The Shopper Has No Eyes: Branding in a World Without Human Perception
Author: Paul F. Accornero
ORCID: https://orcid.org/0009-0009-2567-5155
SSRN: Paper Under Review
Affiliation: The AI Praxis
Date: September 16, 2025
Abstract
Branding has historically relied on sensory appeal and emotional resonance to influence human consumers. However, the emergence of AI agents that execute purchasing decisions autonomously presents fundamental challenges to traditional branding paradigms. This paper introduces the concept of "The Shopper Has No Eyes," a theoretical framework for understanding how branding must evolve when decision-makers lack human sensory and emotional capabilities. Through synthesis of empirical research on AI agent behavior and established branding theory, we propose that brand value is shifting from emotional persuasion to verifiable data transparency. We introduce the "Great Value Sort" as a market realignment mechanism where algorithmic evaluation redistributes market share based on objective performance metrics rather than perceived brand equity. Drawing on platform economics and signaling theory, we develop a conceptual framework for data-driven brand management and explore its implications for competitive strategy. This research contributes to understanding how fundamental assumptions about consumer behavior and brand building may need revision in an era of AI-mediated commerce.
1. Introduction
Branding has evolved over more than a century into a sophisticated discipline focused on building brand equity through carefully orchestrated sensory and emotional appeals (Aaker, 1991; Keller, 1993). The traditional branding paradigm assumes human decision-makers who process visual, auditory, and emotional information to form brand preferences and guide purchasing decisions. This approach leverages what Kahneman (2011) describes as "System 1" thinking—fast, intuitive cognition that relies heavily on heuristics and emotional shortcuts.
However, the emergence of AI agents as autonomous purchasing decision-makers presents a fundamental challenge to this paradigm. These algorithmic systems, which are beginning to execute an increasing proportion of commercial transactions, do not possess the sensory or emotional faculties that traditional branding targets (Forrester, 2025; Gaarlandt et al., 2025). An algorithm cannot be influenced by visual aesthetics, moved by emotional narratives, or persuaded by sensory experiences that form the foundation of traditional brand building.
This development raises critical questions about the future of branding strategy. If the decision-maker lacks human perceptual capabilities, how should organizations approach brand building and management? What types of signals matter to algorithmic decision-makers, and how can brands create competitive advantage in an environment where traditional emotional appeals may be ineffective?
This paper introduces the concept of "The Shopper Has No Eyes" as a theoretical framework for understanding this transition. We argue that this shift represents a fundamental transformation from emotional persuasion to data-driven verification in brand strategy. We also introduce the concept of the "Great Value Sort"—a market realignment process where algorithmic evaluation redistributes market share based on objective performance metrics rather than traditional brand equity.
Our contribution is threefold: First, we establish the theoretical foundation for understanding how AI-mediated commerce challenges traditional branding assumptions. Second, we develop a conceptual framework for data-driven brand management that addresses the requirements of algorithmic decision-makers. Third, we explore the implications of this shift for competitive strategy and organizational capabilities.
2. Literature Review
2.1 Traditional Branding Theory and Human Psychology
Traditional branding theory rests on the premise that brands serve as information shortcuts that reduce cognitive effort and uncertainty in consumer decision-making (Bettman et al., 1998). Brands function as signals that communicate quality, reliability, and other attributes that may be difficult to assess directly (Spence, 1973; Milgrom & Roberts, 1986).
The effectiveness of traditional branding stems from its alignment with human cognitive architecture. Brand building leverages psychological principles such as classical conditioning, social proof, and emotional association to create what researchers term "mental availability" (Sharp, 2010; Romaniuk & Sharp, 2016). This approach recognizes that human decision-making is often driven by emotions and heuristics rather than systematic evaluation of objective attributes (Bettman et al., 1998).
Color psychology, visual design, and sensory marketing have become sophisticated tools for influencing consumer perceptions and preferences (Lindstrom, 2005; Krishna, 2012). Research demonstrates that sensory elements can significantly influence brand perception, purchase intention, and customer loyalty through both conscious and unconscious mechanisms (Hultén et al., 2009).
2.2 Platform Economics and Algorithmic Mediation
The rise of digital platforms has already begun altering traditional branding dynamics by introducing algorithmic intermediation between brands and consumers (Parker et al., 2016; Evans & Schmalensee, 2016). Platform algorithms determine product visibility and recommendation patterns, creating new forms of competitive advantage that may differ from traditional brand equity.
