The Shopper has No Eyes: Branding in a World without Human Perception

Paul F. Accornero 

Affiliations 

Founder, The AI Praxis   

ORCID ID: https://orcid.org/0009-0009-2567-5155

SSRN Working Paper Series: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5511838

Date: 2025 (Original version of this work was conceptualised in March 2025)

Comments welcome: paul.accornero@gmail.com 

Abstract

Branding has historically relied on sensory appeal and emotional resonance to influence human consumers. However, the emergence of Al 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 Al 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 Al-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 Al 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. 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 Al-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).

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). 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).

Sensory marketing has become a sophisticated tool for influencing consumer perceptions and preferences (Lindstrom, 2005). Research demonstrates that sensory elements can significantly influence brand perception and purchase intention through both conscious and unconscious mechanisms.

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.

Platform ecosystems 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 Al systems may reshape brand strategy.

2.3  Al Decision-Making and Commercial Applications

Recent research has begun documenting how Al systems make commercial decisions and how these patterns differ from human behavior. Allouah et al. (2025) provide empirical evidence that Al shopping agents exhibit systematically different purchasing behaviors, prioritizing platform endorsements while discounting traditional advertising signals.

This research reveals that Al agents operate according to different evaluation criteria than human consumers. Where humans may be influenced by emotional appeals, social proof, and aesthetic considerations, Al 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 Al 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 Al 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, Al 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 (Al 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 Al 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 Al 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 Al-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 Al-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 Al 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.

Al 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 Al agents may need to signal quality through verifiable data, third-party certifications, and measurable performance metrics.

4.2  The Hierarchy of Brand Signals for Al Agents

Based on our analysis of how Al 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 Al 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 Al 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, APl-accessible verification from trusted third parties represents the strongest possible signal for Al 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 Al-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 Al 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 Al 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 Al agents regardless of their traditional brand recognition.

5.  Implications for Brand Strategy

The shift toward Al-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.

Al 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 Al 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 Al agents that evaluate trust through different mechanisms.

Research suggests that Al 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 Al 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 Al-Mediated Branding

The transition to Al-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 Al-mediated commerce occurs within existing platform ecosystems that already mediate much of digital commerce. Understanding how Al agents operate within these platforms becomes crucial for an 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 Al-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 Al 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 Al 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 Al agent behavior in commercial contexts, as this field is still emerging. The rapid pace of Al development means that agent behaviors and capabilities may evolve quickly.

Second, our framework assumes that Al agents will continue to operate primarily through logical evaluation processes. However, as Al 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 Al-mediated contexts.

Fourth, our analysis assumes a transition toward Al-mediated commerce but does not address the likely continued importance of hybrid decision-making where both Al 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 Al agent systems across multiple product categories and platforms.

Platform Comparative Analysis: Research should examine how different platforms implement Al-mediated commerce and whether different platforms favor different types of optimization strategies.

Market Share Impact Studies: Longitudinal research should track how the adoption of Al-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 Al agents and human consumers influence purchasing decisions.

Consumer Welfare Implications: Research should examine whether Al-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 Al-optimized strategies and what capabilities prove most critical for success.

8.   Conclusion

The emergence of Al 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 Al 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 Al-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 Al-optimized branding capabilities early may be better positioned to succeed as Al-mediated commerce becomes more prevalent.

Future research should focus on empirically validating these theoretical predictions, understanding how different Al systems evaluate brands, and developing practical frameworks for organizations navigating this transition. The ultimate goal should be ensuring that Al-mediated commerce serves broader consumer welfare objectives while enabling organizations to build sustainable competitive advantage.

Author Note

The author is currently expanding these theoretical frameworks in a forthcoming book on algorithmic commerce under contract with St. Martin's Press, expected 2027.

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