The hardwood flooring industry is undergoing its most significant transformation in decades. Artificial intelligence, machine learning, and neural networks—once confined to tech giants and research laboratories—are now reshaping every aspect of the flooring business, from manufacturing and quality control to customer service and installation planning. This isn’t science fiction or distant future speculation; it’s happening right now, and businesses that embrace these technologies are gaining decisive competitive advantages.
For flooring professionals, contractors, manufacturers, and retailers, understanding how neural networks can enhance operations isn’t optional anymore—it’s essential for survival in an increasingly competitive marketplace. This comprehensive guide explores the practical applications of advanced AI technologies in the hardwood flooring sector, explaining complex concepts in accessible terms and demonstrating real-world business value without requiring technical programming knowledge.
Whether you’re a flooring company owner curious about automation opportunities, a contractor seeking efficiency improvements, or an industry professional wanting to stay ahead of emerging trends, this article will illuminate how neural networks are creating new possibilities throughout the flooring value chain.
Understanding Neural Networks: The Brain Behind Modern AI
Before diving into specific flooring applications, it’s helpful to understand what neural networks actually are and why they’re so powerful for solving industry challenges.
What Are Neural Networks?
Neural networks are computer systems loosely modeled after the human brain’s structure and function. Just as our brains contain billions of interconnected neurons that process information and learn from experience, artificial neural networks consist of layers of mathematical nodes that identify patterns in data and make intelligent decisions.
The “learning” process happens through training, where the network analyzes thousands or millions of examples, gradually adjusting its internal parameters to improve accuracy. For instance, a neural network trained to recognize oak flooring would examine countless oak floor images, learning the distinctive grain patterns, color variations, and texture characteristics that define this wood species.
Why Neural Networks Excel at Flooring Applications
Traditional computer programs follow rigid rules defined by programmers. If you wanted software to identify floor scratches, you’d need to explicitly program every characteristic: “A scratch is a line-shaped mark measuring X pixels wide with Y color difference from surrounding wood.” This approach breaks down quickly when dealing with the infinite variations found in real-world flooring scenarios.
Neural networks take a fundamentally different approach. Rather than following predefined rules, they learn patterns directly from examples. Show a neural network 10,000 images of scratched floors, and it will automatically discover the visual patterns that characterize scratches—including subtle variations that human programmers might never consider. This learning capability makes neural networks exceptionally well-suited for the complex, variable conditions encountered in flooring applications.
Types of Neural Networks Used in Flooring
Different neural network architectures excel at different tasks within the flooring industry:
Convolutional Neural Networks (CNNs) specialize in visual analysis, making them ideal for examining floor images, detecting defects, identifying wood species, and analyzing installation quality. These networks process images through multiple layers, starting with simple features like edges and textures, then building up to complex pattern recognition like specific defect types or finish qualities.
Recurrent Neural Networks (RNNs) excel at analyzing sequential data and time-series information. In flooring applications, RNNs can predict floor lifespan based on historical wear patterns, forecast maintenance requirements, or analyze customer inquiry patterns to optimize staffing and inventory.
Generative Adversarial Networks (GANs) create new, realistic data by learning from existing examples. For flooring businesses, GANs can generate photorealistic floor visualizations, create virtual showroom environments, or produce training data for other AI systems when real examples are scarce.
Transformer Networks have revolutionized natural language processing and are increasingly used in customer service applications. These networks power intelligent chatbots that answer flooring questions, analyze customer reviews to identify common issues, and generate product descriptions or marketing content.
Transforming Floor Manufacturing with Intelligent Automation
Manufacturing represents one of the most immediately impactful areas for neural network deployment in the flooring industry. The combination of high production volumes, stringent quality requirements, and repetitive processes creates ideal conditions for AI-powered optimization.
Real-Time Quality Inspection Systems
Traditional manufacturing quality control relies on human inspectors examining boards or planks at various production stages. This approach has inherent limitations: human attention wavers, subjective judgment varies between inspectors, and comprehensive inspection of every product slows production lines to impractical speeds.
Neural network-based inspection systems eliminate these constraints. High-resolution cameras capture images of each board as it moves through production, while trained neural networks analyze these images in milliseconds, identifying finish defects, color inconsistencies, dimensional variations, knot patterns exceeding specifications, or surface damage.