Research on platform-mediated commerce reveals that visibility and sales performance increasingly depend on algorithmic factors such as search ranking, recommendation algorithms, and automated matching systems (Chen et al., 2016; Tadelis, 2016). These systems operate according to programmed logic rather than emotional or aesthetic considerations, suggesting that optimization strategies may need to evolve accordingly.
Platform ecosystems also create new forms of competitive dynamics where success depends on understanding and optimizing for algorithmic decision-making processes rather than traditional consumer psychology (Zuboff, 2019). This trend toward algorithmic mediation provides early evidence of how AI systems may reshape brand strategy.
2.3 AI Decision-Making and Commercial Applications
Recent research has begun documenting how AI systems make commercial decisions and how these patterns differ from human behavior. Allouah et al. (2025) provide empirical evidence that AI shopping agents exhibit systematically different purchasing behaviors, prioritizing platform endorsements while discounting traditional advertising signals.
This research reveals that AI agents operate according to different evaluation criteria than human consumers. Where humans may be influenced by emotional appeals, social proof, and aesthetic considerations, AI agents appear to prioritize objective data, verifiable metrics, and structured information (Russell & Norvig, 2020).
The implications extend beyond individual purchasing decisions to broader market dynamics. If AI agents consistently favor certain types of products or evaluation criteria, this could lead to systematic changes in market share distribution and competitive advantage (Brynjolfsson & McAfee, 2014).
2.4 Signaling Theory and Information Asymmetry
Signaling theory provides a theoretical foundation for understanding how brand communication may need to evolve for AI audiences (Spence, 1973; Connelly et al., 2011). In traditional consumer markets, brands signal quality and other attributes through emotional and aesthetic cues that resonate with human psychology.
However, AI agents may require different types of signals that are more easily verifiable and less subject to manipulation. This suggests a shift from subjective signaling (emotional appeals, aesthetic design) to objective signaling (performance data, certifications, measurable attributes) as the primary means of brand communication.
The challenge of information asymmetry becomes more complex when the signal receiver (AI agent) operates according to different evaluation criteria than the signal sender was designed to address (human consumers). This mismatch may require fundamental changes in how organizations approach brand signaling and communication.
3. Methodology
This paper employs conceptual framework development through synthesis of empirical research on AI agent behavior, established branding theory, and platform economics literature. Our approach follows established methodologies for theoretical development in marketing research (MacInnis, 2011; Yadav, 2010).
We draw on three primary sources of evidence: recent empirical research demonstrating systematic differences in AI agent purchasing behavior (Allouah et al., 2025), established theoretical frameworks from branding and signaling theory, and industry analysis of platform-mediated commerce trends.
Our analysis focuses on identifying the fundamental assumptions underlying traditional branding approaches and examining how these assumptions may be challenged by AI-mediated decision-making. We develop conceptual frameworks for understanding both the challenges and opportunities presented by this transition.
The resulting theoretical framework represents an extension of existing branding and signaling theory into the domain of AI-mediated commerce. While this approach limits our ability to provide empirical validation, it enables development of conceptual foundations for future research and practical application.
4. Theoretical Framework: The Shopper Has No Eyes
The concept of "The Shopper Has No Eyes" represents a fundamental shift in the assumptions underlying brand strategy. Traditional branding operates on the premise that decision-makers process visual, emotional, and sensory information to form preferences and guide behavior. This assumption becomes problematic when the decision-maker is an AI agent that lacks these perceptual capabilities.
4.1 From Sensory Appeal to Data Transparency
Traditional brand building relies heavily on sensory and emotional appeals designed to influence human psychology. Visual elements such as logos, colors, and packaging design communicate brand attributes and create emotional associations (Henderson & Cote, 1998; Labrecque & Milne, 2012). These approaches assume that decision-makers will be influenced by aesthetic and emotional considerations.
AI agents, however, appear to operate according to different evaluation criteria. Rather than being influenced by visual aesthetics or emotional narratives, they prioritize structured data, verifiable metrics, and objective performance indicators. This suggests a fundamental shift from sensory appeal to data transparency as the primary mechanism for brand communication.
This transition can be understood as a move from subjective to objective signaling. Traditional brands signal quality through emotional associations and aesthetic design. In contrast, brands targeting AI agents may need to signal quality through verifiable data, third-party certifications, and measurable performance metrics.
