standardized product visualization (packaging, apparel, simple consumer goods). The technology excels when speed and volume matter more than unique creative expression.

When to Stick with Traditional: Conventional design approaches remain superior for complex 3D visualization, highly customized brand experiences, architectural or technical product mockups, and low-volume, high-creativity projects. Traditional methods also provide better quality control for mission-critical brand assets where consistency and precision outweigh speed advantages.

Vendor Analysis: Strengths, Limitations & Best Fit Scenarios

Enterprise Collaboration Leaders

Uizard Autodesigner 2.0 - Best for Large Teams, Proven Enterprise ROI

Actual Capabilities: Uizard delivers genuine conversational AI that transforms natural language inputs into functional UI mockups with real-time collaboration features[39][47][54]. The platform generates wireframes, mockups, and interactive prototypes through text-based commands, then enables team editing without version control conflicts.

Real-World Performance: Strongest evidence base in the category. Ingenico achieved 66% reduction in ideation time and generated 200+ screens in 6 months versus previous 18-month cycles[49][54]. Multiple case studies demonstrate successful enterprise deployments with measurable productivity gains and improved cross-functional collaboration[56][57].

Best Fit Scenarios: Enterprise product teams requiring rapid prototyping and stakeholder collaboration. Particularly effective for organizations with 3:1 developer-to-designer ratios needing to democratize design creation[52][56]. Ideal for companies prioritizing speed over pixel-perfect aesthetics in early-stage product development.

Limitations & Risks: Performance degrades significantly in projects exceeding 50 screens[45][50][52]. Code export remains component-level only, requiring developer intervention for production implementation[45][57]. The platform demands 3-6 months implementation timeline with substantial change management investment[52][56].

Implementation Reality: High complexity deployment requiring dedicated project management and organizational restructuring. Success depends on establishing clear AI-human collaboration protocols and realistic expectation setting about output quality versus speed[56].

Competitive Position: Clear leader in enterprise AI collaboration with superior evidence base and mature enterprise features. However, alternatives like Visily offer simpler deployment for teams prioritizing developer handoff over collaborative design creation.

Developer-Focused Solutions

Visily - Best for Product Teams, Developer Handoff Excellence

Actual Capabilities: Visily excels at screenshot-to-design conversion and provides superior developer handoff through Figma plugin integration and component-based exports[135][150][151]. The platform bridges design-to-development workflows more effectively than pure design-focused competitors.

Real-World Performance: PayBIX case study demonstrates practical value - the startup created MVP wireframes that enabled pursuit of €1.2M funding[147]. While evidence base is smaller than Uizard's, documented outcomes show particular strength in startup and product team scenarios.

Best Fit Scenarios: Product teams and startups requiring rapid MVP development with clean developer handoff. Particularly valuable when transitioning from concept sketches or existing applications to production-ready designs[148][149]. Works best for technology companies prioritizing development velocity over extensive collaboration.

Limitations & Risks: Performance issues emerge in larger projects, and lacks on-premise deployment options critical for enterprise compliance[142][144]. The platform focuses primarily on UI/UX rather than broader product mockup visualization.

Implementation Reality: Medium complexity with 2-week SMB onboarding timelines, though enterprise deployments require design system integration planning[147][149]. Success depends on existing Figma workflow adoption and developer toolchain compatibility.

ROI Assessment: Strong value for startups and product teams with rapid payback through development acceleration. Mid-market companies see justified investment when developer handoff represents a significant workflow bottleneck.

Competitive Position: Specialized strength in product development workflows but limited broader mockup capabilities compared to general-purpose platforms like Uizard or Mockey.

High-Volume Processing Specialists

Dynamic Mockups - Best for Bulk Processing, API Integration

Actual Capabilities: Dynamic Mockups provides API-driven bulk mockup processing with PSD automation capabilities for high-volume e-commerce and print-on-demand workflows[114][117]. Claims processing speeds of 100+ mockups per minute through programmatic interfaces.

Real-World Performance: Limited independent validation with primarily vendor-supplied performance claims[115][126]. The platform shows strength in technical capabilities but lacks documented customer case studies comparable to Uizard or proven user adoption metrics like Mockey.

Best Fit Scenarios: High-volume e-commerce operations and print-on-demand businesses requiring automated mockup generation at scale[115][121]. Most valuable when mockup creation becomes a production bottleneck rather than creative process.

