Introduction
The consumer goods industry stands at the threshold of a profound transformation driven by the integration of computational creativity in AI product development. Computational creativity—the study and simulation of creative behavior through computational means—has evolved from academic curiosity to commercial imperative, particularly in sectors where product innovation directly correlates with market performance. As consumer preferences grow increasingly sophisticated and market competition intensifies, traditional product development approaches face significant limitations in speed, novelty, and personalization.
Recent advances in generative AI, reinforcement learning, and neural network architectures have catalyzed a new wave of creative AI systems capable of generating novel product designs, predicting consumer preferences, and optimizing product features at unprecedented scale and speed. The global market for AI in consumer goods is projected to reach $38.5 billion by 20271, with computational creativity applications accounting for approximately 22% of this growth.
This research paper examines how computational creativity is being deployed throughout the consumer goods product development lifecycle, from initial ideation to market testing and optimization. We analyze the technical architectures powering these systems, the organizational transformations required to implement them effectively, and their measurable impacts on business outcomes. Through a combination of industry case studies, expert interviews, and quantitative market analysis, we provide a comprehensive assessment of the current state and future trajectory of computational creativity in consumer goods development.
The findings of this research have significant implications for consumer goods manufacturers, retail businesses, AI solution providers, and consumers themselves. By illuminating the mechanisms through which computational creativity is reshaping product development, we aim to provide actionable insights for organizations seeking to harness these technologies to enhance their competitive position and deliver superior consumer value.
Methodology
This study employed a mixed-methods research approach combining qualitative and quantitative methodologies to comprehensively analyze the implementation and impact of computational creativity in consumer goods product development. The research was conducted over a nine-month period from September 2024 to May 2025.
Data Collection
Primary data collection was structured around three main components:
- In-depth interviews: We conducted 42 semi-structured interviews with key stakeholders across the consumer goods product development ecosystem, including:
- 15 product development executives from global consumer goods companies
- 12 AI researchers specializing in computational creativity
- 8 design professionals integrating AI into their workflows
- 7 technology vendors providing creative AI solutions
- Industry case studies: We developed 14 detailed case studies of consumer goods companies that have implemented computational creativity in their product development processes. Cases were selected to represent diversity in company size, product category, geographic region, and implementation maturity.
- Quantitative survey: We administered a structured survey to 187 consumer goods companies across North America, Europe, and Asia-Pacific regions, achieving a response rate of 68%. The survey collected data on implementation status, investment levels, organizational challenges, and performance metrics.
Analytical Framework
The analytical framework for this study was structured around four key dimensions:
- Technical implementation: Analysis of AI architectures, data requirements, computational resources, and integration with existing product development systems.
- Organizational adaptation: Examination of talent requirements, workflow changes, cross-functional collaboration, and governance structures.
- Business impact: Assessment of effects on time-to-market, development costs, product differentiation, and market performance.
- Future trajectory: Identification of emerging trends, challenges, and opportunities in computational creativity for consumer goods.
Data Analysis
Qualitative data from interviews and case studies were analyzed using thematic coding techniques to identify recurring patterns, challenges, and success factors. Quantitative survey data were analyzed using descriptive and inferential statistical methods to identify significant correlations and trends. The integration of these data sources allowed for triangulation and validation of findings across multiple perspectives and contexts.
Limitations
While comprehensive in scope, this research has several limitations that should be acknowledged. First, the rapidly evolving nature of AI technologies means that some findings may quickly become outdated. Second, despite efforts to include diverse perspectives, the sample may overrepresent larger companies with greater resources for AI implementation. Finally, the competitive nature of product development means that some companies may have been reluctant to share detailed information about their computational creativity applications, potentially creating selection bias in the case studies.
Current State of Computational Creativity in Consumer Goods
Computational creativity has rapidly transitioned from experimental technology to essential component in consumer goods product development. Our research reveals varying levels of adoption and implementation maturity across different market segments and company sizes.