4.2 The Hierarchy of Brand Signals for AI Agents
Based on our analysis of how AI agents evaluate information, we propose a hierarchy of brand signals ranked by their likely effectiveness with algorithmic decision-makers:
Level 1: Marketing Claims (Least Effective) Unverified marketing language such as "premium quality," "eco-friendly," or "innovative design" represents the weakest form of signaling for AI agents. These subjective claims lack the specificity and verifiability that algorithmic systems require for evaluation.
Level 2: Self-Reported Metrics (Moderate Effectiveness) Brands that provide specific, measurable data about their products create stronger signals for AI evaluation. This includes technical specifications, performance benchmarks, and quantified attributes that enable systematic comparison.
Level 3: Third-Party Verification (High Effectiveness) Independent certifications, third-party testing results, and external validation represent stronger signals because they reduce concerns about self-serving bias and provide objective verification of claimed attributes.
Level 4: API-Accessible Verification (Highest Effectiveness) Real-time, API-accessible verification from trusted third parties represents the strongest possible signal for AI agents. This enables automated verification of claims and reduces information asymmetry to its lowest possible level.
4.3 The Great Value Sort: Market Realignment Through Algorithmic Evaluation
We introduce the concept of the "Great Value Sort" to describe how AI-mediated commerce may redistribute market share based on objective value rather than traditional brand equity. This process represents a systematic realignment where algorithmic evaluation exposes the gap between perceived brand value and actual product performance.
Traditional brand equity often includes significant premiums for emotional associations, aesthetic appeal, and marketing-created perceptions that may not correlate with objective product performance (Aaker, 1991). The Great Value Sort hypothesis suggests that AI agents, by focusing on objective evaluation criteria, may systematically favor products that offer superior actual value over those with superior perceived value.
This realignment process has several characteristics:
Transparency Advantage: Products with superior actual performance but limited marketing investment may gain market share as AI agents discover and recommend them based on objective criteria.
Premium Erosion: Products that command traditional brand premiums based primarily on emotional associations may lose market share if their objective performance does not justify their premium pricing.
Data-Driven Discovery: Products that invest in comprehensive, verifiable data presentation may become more discoverable and selectable by AI agents regardless of their traditional brand recognition.
5. Implications for Brand Strategy
The shift toward AI-mediated commerce presents both challenges and opportunities for brand management. Organizations must develop new capabilities while potentially devaluing existing brand assets that rely primarily on emotional appeal.
5.1 From Storytelling to Data Architecture
Traditional brand management emphasizes narrative development and emotional storytelling as primary tools for creating brand differentiation and customer loyalty (Fog et al., 2010). This approach assumes that customers will be influenced by brand stories and emotional associations when making purchasing decisions.
AI agents, however, appear to prioritize verifiable data over narrative content. This suggests that brand management may need to shift from storytelling to what we term "data architecture"—the systematic organization and presentation of verifiable information about products and services.
Data architecture involves several key components:
Structured Information Design: Organizing product information in standardized, machine-readable formats that enable systematic comparison and evaluation.
Verification Integration: Implementing systems that provide real-time access to third-party certifications, performance data, and other verifiable attributes.
API Development: Creating technical infrastructure that enables AI agents to access and evaluate brand-relevant information through automated processes.
5.2 Trust Signals for Algorithmic Evaluation
Traditional trust-building in branding relies heavily on emotional and social signals such as celebrity endorsements, user testimonials, and brand heritage (Keller, 1993). While these approaches may continue to influence human consumers, they may be less effective with AI agents that evaluate trust through different mechanisms.
Research suggests that AI agents respond more strongly to objective trust signals such as platform endorsements, third-party certifications, and verifiable performance metrics (Allouah et al., 2025). This indicates that organizations may need to develop new approaches to trust-building that emphasize verification over persuasion.
Effective trust signals for AI agents may include:
Platform Certifications: Official recognition from platforms such as "Amazon's Choice" or "Google Shopping" recommendations that provide algorithmic validation.
Third-Party Ratings: Independent evaluation by recognized rating agencies, testing organizations, or certification bodies.
Performance Data: Historical data on delivery times, return rates, customer satisfaction metrics, and other objective performance indicators.
Security Certifications: Technical certifications related to data security, privacy protection, and other technical standards.