Limitations & Risks: Cloud-only deployment with unclear distinction between AI capabilities and traditional automation[118][120][125]. Performance under concurrent usage scenarios remains unverified, and the platform requires significant developer resources for API configuration[114][125].

Implementation Reality: Medium complexity requiring dedicated developer resources for API integration and workflow configuration. Success depends on existing e-commerce infrastructure and technical team capabilities.

ROI Assessment: Potentially strong for high-volume scenarios but lacks independent validation of claimed performance benefits. Companies should demand proof-of-concept testing before full implementation commitment.

Competitive Position: Unique API-first approach serves specialized needs but broader market finds more accessible solutions with Mockey or Uizard for most use cases.

SMB and E-commerce Leaders

Mockey - Best for Small Businesses, E-commerce, Free Tier Excellence

Actual Capabilities: Mockey delivers production-ready product mockups with an unusually generous free tier and focus on e-commerce visualization[78][85][91]. The platform provides realistic product placement, automated lighting adjustments, and mobile accessibility through Android app.

Real-World Performance: Strong user adoption evidence with 100k monthly users and documented growth trajectory[94]. While lacking enterprise case studies, the platform demonstrates proven value delivery through sustained user engagement and positive community feedback.

Best Fit Scenarios: SMB e-commerce sellers, social media content creators, and organizations requiring cost-effective product visualization[91][95]. Particularly effective for standardized products like apparel, accessories, and packaged goods where template-based approaches provide sufficient customization.

Limitations & Risks: AI-generated shadows and reflections often appear unrealistic, requiring manual refinement for professional applications[78][95]. Android-only mobile app limits cross-platform team collaboration, and advanced 3D capabilities lag behind specialized competitors.

Implementation Reality: Low complexity with straightforward onboarding and minimal training requirements[78][87]. Most users achieve productive workflows within days rather than weeks, making it accessible for resource-constrained small businesses.

ROI Assessment: Exceptional value for SMBs through free tier accessibility and rapid time-to-value. Paid tiers at reasonable pricing points provide clear ROI through reduced photography costs and faster content creation cycles.

Competitive Position: Clear SMB market leader with sustainable competitive advantages through free tier and mobile accessibility. Enterprise buyers find more sophisticated alternatives, but Mockey dominates its target segment.

Accessibility-Focused Options

Canva AI Mockup Generator - Best for Non-Designers, Quick Social Assets

Actual Capabilities: Canva provides template-based mockup generation integrated with broader design platform capabilities[63][69]. The platform emphasizes accessibility for non-designers through drag-and-drop interfaces and extensive template libraries.

Real-World Performance: Limited specific performance data but benefits from Canva's broader platform success and user satisfaction[71][73]. The mockup functionality leverages Canva's established design ecosystem rather than standalone AI innovation.

Best Fit Scenarios: Non-designers, freelancers, and small businesses requiring basic social media mockups without dedicated design resources[62][71][76]. Most effective when mockup creation represents occasional need rather than core business function.

Limitations & Risks: Template dependency limits customization and advanced 3D capabilities lag significantly behind specialized competitors[62][71]. The platform serves general design needs adequately but lacks deep mockup specialization.

Implementation Reality: Minimal complexity with browser-based access and under-hour onboarding[70][72]. Success correlates directly with template suitability for specific business needs.

ROI Assessment: Cost-effective for basic needs but limited scalability for growing businesses. Value proposition depends heavily on broader Canva platform utilization rather than standalone mockup capabilities.

Competitive Position: Accessible entry point but users typically graduate to specialized solutions as mockup requirements become more sophisticated.

Business Size & Use Case Analysis

Small Business (1-50 employees): Speed and Accessibility Priority

Optimal Vendor Selection: Mockey emerges as the clear SMB leader through its unlimited free tier and e-commerce focus[85][91][94]. The platform provides immediate value without financial commitment, making it ideal for resource-constrained small businesses testing AI adoption. Canva serves as a secondary option for businesses requiring broader design capabilities beyond product mockups[62][71][76].

Budget-Friendly Implementation: Small businesses typically invest $0-$29/month for complete mockup solutions. Mockey's free tier covers most SMB needs, while paid upgrades at $9-$19/month provide additional features without breaking budgets[85][88]. Implementation complexity remains minimal - most small businesses achieve productive workflows within 2-3 days[78][87].

Success Factors: Small businesses succeed when they match their product types to platform strengths. Mockey excels with apparel, accessories, and packaged goods but struggles with complex 3D products. Businesses selling standard consumer products see immediate benefits, while those requiring custom visualization may need upgraded solutions sooner.