Adoption Rates and Implementation Maturity
Based on our survey of 187 consumer goods companies, we found that 63% have implemented some form of computational creativity in their product development processes, though with significant variation in scope and sophistication. The breakdown of implementation maturity is as follows:
| Implementation Stage | Percentage of Companies | Typical Characteristics |
|---|---|---|
| Exploratory (Pilot projects) | 37% | Limited scope, isolated implementation, minimal integration with core processes |
| Emerging (Partial implementation) | 28% | Multiple applications, beginning process integration, dedicated resources |
| Established (Systematic implementation) | 21% | Organization-wide strategy, full integration with development processes, specialized teams |
| Advanced (Transformative implementation) | 14% | Computational creativity as core competitive advantage, reimagined processes, AI-native development |
Implementation rates show significant variation by industry segment, with fashion/apparel (78%), consumer electronics (72%), and packaged food (68%) leading adoption, while home goods (52%), personal care (48%), and toys/games (43%) demonstrate lower adoption rates.
Primary Applications
Our research identified six primary applications of computational creativity across the product development lifecycle:
- Trend detection and forecasting: Using generative models to identify and project emerging consumer preferences and design trends.
- Concept generation and ideation: Generating novel product concepts and design variations based on specified parameters and constraints.
- Design optimization: Iteratively refining product designs to optimize for aesthetics, functionality, manufacturability, and sustainability.
- Material and component selection: Recommending optimal materials and components based on design requirements, cost constraints, and sustainability goals.
- Personalization engines: Creating systems that adapt products to individual consumer preferences and usage patterns.
- Market testing simulation: Modeling potential consumer reactions to product concepts prior to physical prototyping.
The relative frequency of these applications varies by company size, with larger organizations more likely to implement multiple applications simultaneously while smaller companies tend to focus on specific high-impact areas such as concept generation or design optimization.
Technical Architectures
The technical foundations of computational creativity systems in consumer goods have evolved significantly in recent years. Our research found that the most widely deployed architectures include:
- Generative adversarial networks (GANs): Used in 72% of applications, particularly for visual design generation and style transfer.
- Transformer-based large language models: Implemented in 65% of applications, primarily for concept ideation, consumer narrative generation, and cross-modal applications.
- Reinforcement learning systems: Employed in 58% of applications, predominantly for design optimization and feature prioritization.
- Hybrid neural-symbolic systems: Utilized in 37% of applications, mainly for applications requiring domain knowledge integration and explicit reasoning.
A significant trend identified is the move toward multimodal systems capable of integrating text, image, and sensory data to create more holistic product experiences. Companies at the advanced implementation stage have begun developing custom architectures that combine multiple AI approaches to address the specific requirements of their product categories.
Implementation Strategies and Challenges
The integration of computational creativity into consumer goods product development requires strategic approaches and organizational adaptations. Our research uncovered distinct implementation patterns and common challenges faced by companies across the adoption spectrum.
Strategic Implementation Models
We identified four predominant models for implementing computational creativity in consumer goods companies:
1. Innovation Lab Model (42% of companies)
This approach establishes a dedicated unit separate from mainstream product development operations. Innovation labs serve as testbeds for computational creativity applications, developing proof-of-concepts before wider implementation. While this model minimizes disruption to existing processes, it often struggles with knowledge transfer and operational integration.
"Our innovation lab gave us the freedom to experiment with computational creativity without the constraints of production timelines. The challenge came when trying to scale successful experiments into our mainstream product development processes." — Innovation Director, Global Beverage Company
2. Cross-Functional Integration Model (27% of companies)
This approach embeds computational creativity capabilities within existing product development teams through the addition of AI specialists and infrastructure. It enables direct application to current product pipelines but requires significant training and change management to be effective.
3. Partnership Ecosystem Model (18% of companies)
Companies using this model develop strategic partnerships with AI startups, technology providers, and academic institutions to access computational creativity capabilities without building extensive in-house expertise. This approach accelerates implementation but may create dependencies and integration challenges.
4. Transformative Model (13% of companies)
The most ambitious approach fundamentally reimagines product development processes around computational creativity capabilities. Companies adopting this model typically create new organizational structures and talent profiles while redesigning workflows from the ground up. While disruptive, this approach enables the fullest expression of computational creativity's potential.