The business impact is substantial. Leading flooring manufacturers implementing AI inspection systems report defect detection rates exceeding 99%, compared to 85-90% for manual inspection. False positive rates—incorrectly flagging good products as defective—drop dramatically, reducing waste and improving profitability. Production line speeds increase by 30-50% since AI inspection doesn’t require the physical board handling and visual examination time needed for human inspectors.
Predictive Maintenance for Production Equipment
Manufacturing equipment failures cause costly downtime, missed delivery deadlines, and expensive emergency repairs. Neural networks are transforming equipment maintenance from reactive problem-solving to proactive prevention.
By analyzing sensor data from machinery—vibration patterns, temperature fluctuations, power consumption, acoustic signatures, and operational parameters—neural networks learn to recognize the subtle patterns that precede equipment failures. These systems can predict bearing failures days in advance, identify developing motor issues before they cause breakdowns, and recommend optimal maintenance timing that maximizes equipment life while minimizing production disruption.
One major flooring manufacturer reported a 40% reduction in unplanned downtime after implementing neural network-based predictive maintenance, translating to millions of dollars in avoided lost production and emergency repair costs annually.
Optimizing Production Scheduling and Inventory
Neural networks excel at solving complex optimization problems with numerous variables and constraints. In flooring manufacturing, production scheduling involves balancing customer orders of different product types, machine changeover times, raw material availability, seasonal demand fluctuations, and workforce scheduling.
Advanced neural networks can analyze historical production data, current order books, supply chain information, and market trends to generate optimal production schedules that minimize changeover waste, reduce inventory carrying costs, and improve on-time delivery rates. Some systems continuously learn from outcomes, refining scheduling algorithms as they accumulate more operational experience.
Revolutionizing Customer Experience and Sales
The customer journey in flooring—from initial product research through purchase decision and post-installation support—offers numerous opportunities for neural network enhancement that improve satisfaction while reducing business costs.
Intelligent Product Recommendation Systems
Choosing the right flooring involves weighing dozens of factors: room usage patterns, traffic levels, aesthetic preferences, existing décor, budget constraints, maintenance willingness, environmental conditions, and more. This complexity overwhelms many customers and leads to extended decision cycles or suboptimal choices.
Neural network-powered recommendation engines analyze customer inputs, past purchase patterns, successful installations in similar situations, and current inventory to suggest optimal flooring options. These systems learn from thousands of previous customer interactions, identifying patterns between customer requirements and satisfactory outcomes that even experienced salespeople might miss.
Advanced implementations incorporate visual preference learning, where customers browse floor images and the system learns their aesthetic preferences through their selections, automatically refining recommendations to match discovered taste patterns. This approach reduces decision fatigue and accelerates purchase decisions while improving customer satisfaction with final selections.
Virtual Floor Visualization and Augmented Reality
One of the biggest hurdles in flooring sales is helping customers visualize how products will look in their actual spaces. Small showroom samples provide limited perspective, and customers struggle imagining how colors, grain patterns, and plank widths will appear covering entire rooms.
Neural networks enable sophisticated virtual visualization solutions. Customers photograph their rooms using smartphones, and AI systems automatically segment the image, identifying existing flooring areas and mapping new flooring products onto those surfaces with realistic lighting, perspective, and texture rendering. The results are photorealistic previews showing exactly how different products will appear in the customer’s actual space.
These visualization tools dramatically increase conversion rates—retailers report 30-40% higher close rates when customers use AR visualization before purchase—while reducing returns caused by appearance mismatches between customer expectations and actual installation results.
Intelligent Customer Service and Support
Neural network-powered chatbots and virtual assistants are transforming flooring customer service. Unlike traditional scripted chatbots that frustrate customers with rigid, limited responses, modern neural language models understand natural conversational queries and provide helpful, contextual answers.
A customer can ask complex questions like “What’s the best floor for a kitchen with three kids and two large dogs, and I want something that looks like hardwood but is waterproof and under $5 per square foot?” The AI assistant understands the multiple constraints, searches the product database, considers installation requirements, and provides personalized recommendations with explanations.
These systems handle routine inquiries 24/7, freeing human staff to focus on complex situations requiring personal attention. Flooring retailers implementing AI customer service report 50-70% reductions in routine inquiry handling costs while improving response times and customer satisfaction scores.