Mid-Market (50-500 employees): Balance of Capability and Complexity

Strategic Vendor Choices: Visily and Uizard compete directly in this segment, with selection depending on collaboration versus development focus[147][149][19][56]. Product-focused mid-market companies typically prefer Visily for developer handoff capabilities[148][149], while design-collaborative organizations benefit from Uizard's real-time editing features[56][57].

Investment Analysis: Mid-market companies justify $500-$2,000/month platform investments through team productivity gains. Implementation timelines extend 2-6 weeks with dedicated change management requirements[56][147]. The total cost of ownership includes training, workflow reconfiguration, and potential integration development.

Growth Considerations: Mid-market selection should anticipate scaling to 100-200 team members and integration with existing design systems. Platforms like Visily provide clearer upgrade paths through Figma ecosystem integration[151], while Uizard offers superior collaboration features for distributed teams[56][57].

Integration Requirements: 92% of mid-market buyers require Figma/Adobe Creative Cloud compatibility[33][34]. This requirement eliminates several vendors and emphasizes platforms with strong ecosystem integration rather than standalone capabilities.

Enterprise (500+ employees): Advanced Features and Compliance Focus

Enterprise-Grade Solutions: Uizard dominates enterprise scenarios through proven large-scale deployments and documented ROI case studies[49][54]. The platform's 3-6 month implementation timeline aligns with enterprise change management requirements, while collaboration features support complex organizational structures[52][56].

Compliance and Security: Critical limitation across most vendors - on-premise deployment options remain scarce[52][142][205][209]. Healthcare and financial services enterprises face significant vendor limitations due to data residency and compliance requirements. Only specialized vendors offer adequate enterprise security features.

Large-Scale Deployment Factors: Enterprise success requires 3:1 developer-to-designer ratios and substantial change management investment[52][56]. Performance degradation in projects exceeding 50 screens affects 62% of enterprise deployments[25][33][50][52], requiring careful project scoping and realistic capability expectations.

Industry-Specific Considerations

E-commerce and Retail: Mockey and Dynamic Mockups serve different e-commerce segments[91][95][115][121]. Small-to-medium e-commerce operations benefit from Mockey's accessibility and realistic product rendering, while high-volume retailers require Dynamic Mockups' API-driven bulk processing capabilities. E-commerce sellers report 40% higher client approval rates with AI-generated mockups compared to traditional photography[2][13].

Software and Technology: Technology companies prioritize developer handoff and code export capabilities. Visily's Figma integration and component-based exports align with software development workflows[135][150][151], while Uizard serves larger technology organizations requiring cross-functional collaboration[19][31][56][57]. 80% of new UI designs now leverage AI generation in technology sectors[31].

Healthcare and Financial Services: Severe vendor limitations due to compliance requirements. Most platforms lack on-premise deployment, GDPR-specific features, and healthcare data handling capabilities[52][142][205][209]. These industries often require custom implementations or specialized vendors not covered in mainstream AI mockup platforms.

Print-on-Demand and Manufacturing: PixWrap specializes in 3D rendering for physical product mockups, particularly tumblers and promotional items[99][106][110]. However, evidence base remains limited with heavy vendor reliance, making thorough evaluation essential before commitment.

Implementation Reality & Success Factors

Technical Requirements and Infrastructure Assessment

Infrastructure Readiness: Most AI mockup generators require minimal technical infrastructure beyond reliable internet connectivity and modern browsers[70][72][78][87]. However, enterprise deployments demand significant organizational preparation including design system integration, workflow reconfiguration, and team training protocols[52][56].

Expertise and Resource Allocation: SMB implementations succeed with 1 designer plus 2 developers for integration work[55][56]. Enterprise deployments require dedicated project management, change management specialists, and 3:1 developer-to-designer ratios for successful adoption[52][56]. Mid-market organizations typically need part-time project coordination and 2-4 weeks focused implementation effort[147][149].

System Integration Complexity: 92% of buyers require existing ecosystem compatibility[33][34]. Platforms with strong Figma, Adobe Creative Cloud, and development tool integration show higher success rates. API-first solutions like Dynamic Mockups demand developer expertise for configuration and maintenance[114][125].

Timeline Expectations and Value Realization

Realistic Deployment Schedules: SMBs achieve productive workflows within 2 days to 2 weeks[21][55][78][87]. Mid-market implementations require 2-6 weeks including team training and workflow adjustment[147][149][56]. Enterprise deployments extend 3-6 months with comprehensive change management and organizational adoption requirements[52][56].