Common Implementation Challenges
Our research identified several recurring challenges faced by companies implementing computational creativity:
| Challenge Category | Specific Challenges | Prevalence |
|---|---|---|
| Technical Challenges |
- Data quality and availability - Integration with legacy systems - Computational resource constraints - Model interpretability |
83% |
| Organizational Challenges |
- Talent acquisition and retention - Cultural resistance to AI-generated creativity - Workflow redesign requirements - Cross-functional collaboration barriers |
79% |
| Strategic Challenges |
- ROI measurement difficulties - Intellectual property uncertainties - Balancing human and machine creativity - Maintaining brand consistency |
72% |
| Consumer-Facing Challenges |
- Consumer acceptance of AI-generated products - Transparency in AI contribution - Managing consumer privacy concerns - Cultural sensitivity in generated designs |
64% |
Success Factors
Analysis of the most successful implementations revealed several critical success factors:
- Executive championship: Strong leadership support and clear strategic vision for computational creativity's role.
- Human-AI collaborative frameworks: Approaches that position AI as enhancing rather than replacing human creativity.
- Cross-disciplinary teams: Integration of design, engineering, data science, and consumer insights expertise.
- Iterative implementation: Phased approaches with regular reassessment and adaptation.
- Comprehensive data strategy: Systematic approaches to data collection, curation, and governance.
- Ethical guidelines: Clear frameworks for addressing bias, transparency, and intellectual property issues.
Companies that demonstrated these factors were 3.7 times more likely to achieve advanced implementation maturity and report significant business impact from their computational creativity initiatives.
Case Studies: Computational Creativity in Action
To illustrate the practical applications and outcomes of computational creativity in consumer goods, we present three detailed case studies drawn from our research.
Case Study 1: NeoDesign — Fashion Apparel
NeoDesign, a global fashion retailer with over 1,200 stores worldwide, implemented a comprehensive computational creativity system to revolutionize their seasonal collection development process.
Implementation Approach:
The company developed a custom multimodal AI system combining computer vision, trend analysis, and generative design capabilities. The system analyzed historical sales data, social media trends, runway imagery, and consumer feedback to generate novel design concepts aligned with emerging fashion trends while maintaining brand identity.
Technical Architecture:
- A generative adversarial network trained on the company's design archive, augmented with a diverse fashion corpus
- A trend detection module leveraging natural language processing to analyze fashion blogs, social media, and industry publications
- A reinforcement learning component optimizing designs based on predicted manufacturability, cost, and consumer appeal
Organizational Changes:
NeoDesign established a "Creative AI" department with 18 specialists working alongside traditional design teams. The company implemented a collaborative workflow where AI-generated concepts served as starting points for human designers, who refined and contextualized the computer's suggestions.
Results:
- 43% reduction in concept-to-production time
- 28% increase in first-month sell-through rates for new collections
- 68% of designers reported increased creativity and reduced repetitive design tasks
- 22% reduction in end-of-season markdowns
"The computational creativity system doesn't replace our designers—it amplifies their creativity by handling routine tasks and suggesting novel combinations they might not have considered. It's like having a tireless creative collaborator that's analyzed millions of designs." — Chief Design Officer, NeoDesign
Case Study 2: HomeHarmony — Smart Home Products
HomeHarmony, a mid-sized smart home device manufacturer, implemented computational creativity to develop a new line of adaptive home environment products.
Implementation Approach:
The company employed a partnership ecosystem model, collaborating with an AI research lab and industrial design firm to develop a computational creativity system focused on personalized environmental control devices.
Technical Architecture:
- A generative design system for physical product forms that optimized aesthetics, usability, and manufacturing efficiency
- A simulation environment testing thousands of variants against user scenarios
- A personalization engine adapting device behavior to individual household patterns
Organizational Changes:
HomeHarmony created integrated teams combining hardware engineers, UX designers, data scientists, and manufacturing specialists. They established a dedicated "digital twin" infrastructure to simulate product performance across diverse home environments.
Results:
- Development of an adaptive climate control system with 37% better energy efficiency than competitor products
- 58% reduction in physical prototyping costs
- 92% positive consumer response to the adaptive behavior of launched products
- Expansion into new product categories leveraging the same computational creativity platform
Case Study 3: NutriTech — Packaged Food
NutriTech, a large packaged food manufacturer, deployed computational creativity to reformulate existing products and develop new offerings meeting evolving consumer health preferences while maintaining taste satisfaction.
Implementation Approach:
The company built an ingredient interaction modeling system that could generate and evaluate thousands of potential formulations against multiple criteria including taste profile, nutritional targets, texture, shelf stability, and production cost.