Enhancing Installation Planning and Project Management
Professional flooring installation involves careful planning, precise execution, and ongoing project coordination. Neural networks are making installation processes more efficient, predictable, and profitable.
Automated Material Estimation and Waste Reduction
Accurate material estimation is crucial for project profitability. Overestimating wastes money on excess materials, while underestimating causes costly project delays and additional delivery charges. Traditional estimation relies on installers applying rule-of-thumb waste percentages to measured square footage.
Neural networks trained on thousands of completed projects can generate more accurate estimates by analyzing room dimensions, layout complexity, pattern specifications, installer experience levels, and material characteristics. These systems learn that complex room geometries require more waste allowance, certain installers consistently waste less material, and specific products generate predictable waste patterns.
The result is optimized material orders that minimize both shortage and excess scenarios, improving project margins and reducing environmental impact from unnecessary material consumption.
Installation Quality Prediction and Risk Assessment
Not all installation projects proceed smoothly. Subfloor conditions, environmental factors, material variations, and installation complexity can create challenges that delay completion or compromise quality. Neural networks can assess project risk before work begins.
By analyzing project characteristics—subfloor type and condition, room moisture levels, installation method, product specifications, timeline constraints, and installer qualifications—AI systems can predict potential problems and recommend preventive measures. High-risk projects receive additional planning attention, specialized installer assignment, or pre-emptive issue resolution, reducing callbacks, warranty claims, and customer dissatisfaction.
Intelligent Scheduling and Route Optimization
For installation companies managing multiple concurrent projects, efficient scheduling and installer routing significantly impact profitability. Neural networks can optimize these complex logistics problems, considering installer locations, skill sets, and availability alongside project timelines, geographic distribution, traffic patterns, and customer preferences.
These systems learn from historical data, identifying patterns like which installers work most efficiently together, which times of day yield fastest installation speeds in different neighborhoods, and how weather conditions affect project timelines. Optimized schedules reduce installer drive time, improve crew utilization, and increase completed installations per day.
Predicting Floor Performance and Lifespan
One of the most valuable applications of neural networks in flooring involves predicting how floors will perform over time under various conditions. This predictive capability benefits manufacturers, installers, and end customers.
Lifespan Prediction Based on Usage Patterns
How long will a hardwood floor last in a busy restaurant versus a quiet bedroom? Traditional answers rely on generic estimates that often prove inaccurate. Neural networks can provide much more precise predictions by analyzing the complex interplay of factors affecting floor lifespan.
These systems learn from historical data covering thousands of floor installations, their usage conditions, maintenance practices, environmental factors, and eventual replacement needs. By identifying patterns in this data, neural networks can predict expected lifespan for specific floor types in particular environments with remarkable accuracy.
This capability enables better product selection recommendations, more accurate warranty cost forecasting for manufacturers, and realistic maintenance budget planning for facility managers. Customers make more informed decisions when they understand true expected lifespan rather than optimistic marketing claims.
Predictive Maintenance for Installed Floors
Proactive floor maintenance extends lifespan and prevents minor issues from becoming major problems. Neural networks can analyze floor condition data—from visual inspections, wear pattern monitoring, or embedded sensors—to predict when maintenance interventions should occur.
For commercial installations, these systems might recommend specific cleaning protocol adjustments based on detected wear patterns, suggest recoating timing optimized for traffic schedules, or alert facility managers to developing problems requiring immediate attention. This intelligence transforms maintenance from calendar-based routines to condition-based optimization that maximizes floor investment value.
Environmental Impact Prediction
Sustainability increasingly influences flooring decisions. Neural networks can predict environmental impacts of different flooring choices by analyzing manufacturing data, transportation distances, installation requirements, maintenance needs, expected lifespan, and end-of-life disposal or recycling options.
These comprehensive analyses help environmentally conscious customers make informed decisions balancing aesthetic preferences, functional requirements, budget constraints, and sustainability goals. Flooring companies can also use these predictions to identify opportunities for reducing their environmental footprint throughout the product lifecycle.
Advancing Wood Science and Product Development
Neural networks are accelerating flooring product innovation by enabling more sophisticated analysis of wood properties and performance characteristics.
Wood Species Identification and Classification
Accurate wood species identification ensures regulatory compliance, verifies sustainable sourcing claims, and maintains product quality standards. Traditionally requiring expert examination and sometimes laboratory testing, species identification can now be automated using neural networks trained on images and spectroscopic data from thousands of wood samples.