Value Realization Timelines: Immediate productivity gains occur in high-volume, template-suitable workflows. However, full ROI typically requires 6-12 months as teams develop AI-human hybrid processes and optimize workflow efficiency[49][23][29]. Organizations expecting immediate transformation often experience adoption resistance and suboptimal outcomes.

Performance Benchmarking: Successful implementations establish clear success metrics including time-per-mockup reduction (target: 50-80% improvement[11][18][49]), team adoption rates (target: 75% organizational uptake[29][37]), and quality consistency measures. Projects exceeding 50 screens require careful performance monitoring due to documented degradation patterns[25][33][50][52].

Common Failure Points and Risk Mitigation

Organizational Resistance: Change management represents the primary failure point in AI mockup adoption. Teams accustomed to traditional design processes resist AI-generated starting points, viewing them as creativity limitations rather than efficiency enablers. Success requires clear communication about AI's role as design acceleration rather than replacement[37][38].

Unrealistic Capability Expectations: 45% of implementations fail due to misaligned expectations about AI versus automation capabilities[7][12]. Organizations expecting perfect, finished mockups encounter disappointment with AI-generated starting points requiring human refinement. Proper expectation setting about AI's role in hybrid workflows prevents adoption failure.

Vendor Selection Mismatches: Poor use case alignment with vendor strengths causes implementation difficulties. Enterprises selecting accessibility-focused platforms like Canva encounter scalability limitations, while SMBs choosing enterprise-focused platforms like Uizard face complexity overwhelm[62][71][52][56].

Performance Limitations at Scale: 62% of enterprises report performance degradation in large projects[25][33][50][52]. Organizations must establish project size limitations and alternative workflows for complex requirements exceeding AI capabilities.

Success Enablers and Best Practices

Pilot Project Approach: Successful implementations begin with focused pilot projects under 50 screens with clear success criteria[21][25][28]. Pilot testing validates vendor capabilities, team adoption patterns, and workflow integration requirements before full-scale deployment.

AI-Human Workflow Design: Optimal outcomes combine AI generation with human refinement rather than expecting autonomous AI creation. Successful teams establish clear handoff protocols between AI-generated concepts and human design finalization, maximizing both speed and quality outcomes.

Continuous Training and Optimization: Ongoing team development in AI tool utilization correlates with implementation success. Organizations providing regular training updates and workflow optimization achieve higher adoption rates and productivity gains compared to one-time implementation approaches[29][37].

Realistic Scope Management: Success requires matching project complexity to AI capabilities. Teams that scope projects appropriately for AI strengths (high-volume, template-based, rapid iteration) while using traditional approaches for complex visualization achieve optimal overall productivity.

Market Evolution & Future Considerations

Technology Maturity and Development Trajectory

Current Capability Assessment: The AI mockup generation market demonstrates uneven maturity across use cases. Basic product visualization (apparel, packaging, consumer goods) has reached production reliability with consistent quality and performance[4][5][12][14]. However, complex 3D architectural visualization and custom brand implementation remain experimental with unpredictable outcomes[4][5][12][14].

Innovation Acceleration Patterns: Conversational AI capabilities are advancing rapidly, with Uizard leading text-to-design generation[39][47]. Mobile accessibility is expanding through platforms like Mockey's Android app[81][91], while API integration capabilities are maturing for enterprise workflow integration[114][117]. AR/VR preview capabilities are emerging, with 70% of new tools expected to offer immersive mockup previews by 2025[1][7].

Performance Scaling Improvements: Current limitations in large-scale project performance (>50 screens) represent active development areas[25][33][50][52]. Vendor roadmaps focus on processing optimization and collaborative workflow enhancements, though timeline for resolution remains 12-18 months based on current development patterns.

Vendor Stability and Competitive Dynamics

Market Consolidation Indicators: 70% consolidation expected with established players like Uizard and Visily capturing professional market segments[31][34]. Platform integration trends suggest screenshot-to-design capabilities becoming standard features rather than specialized advantages[34][135]. Specialized vendors in print-on-demand (PixWrap) and bulk processing (Dynamic Mockups) likely remain niche solutions[5][14].

Competitive Response Patterns: Freemium model pressures from Mockey's unlimited free tier are forcing competitor pricing adjustments[85][91]. Enterprise-focused vendors are expanding collaboration features to justify premium pricing[19][56]. Feature convergence is occurring in core capabilities while specialization persists in advanced functionality.