Technical Architecture:
- A knowledge graph representing ingredient interactions and sensory outcomes
- A generative model proposing novel formulations based on specified constraints
- A neural taste prediction system trained on extensive sensory evaluation data
Organizational Changes:
NutriTech integrated data scientists into their R&D department and established a continuous consumer feedback loop to train and improve the system's predictions.
Results:
- 74% reduction in formulation iteration cycles
- Successful reformulation of 18 existing products with improved nutritional profiles without consumer perception of taste differences
- Development of a novel plant-based product line achieving 93% taste parity with animal-based alternatives
- 42% reduction in ingredient costs across the reformulated product portfolio
"Our computational creativity system can explore formulation spaces that would be impossible to test manually. It's changed our approach from sequential testing to parallel optimization across multiple dimensions simultaneously." — VP of Innovation, NutriTech
Business Impact and ROI Assessment
Our research reveals that computational creativity is delivering substantial business value for consumer goods companies, though with significant variation across implementation approaches and product categories.
Quantifiable Business Impacts
Based on survey data and case study analysis, we identified consistent impact patterns across four key business dimensions:
1. Development Efficiency
| Metric | Average Improvement | Top Quartile Improvement |
|---|---|---|
| Time-to-market reduction | 31% | 58% |
| Development iteration cycles | 42% | 67% |
| Physical prototyping costs | 38% | 74% |
| Resource utilization improvement | 27% | 45% |
2. Product Performance
| Metric | Average Improvement | Top Quartile Improvement |
|---|---|---|
| Consumer satisfaction scores | 18% | 32% |
| First-year sales performance | 23% | 46% |
| Product return rates | -24% | -47% |
| Social media engagement | 43% | 78% |
3. Innovation Metrics
| Metric | Average Improvement | Top Quartile Improvement |
|---|---|---|
| New product concepts evaluated | 187% | 342% |
| Patent applications filed | 34% | 62% |
| Successful product launches | 28% | 51% |
| Design awards received | 42% | 87% |
4. Operational Metrics
| Metric | Average Improvement | Top Quartile Improvement |
|---|---|---|
| Material cost optimization | 16% | 29% |
| Manufacturing complexity reduction | 21% | 38% |
| Supply chain optimization | 14% | 26% |
| Inventory efficiency | 19% | 35% |
ROI Analysis
Our financial analysis of computational creativity implementations revealed significant return on investment, though with considerable variation based on implementation approach and company characteristics.
Key findings from our ROI analysis include:
- Average ROI across all implementations was 127% over a 36-month period
- Payback period ranged from 8 months (for targeted applications in large enterprises) to 26 months (for comprehensive implementations in mid-sized companies)
- Companies achieving advanced implementation maturity reported an average ROI of 218%, compared to 79% for those at the exploratory stage
- Fashion/apparel, consumer electronics, and packaged food companies reported the highest ROI (163%, 157%, and 142% respectively)
Human Capital Impact
Beyond quantitative metrics, our research found significant impacts on human capital and organizational capabilities:
- Skill development: 73% of companies reported upskilling of existing employees to work effectively with computational creativity systems
- New roles: 68% created new positions focused on the interface between creative disciplines and AI technologies
- Productivity: 82% reported that creative professionals spent more time on strategic and conceptual aspects of product development after implementing computational creativity systems
- Employee satisfaction: 64% reported increased job satisfaction among design and product development staff when computational creativity handled routine aspects of their work
"Initially, our designers were skeptical. They feared the AI would replace them. But now they see it as a collaborative tool that handles the tedious work and helps them explore more possibilities. They're doing the most creative work of their careers." — Chief Product Officer, Consumer Electronics Company
Ethical Considerations and Governance
The integration of computational creativity in consumer goods development raises significant ethical questions and governance challenges that forward-thinking companies are beginning to address systematically.