5.3 Organizational Capabilities for AI-Mediated Branding
The transition to AI-mediated commerce may require significant changes in organizational capabilities and resource allocation. Traditional brand management skills may need to be supplemented with technical capabilities in data management, API development, and systematic verification processes.
Cross-Functional Integration: Brand management may need to integrate more closely with information technology, operations, and quality assurance functions to ensure that brand promises are supported by verifiable data and systematic performance.
Investment Reallocation: Organizations may need to shift resources from traditional advertising and creative development toward data infrastructure, certification processes, and technical integration capabilities.
Measurement Evolution: Success metrics may need to evolve from traditional brand awareness and emotional association measures toward algorithmic discoverability, selection rates, and objective performance indicators.
6. Platform Ecosystem Implications
The shift toward AI-mediated commerce occurs within existing platform ecosystems that already mediate much of digital commerce. Understanding how AI agents operate within these platforms becomes crucial for effective brand strategy.
6.1 Platform-Specific Optimization
Different platforms may develop different algorithmic evaluation criteria, requiring platform-specific optimization strategies. Success on Amazon may require different capabilities than success on Google Shopping, which may differ from success on emerging AI-powered commerce platforms.
This platform specificity creates new challenges for brand management, as organizations may need to develop multiple optimization strategies rather than relying on universal brand equity that transfers across all contexts.
6.2 Algorithmic Competition
As AI agents become more prevalent, competition may increasingly occur at the algorithmic level rather than the human perception level. Organizations may compete to become the preferred choice of AI systems rather than human consumers, requiring different competitive strategies and capabilities.
This algorithmic competition may favor organizations with superior data quality, technical integration capabilities, and systematic approach to verification over those with traditional marketing strengths.
7. Limitations and Future Research
7.1 Limitations
This conceptual framework has several important limitations. First, it is based on limited empirical evidence about AI agent behavior in commercial contexts, as this field is still emerging. The rapid pace of AI development means that agent behaviors and capabilities may evolve quickly.
Second, our framework assumes that AI agents will continue to operate primarily through logical evaluation processes. However, as AI systems become more sophisticated, they may incorporate more complex decision-making processes that include elements designed to simulate human-like evaluation criteria.
Third, the framework focuses primarily on product-based commerce and may not fully capture the dynamics of service-based or relationship-based business models where emotional factors may remain more important even in AI-mediated contexts.
Fourth, our analysis assumes a transition toward AI-mediated commerce but does not address the likely continued importance of hybrid decision-making where both AI agents and humans play roles in the purchasing process.
7.2 Future Research Directions
Several research opportunities emerge from this framework:
Empirical Validation: Future research should empirically test the effectiveness of different types of brand signals with various AI agent systems across multiple product categories and platforms.
Platform Comparative Analysis: Research should examine how different platforms implement AI-mediated commerce and whether different platforms favor different types of optimization strategies.
Market Share Impact Studies: Longitudinal research should track how the adoption of AI-mediated commerce affects market share distribution and competitive dynamics across different industries.
Hybrid Decision-Making: Research should explore how brand strategy may need to address contexts where both AI agents and human consumers influence purchasing decisions.
Consumer Welfare Implications: Research should examine whether AI-mediated commerce and the Great Value Sort improve consumer welfare by providing more objective product evaluation or create new forms of market inefficiency.
Organizational Adaptation: Research should study how organizations successfully transition from traditional branding approaches to AI-optimized strategies and what capabilities prove most critical for success.
8. Conclusion
The emergence of AI agents as commercial decision-makers represents a fundamental challenge to traditional branding paradigms built on human psychology and sensory perception. The concept of "The Shopper Has No Eyes" captures the essential insight that algorithmic decision-makers require different types of signals and information than human consumers.
This transition from emotional persuasion to data-driven verification may trigger what we term the "Great Value Sort"—a market realignment where objective performance becomes more important than traditional brand equity in determining commercial success. Organizations that successfully navigate this transition may gain competitive advantage, while those that rely primarily on emotional branding may find their market position eroded.
The implications extend beyond individual brand management to broader questions about market structure, competitive dynamics, and consumer welfare. If AI agents systematically favor products with superior objective performance, this could lead to more efficient markets but may also create new forms of competitive disadvantage for organizations with limited technical capabilities.
Success in an AI-mediated commerce environment may require fundamental changes in organizational capabilities, investment priorities, and strategic focus. Organizations may need to shift from storytelling to data architecture, from emotional appeals to verifiable performance, and from human psychology to algorithmic optimization.