Long-term Viability Assessment: Uizard demonstrates strongest enterprise positioning through documented customer success and sustained development investment[49][54]. Mockey's user growth and sustainable free-tier model suggest stable SMB market presence[94][91]. Specialized vendors require careful evaluation for long-term platform commitment due to limited market validation.

Investment Timing and Strategic Considerations

Adoption Window Analysis: Current timing favors selective AI adoption for established use cases rather than waiting for future capability development. 80% AI generation adoption in UI design demonstrates proven market acceptance[31], while documented ROI case studies reduce early adopter risk[49][54].

Technology Risk Assessment: Feature stability in basic mockup generation provides reasonable implementation confidence for standard business needs. Experimental capabilities (complex 3D, architectural visualization) require cautious approach with alternative solution backup plans. Vendor lock-in risks remain manageable through standard export formats across major platforms[19][34][45].

Competitive Advantage Timing: Early adopters in specific use cases (e-commerce visualization, rapid prototyping, cross-functional collaboration) achieve sustainable productivity advantages. Waiting for technology maturity in complex applications remains appropriate, but basic mockup workflows offer immediate competitive benefits.

Budget Planning Considerations: Implementation costs are frontloaded with ongoing subscription models. SMB adoption provides immediate positive ROI, while enterprise implementations require 6-12 month payback planning[49][54]. Mid-market companies should budget for workflow transition costs beyond subscription pricing[56][147].

Decision Framework & Next Steps

Vendor Evaluation Methodology

Primary Assessment Criteria: Use case alignment represents the most critical evaluation factor. Organizations must clearly define mockup volume requirements, complexity levels, and collaboration needs before vendor evaluation. Evidence-based validation should prioritize vendors with documented customer outcomes over marketing claims.

Technical Evaluation Framework: Pilot testing with realistic project scope (<50 screens) provides accurate capability assessment[21][25][28]. Integration compatibility with existing design tools requires hands-on testing rather than feature list comparison[33][34]. Performance benchmarking should include concurrent user scenarios and project scaling patterns.

Business Case Development: ROI calculations should include implementation costs, training time, and workflow transition expenses beyond subscription pricing. Risk assessment must evaluate vendor stability, feature development roadmaps, and alternative solution availability. Success metrics should establish clear productivity benchmarks and adoption timeline expectations.

Proof of Concept Execution Strategy

Structured Testing Approach: Start with 10-20 mockup pilot project using actual business requirements rather than vendor demonstrations. Include multiple team members in evaluation to assess collaboration features and workflow integration. Test performance limits by gradually increasing project complexity and team size.

Comparison Framework: Evaluate 2-3 vendors simultaneously using identical project requirements for direct capability comparison. Focus on business outcomes (time savings, quality consistency, team satisfaction) rather than feature checklists. Document specific limitations encountered during testing for realistic implementation planning.

Success Criteria Definition: Establish quantitative benchmarks including time-per-mockup targets (50-80% improvement goal[11][18][49]), quality consistency measures, and team adoption rates. Define acceptable failure modes and alternative workflow requirements for scenarios exceeding AI capabilities.

Implementation Planning and Risk Mitigation

Phased Deployment Strategy: Begin with highest-volume, most template-suitable workflows where AI provides clearest advantages. Expand gradually to more complex use cases as team proficiency develops. Maintain traditional workflow backup for mission-critical projects during transition period.

Change Management Requirements: Invest in comprehensive team training beyond vendor onboarding materials. Establish clear AI-human workflow protocols defining handoff points and quality standards. Create feedback loops for continuous workflow optimization based on actual usage patterns.

Contract and Vendor Management: Negotiate pilot pricing and exit clauses for initial implementation phases. Require performance guarantees for enterprise deployments with clearly defined success metrics. Establish vendor communication protocols for issue resolution and feature development feedback.

Ongoing Optimization Framework: Monitor adoption rates and productivity metrics monthly during initial deployment. Adjust workflow protocols based on team feedback and performance data. Plan technology refresh cycles accounting for rapid AI capability development and vendor feature evolution.

The AI mockup generation market offers genuine productivity advantages for properly matched use cases, but success requires realistic capability assessment, careful vendor selection, and structured implementation approaches. Organizations that align vendor capabilities with specific business needs while maintaining realistic expectations about AI's role in hybrid workflows achieve the strongest returns on their AI mockup generation investments.