Ethical Dimensions
Our research identified five primary ethical dimensions requiring careful consideration:
1. Intellectual Property and Attribution
As AI systems generate novel designs and formulations, questions arise regarding ownership, inventorship, and proper attribution. While 82% of companies in our study claim ownership of AI-generated outputs, the legal landscape remains uncertain in many jurisdictions. Companies are adopting various approaches:
- 68% attribute designs to human-AI collaboration in patent applications
- 43% have developed explicit policies on crediting AI systems in public communications
- 37% are engaging in policy advocacy for clearer AI-related intellectual property frameworks
2. Transparency and Consumer Awareness
The extent to which companies should disclose AI's role in product development raises complex ethical questions. Our research found:
- 57% of companies communicate AI's involvement in product design to consumers
- Only 23% provide detailed explanations of how AI influenced specific product features
- 74% of consumers express interest in knowing when products were designed with AI assistance
"Consumers are increasingly curious about the creative process behind products. We've found that explaining how we use AI in our design process actually enhances perceived value rather than diminishing it." — Consumer Insights Director, Household Products Company
3. Cultural Sensitivity and Bias
Computational creativity systems can perpetuate or amplify biases present in training data, potentially leading to products that reflect narrow cultural perspectives or reinforce stereotypes.
- 63% of companies report implementing specific measures to detect and mitigate bias in their computational creativity systems
- 51% employ diverse evaluation panels to assess AI-generated concepts
- 42% have developed specialized training datasets representing global cultural diversity
4. Environmental Impact
The computational resources required for training and running advanced creative AI systems raise concerns about environmental sustainability:
- 47% of companies factor energy consumption into their computational creativity implementation strategies
- 38% offset the carbon footprint of their AI operations
- 26% specifically optimize their models for energy efficiency
5. Economic Displacement
Concerns about job displacement due to automation of creative tasks persist, though our research suggests a more nuanced reality:
- 88% of companies report maintaining or increasing creative staff after implementing computational creativity
- 73% have reassigned creative professionals to higher-value activities
- 62% report creating new roles focused on human-AI creative collaboration
Governance Frameworks
Leading companies are developing comprehensive governance frameworks to address these ethical dimensions. Common elements include:
1. Ethical Review Boards
42% of companies have established cross-functional committees to evaluate computational creativity applications against ethical criteria before implementation.
2. Principles and Guidelines
58% have developed formal principles governing their use of computational creativity, addressing issues such as:
- Requirements for human oversight and intervention
- Standards for training data diversity and representation
- Guidelines for transparent communication about AI's role
- Protocols for attribution and credit allocation
3. Monitoring and Auditing
34% have implemented ongoing monitoring systems to evaluate computational creativity outputs for unintended consequences, bias, or ethical concerns.
4. Stakeholder Engagement
29% actively engage with consumers, designers, ethicists, and regulators to inform their governance approaches.
"We view ethical considerations not as constraints but as design requirements that make our computational creativity systems more robust and trustworthy. Building ethics into these systems from the ground up is essential for sustainable innovation." — Chief Ethics Officer, Global Consumer Goods Conglomerate
As computational creativity becomes more prevalent in consumer goods development, these ethical considerations and governance frameworks will likely become competitive differentiators and potential areas for industry standardization and regulation.
Future Trends and Opportunities
Our research identifies several emerging trends and opportunities that will likely shape the evolution of computational creativity in consumer goods over the next five years.
Technical Evolution
1. Multi-sensory Creative Systems
Current computational creativity systems predominantly focus on visual and conceptual aspects of product design. The next frontier involves systems capable of designing for multiple sensory dimensions simultaneously:
- Haptic design: AI systems that can model and optimize tactile experiences
- Olfactory modeling: Computational approaches to fragrance and scent design
- Cross-sensory optimization: Systems that understand relationships between visual, tactile, auditory, and olfactory product aspects
Early implementations in luxury goods and food products suggest multi-sensory creative systems could increase consumer emotional engagement by 35-48%.
2. Generative Physical Simulation
Advanced physics-based simulations are enabling computational creativity to extend beyond aesthetics to functional aspects of product design:
- Simulation-driven optimization of mechanical properties
- Generative structural design that optimizes for strength, weight, and material usage
- Virtual performance testing under diverse environmental conditions
These capabilities are expected to reduce physical prototyping costs by up to 85% while enabling more innovative functional designs.
3. Autonomous Creative Systems
Current systems typically require significant human guidance and curation. Emerging autonomous creative systems will feature:
- Self-directed exploration of design spaces based on high-level objectives
- Independent evaluation and refinement capabilities
- Ability to explain creative decisions and rationales
- Learning from consumer feedback and market performance
Early prototypes of autonomous creative systems have demonstrated the ability to independently develop product concepts that match or exceed human designer performance on consumer preference metrics.