These systems identify species from photographs or microscopic images, analyze grain patterns and color characteristics, and even detect common wood species fraud like substituting less valuable species for premium ones. The technology supports supply chain integrity and helps combat illegal logging by verifying wood origin claims.
Finish Performance Optimization
Floor finish performance depends on complex chemical interactions between finish formulations and wood substrates, influenced by application methods, environmental conditions, and usage patterns. Neural networks can analyze vast experimental datasets to identify optimal finish formulations for specific applications.
By learning from laboratory testing, accelerated aging studies, and real-world performance data, these systems suggest formulation adjustments that improve durability, reduce VOC emissions, enhance appearance, or achieve other performance goals. This AI-assisted research accelerates product development cycles and reduces the expensive trial-and-error historically required for finish optimization.
Biomimetic Design and Novel Materials
Nature has evolved countless solutions to material challenges over millions of years. Neural networks can analyze biological structures and properties, identifying design principles applicable to engineered flooring materials.
These systems might discover that certain tree bark structures provide inspiration for more scratch-resistant surfaces, or that specific wood grain patterns correlate with superior dimensional stability. By connecting biological data with material performance characteristics, neural networks help researchers develop innovative flooring products that combine natural aesthetics with enhanced performance.
Transforming Supply Chain and Inventory Management
Efficient supply chain management is crucial for flooring business profitability. Neural networks optimize inventory, logistics, and purchasing decisions across the distribution network.
Demand Forecasting
Accurate demand forecasting prevents the twin problems of stockouts that lose sales and excess inventory that ties up capital. Neural networks analyze historical sales data, seasonal patterns, economic indicators, housing market trends, design trends from social media, and countless other factors to predict future demand with unprecedented accuracy.
These systems identify subtle patterns like how weather patterns affect flooring purchases, how home improvement TV shows influence product preferences, or how local economic conditions impact upgrade versus budget product mix. More accurate forecasts enable optimal inventory positioning, reducing carrying costs while improving product availability.
Dynamic Pricing Optimization
Flooring pricing involves balancing competitive pressures, inventory levels, seasonal demand fluctuations, supplier costs, and profitability goals. Neural networks can optimize pricing dynamically, adjusting recommendations based on current market conditions.
These systems might suggest modest price increases when demand exceeds supply, recommend promotional pricing to move slow-moving inventory, or identify opportunities where slight price adjustments significantly impact sales volume. The result is optimized revenue and margin performance across product lines and market segments.
Supplier Risk Assessment
Supply chain disruptions can cripple flooring businesses. Neural networks help identify and mitigate supplier risks by analyzing diverse data sources including financial reports, shipping performance, geopolitical conditions, weather patterns, and social media sentiment.
Early warning of potential supplier problems enables proactive sourcing adjustments, preventing stockouts that damage customer relationships. This risk intelligence is particularly valuable in industries like flooring that rely on complex global supply chains.
Enhancing Safety and Compliance
Safety and regulatory compliance are critical concerns in flooring manufacturing and installation. Neural networks help businesses meet these obligations more effectively.
Workplace Safety Monitoring
Manufacturing environments present various safety hazards. Neural networks analyzing video feeds from facility cameras can identify unsafe behaviors or conditions in real-time—workers not wearing required protective equipment, unsafe material handling practices, equipment operating outside normal parameters, or developing hazardous situations.
Unlike human monitors who can’t watch everywhere simultaneously, AI systems provide continuous comprehensive monitoring, alerting supervisors to safety issues requiring intervention. Companies implementing AI safety monitoring report significant reductions in workplace accidents and insurance costs.
Regulatory Compliance Verification
Flooring products must comply with numerous regulations covering formaldehyde emissions, VOC content, fire ratings, sustainability certifications, and accessibility requirements. Neural networks can automatically verify compliance by analyzing product specifications, manufacturing processes, and testing data.
These systems flag potential compliance issues before products reach market, reducing costly recalls and regulatory violations. They also help identify regulatory changes affecting products, alerting manufacturers to needed formulation or process adjustments.
Installation Code Compliance
Building codes specify requirements for flooring installations including moisture barriers, subfloor preparation, fastening patterns, and expansion gaps. Neural networks trained on code requirements and installation best practices can review installation plans, identifying potential code violations before work begins.