The transition is likely to be gradual and uneven across different product categories and markets. However, organizations that begin developing AI-optimized branding capabilities early may be better positioned to succeed as AI-mediated commerce becomes more prevalent.
Future research should focus on empirically validating these theoretical predictions, understanding how different AI systems evaluate brands, and developing practical frameworks for organizations navigating this transition. The ultimate goal should be ensuring that AI-mediated commerce serves broader consumer welfare objectives while enabling organizations to build sustainable competitive advantage.
Author Note: The author confirms sole intellectual ownership of the concepts, frameworks, and all substantive arguments developed herein. AI was used as an assistive tool in research and drafting certain portions of the manuscript, but all original theoretical contributions are those of the author, who retains full responsibility for content and conclusions. Disclosure is provided in line with current academic and publishing ethics guidelines. The author is currently expanding these theoretical frameworks in a forthcoming book on algorithmic commerce under contract with St. Martin's Press, expected 2027.
References
Aaker, D. A. (1991). Managing Brand Equity: Capitalizing on the Value of a Brand Name. The Free Press.
Allouah, A., Besbes, O., Figueroa, J. D., Kanoria, Y., & Kumar, A. (2025). What Is Your AI Agent Buying? Evaluation, Implications, and Emerging Questions for Agentic E-Commerce. arXiv preprint arXiv:2508.02630.
Bettman, J. R., Luce, M. F., & Payne, J. W. (1998). Constructive consumer choice processes. Journal of Consumer Research, 25(3), 187-217.
Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.
Chen, L., Mislove, A., & Wilson, C. (2016). An empirical analysis of algorithmic pricing on Amazon marketplace. Proceedings of the 25th International Conference on World Wide Web, 1339-1349.
Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assessment. Journal of Management, 37(1), 39-67.
Eccles, R. G., Ioannou, I., & Serafeim, G. (2014). The impact of corporate sustainability on organizational processes and performance. Management Science, 60(11), 2835-2857.
Evans, D. S., & Schmalensee, R. (2016). Matchmakers: The New Economics of Multisided Platforms. Harvard Business Review Press.
Fog, K., Budtz, C., Munch, P., & Blanchette, S. (2010). Storytelling: Branding in Practice. Springer.
Forrester. (2025). With Agentic AI, Generative AI Is Evolving From Words To Actions.
Gaarlandt, J., Korver, W., Furr, N., & Shipilov, A. (2025, February 26). AI Agents Are Changing How People Shop. Here's What That Means for Brands. Harvard Business Review.
Henderson, P. W., & Cote, J. A. (1998). Guidelines for selecting or modifying logos. Journal of Marketing, 62(2), 14-30.
Hultén, B., Broweus, N., & Van Dijk, M. (2009). Sensory Marketing. Palgrave Macmillan.
Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1-22.
Krishna, A. (2012). An integrative review of sensory marketing: Engaging the senses to affect perception, judgment and behavior. Journal of Consumer Psychology, 22(3), 332-351.
Labrecque, L. I., & Milne, G. R. (2012). Exciting red and competent blue: The importance of color in marketing. Journal of the Academy of Marketing Science, 40(5), 711-727.
Lindstrom, M. (2005). Brand Sense: Build Powerful Brands Through Touch, Taste, Smell, Sight, and Sound. Free Press.
MacInnis, D. J. (2011). A framework for conceptual contributions in marketing. Journal of Marketing, 75(4), 136-154.
Milgrom, P., & Roberts, J. (1986). Price and advertising signals of product quality. Journal of Political Economy, 94(4), 796-821.
Parker, G. G., Van Alstyne, M. W., & Choudary, S. P. (2016). Platform Revolution: How Networked Markets Are Transforming the Economy—and How to Make Them Work for You. W. W. Norton & Company.
Romaniuk, J., & Sharp, B. (2016). How Brands Grow: Part 2. Oxford University Press.
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Schwartz, B. (2004). The Paradox of Choice: Why More Is Less. Harper Perennial.
Sharp, B. (2010). How Brands Grow: What Marketers Don't Know. Oxford University Press.
Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355-374.
Tadelis, S. (2016). Reputation and feedback systems in online platform markets. Annual Review of Economics, 8, 321-340.
Yadav, M. S. (2010). The decline of conceptual articles and implications for knowledge development. Journal of Marketing, 74(1), 1-19.
Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.