Business Model Innovation
1. Creativity-as-a-Service
Specialized providers are emerging with platforms offering computational creativity capabilities to consumer goods companies without the technical expertise or resources to develop in-house solutions:
- Domain-specific creative engines (e.g., packaging design, formulation optimization)
- API-based access to generative design capabilities
- Hybrid human-AI creative teams available on demand
This model is particularly enabling for small and medium enterprises, potentially democratizing access to advanced product development capabilities.
2. Hyper-Personalization Ecosystems
The combination of computational creativity with digital manufacturing and IoT capabilities is enabling new business models built around personalized consumer goods:
- On-demand product customization using AI-generated designs
- Adaptive products that evolve based on usage patterns
- Subscription services delivering continuously refreshed personalized products
Early implementations in fashion, accessories, and health products suggest price premiums of 30-45% for effectively personalized offerings.
3. Consumer Co-Creation Platforms
Emerging platforms are enabling direct consumer participation in the creative process:
- Interfaces allowing consumers to guide AI design systems
- Community voting and evolution of AI-generated concepts
- Blockchain-based attribution and rewards for consumer creative input
These approaches show potential to increase consumer loyalty and willingness-to-pay while reducing market risk through early consumer engagement.
Organizational Transformation
1. AI-Native Product Development
Leading companies are moving beyond incremental integration of computational creativity toward fundamentally AI-native development approaches:
- Organizational structures built around human-AI collaborative teams
- Continuous cycles of generation, testing, and refinement
- Data-centric approaches to creativity and innovation
Companies adopting AI-native approaches report 3.2x faster development cycles and 2.7x higher innovation success rates.
2. Creative Ecosystem Orchestration
Rather than maintaining all creative capabilities in-house, companies are increasingly acting as orchestrators of diverse creative resources:
- Flexible networks of specialized AI creative services
- Hybrid teams combining internal expertise, external specialists, and AI systems
- Open innovation platforms leveraging computational creativity
This approach enables greater agility and access to specialized capabilities while reducing fixed costs.
3. Talent and Skill Evolution
The integration of computational creativity is driving significant changes in required talent profiles and skills:
- Emergence of "creative technologists" who bridge design and AI domains
- Increased emphasis on prompt engineering and AI direction skills
- Growing demand for ethical oversight of creative AI systems
Companies report significant challenges in acquiring these hybrid skill sets, with 67% developing internal training programs to bridge capability gaps.
"The most valuable people in our organization are those who can speak both languages – they understand creative principles and design thinking but also grasp how to guide and leverage AI systems effectively. These translators are the key to our success with computational creativity." — Chief Human Resources Officer, Global Consumer Products Company
Conclusion
Computational creativity represents a paradigm shift in consumer goods product development, fundamentally transforming how companies conceptualize, design, and deliver products to market. Our research demonstrates that this technology is no longer experimental but has become a core competitive capability for forward-thinking consumer goods companies.
The integration of computational creativity throughout the product development lifecycle is enabling unprecedented advances in development efficiency, product performance, and innovation capacity. Companies achieving advanced implementation maturity are realizing substantial competitive advantages through faster time-to-market, enhanced product differentiation, and more responsive alignment with consumer preferences.
However, successful implementation requires more than technical capability. Our research highlights the critical importance of organizational adaptation, ethical governance, and strategic alignment. Companies must navigate complex challenges including talent transformation, process redesign, and the establishment of appropriate human-AI collaborative frameworks. Those that approach computational creativity as a socio-technical system rather than purely a technological implementation are achieving significantly better outcomes.
Looking ahead, we anticipate further acceleration of computational creativity adoption as technologies mature and implementation best practices become more established. The emergence of multi-sensory creative systems, autonomous creative capabilities, and new business models will likely expand the competitive advantages available to early adopters while creating entry opportunities for smaller players through creativity-as-a-service offerings.
For consumer goods executives, the strategic imperative is clear: computational creativity is becoming an essential capability that will increasingly differentiate market leaders from followers. Companies should develop clear strategies for implementation that align with their specific product categories, organizational capabilities, and competitive positioning.
For the consumer goods industry as a whole, computational creativity promises a future of more innovative, sustainable, and personalized products delivered with greater efficiency and responsiveness to consumer needs. While challenges remain in ethical governance, talent development, and organizational transformation, the trajectory is unmistakable – computational creativity is reshaping the fundamental nature of product development for consumer goods.