This proactive compliance verification reduces failed inspections, callbacks, and liability exposure while ensuring installations meet required standards. Mobile applications using neural networks can even analyze installation photos during work, providing real-time compliance guidance to installers.
The Business Case: ROI and Implementation Considerations
Understanding the theoretical capabilities of neural networks is valuable, but business decisions ultimately depend on practical implementation considerations and return on investment.
Quantifying Expected Benefits
Neural network implementations in flooring businesses typically deliver returns through several mechanisms:
Cost Reduction: Automated inspection replaces manual labor, predictive maintenance reduces emergency repairs, optimized scheduling reduces installer drive time, and improved forecasting minimizes excess inventory. Companies often see 20-40% operational cost reductions in automated processes.
Revenue Enhancement: Better product recommendations increase average transaction values, virtual visualization tools improve close rates, reduced lead times capture time-sensitive sales, and improved quality reduces warranty costs. Revenue improvements of 10-25% are common in customer-facing applications.
Quality Improvement: Defect detection rates improve, customer satisfaction increases, warranty claims decrease, and brand reputation strengthens. These quality benefits compound over time as positive customer experiences generate referrals and repeat business.
Competitive Advantage: Early adopters establish technological leadership positions, attract customers seeking innovative partners, and develop capabilities that create barriers to competition. These strategic benefits are difficult to quantify but potentially most valuable long-term.
Implementation Pathways
Successful neural network adoption in flooring businesses typically follows staged approaches rather than attempting wholesale transformation:
Pilot Projects: Start with focused applications addressing specific pain points—perhaps automated defect detection in one product line or AI-powered customer service for common inquiries. Pilot projects minimize risk while demonstrating value and building organizational capability.
Incremental Expansion: After successful pilots, gradually expand to additional processes or departments. This staged approach allows learning from early implementation experiences, building internal expertise, and managing change effectively.
Partnership Models: Many flooring businesses achieve successful neural network adoption through partnerships with technology providers rather than in-house development. Specialized vendors offer industry-specific AI solutions as subscription services, eliminating large upfront investments and ongoing technical maintenance burdens.
Hybrid Approaches: Effective implementations often combine AI capabilities with human expertise rather than attempting full automation. Neural networks handle high-volume routine tasks while escalating complex situations to human experts. This hybrid model delivers efficiency benefits while maintaining quality and flexibility.
Overcoming Implementation Challenges
Despite compelling benefits, neural network adoption faces obstacles that successful implementations address proactively:
Data Requirements: Neural networks require substantial training data. Businesses may need to systematically collect data for months before implementation, or use transfer learning techniques that require less company-specific data.
Change Management: Employees may fear job displacement or resist new technologies. Successful implementations emphasize how AI augments rather than replaces human capabilities, involve employees in implementation planning, and provide training that builds confidence with new tools.
Technical Complexity: While using trained neural networks is increasingly user-friendly, initial development requires specialized expertise. Partnerships with technology providers or consultants help navigate technical challenges.
Cost Considerations: Initial investments can be substantial, though decreasing rapidly. Clear ROI projections, staged implementation approaches, and subscription-based solutions help manage financial constraints.
Future Horizons: What’s Next for AI in Flooring
The neural network applications discussed above represent current capabilities, but rapid ongoing development promises even more transformative changes ahead.
Integrated Intelligence Ecosystems
Future flooring systems will integrate neural networks throughout the value chain, creating seamless intelligence ecosystems. A customer’s product selection made through AI-powered recommendation engines automatically triggers optimized production scheduling, intelligent inventory allocation, coordinated logistics planning, and scheduled installation—all coordinated by communicating AI systems with minimal human intervention.
These integrated systems will optimize holistically across the entire business operation rather than just individual functions, unlocking efficiency gains impossible through isolated optimizations.
Edge AI and Real-Time Processing
Current neural network applications often require sending data to cloud servers for processing. Emerging “edge AI” technologies enable neural networks to run directly on devices—smartphones, cameras, sensors, or dedicated processors—providing instant results without internet connectivity requirements.
For flooring applications, edge AI enables real-time installation guidance viewed through installer AR glasses, instant defect detection on battery-powered inspection devices, or immediate customer visualization on smartphones without network delays.
Generative AI and Creative Applications
Generative neural networks like GPT and DALL-E demonstrate remarkable creative capabilities. Future flooring applications might include AI systems that design custom floor patterns based on room characteristics and customer preferences, generate marketing content and product descriptions automatically, or create photorealistic renders of proposed installations indistinguishable from actual photographs.
Sustainability Optimization
Climate concerns are driving demand for sustainable flooring solutions. Future neural networks will optimize products and processes for environmental impact, identifying opportunities to reduce carbon footprints, suggesting sustainable material substitutions, designing products for circular economy recyclability, and helping customers make environmentally informed choices.
Autonomous Systems
Robotics combined with neural network intelligence will enable increasingly autonomous flooring operations. Imagine installation robots that autonomously position and secure planks with superhuman precision, or warehouse robots that handle flooring materials and fulfill orders without human intervention.
While full autonomy remains years away, incremental progress toward autonomous operations will steadily improve efficiency, consistency, and safety.
Taking Action: How to Begin Your AI Journey
For flooring businesses ready to explore neural network opportunities, a structured approach maximizes success probability while managing risks.
Assessment and Strategy Development
Begin by thoroughly assessing your business operations, identifying processes where neural networks could deliver meaningful value. Consider factors like current pain points, data availability, potential ROI, strategic importance, and implementation feasibility.
Develop a clear strategy that prioritizes opportunities, establishes success metrics, allocates resources, and creates realistic timelines. This strategic foundation guides implementation decisions and helps secure organizational commitment.
Education and Capability Building
Invest in education for key personnel who will interact with neural network systems. While detailed technical knowledge isn’t necessary for most users, understanding capabilities, limitations, and appropriate applications ensures effective utilization.
Consider training programs, industry conferences, technology vendor demonstrations, and peer networking to build organizational AI literacy. Some forward-thinking flooring businesses are hiring data scientists or AI specialists to build internal capabilities.
Pilot Project Selection
Choose initial pilot projects carefully based on clear success criteria, manageable scope, strong business case, and reasonable data availability. Successful pilots build momentum for broader adoption, while failed pilots can undermine confidence in the technology.
Document pilot results rigorously, capturing both quantitative metrics and qualitative feedback. These learnings guide subsequent implementation decisions and help communicate value to stakeholders.
Vendor Selection and Partnership
Most flooring businesses will partner with technology providers rather than developing neural network systems internally. Evaluate potential partners based on industry expertise, technical capabilities, implementation support, pricing models, and reference customer success.
Strong partnerships provide not just technology but also guidance on best practices, ongoing support, and continuous improvement as capabilities evolve. View vendor relationships as strategic partnerships rather than transactional technology purchases.
Continuous Improvement Mindset
Neural network systems improve over time as they accumulate more data and learn from outcomes. Establish processes for monitoring performance, collecting feedback, and refining models. This continuous improvement approach maximizes long-term value from AI investments.
Stay informed about emerging capabilities and applications through industry publications, technology conferences, and peer networks. The AI field evolves rapidly, and maintaining awareness of new developments helps identify opportunities for competitive advantage.
Conclusion: Embracing the Intelligent Future of Flooring
Neural networks represent the most significant technological advancement in the flooring industry since power tools revolutionized installation or polyurethane finishes transformed floor protection. The technology has matured beyond experimental novelty to practical business tool delivering measurable value across manufacturing, sales, installation, and service operations.
The competitive landscape is shifting rapidly. Early adopters are establishing advantages that will compound over time, while businesses delaying engagement risk falling behind permanently. The good news is that implementation has become increasingly accessible—subscription services, turnkey solutions, and vendor partnerships enable businesses of all sizes to access sophisticated AI capabilities without massive investments or technical expertise.
Success requires moving beyond passive observation to active experimentation. Start small if necessary, but start soon. Identify a specific application where neural networks could address a genuine business challenge, explore available solutions, and implement a focused pilot project. Learn from results, refine your approach, and gradually expand adoption based on demonstrated value.
The flooring industry’s future will be shaped by intelligent systems that enhance human capabilities, optimize operations, and deliver superior customer experiences. Businesses that embrace this transformation position themselves for sustained success, while those clinging to purely traditional approaches face increasing disadvantage in an evolving marketplace.
The question isn’t whether neural networks will transform your flooring business—it’s whether you’ll lead that transformation or scramble to catch up as competitors pull ahead. The choice, and the opportunity, is yours.
