Competing in the Age of Algorithmic Intermediation: A Dynamic Capabilities Framework for Algorithmic Readiness
Paul F. Accornero
Affiliations
Founder, The AI Praxis
ORCID ID: https://orcid.org/0009-0009-2567-5155
SSRN Working Paper Series: 5693863
Date: 2025
Comments welcome: paul.accornero@gmail.com
WORKING PAPER - NOT INTENDED FOR CITATION
This is a pre-print version of a more in-depth paper undergoing peer review.
Abstract
As autonomous AI agents increasingly intermediate commercial transactions, organizations confront a fundamental strategic challenge: competing when algorithms, not humans, evaluate and select suppliers. We develop the construct of algorithmic readiness—organizational capacity to compete effectively in AI agent-mediated markets—grounded in dynamic capabilities theory. Through expert interviews with platform strategists and procurement executives, we identify distinctive capability requirements that extend beyond digital maturity. We theorize algorithmic readiness through sensing (detecting when algorithmic evaluation criteria diverge from human preferences), seizing (managing the transparency-protection paradox in data provision), and transforming (operating dual-mode systems for human and algorithmic audiences). We develop testable propositions linking algorithmic readiness to competitive outcomes, specify boundary conditions, and provide preliminary empirical validation. This research addresses critical gaps in understanding organizational adaptation when the nature of the "customer" fundamentally changes.
Keywords: algorithmic readiness, artificial intelligence agents, dynamic capabilities, digital transformation, B2B marketing, platform strategy
1. Introduction
On a typical day in 2024, an enterprise procurement system autonomously evaluates 50 suppliers across 200 parameters, executing a $2 million contract without human intervention. Amazon's Alexa reorders household products based on usage patterns and price optimization algorithms. Financial robo-advisors allocate billions in assets through algorithmic risk assessment (Davenport, Guha, Grewal, & Bressgott, 2020; Huang & Rust, 2021). This phenomenon—autonomous AI agents acting as primary commercial intermediaries—represents what we term algorithmic intermediation: the delegation of purchasing evaluation and decision authority to computational systems.
The proliferation of algorithmic intermediation catalyzes fundamental shifts in competitive dynamics. Traditional marketing theory emphasizes persuading human customers through emotional appeals, brand narratives, and relationship cultivation (Palmatier, Dant, Grewal, & Evans, 2006). Traditional sales theory relies on interpersonal influence, trust building, and consultative engagement (Weitz & Bradford, 1999). Yet when evaluators are algorithms optimizing against explicit criteria, these capabilities lose effectiveness. Organizations confront a strategic puzzle: How do we compete when the "customer" is a dispassionate computational system immune to persuasion?
1.1 The Theoretical Puzzle
Algorithmic intermediation creates three interrelated paradoxes that existing theory cannot adequately resolve:
· The Transparency-Opacity Paradox: Algorithms require structured, comprehensive data for evaluation (transparent requirements), yet use proprietary decision logic that suppliers cannot observe (opaque process). Dynamic capabilities theory emphasizes sensing environmental change (Teece, 2007), but how do organizations sense and respond when evaluation criteria are simultaneously explicit and hidden?
· The Control-Dependency Paradox: Organizations must cede evaluation control to external algorithms to gain market access, yet this dependency creates strategic vulnerability. The resource-based view emphasizes controlling valuable resources (Barney, 1991), but algorithmic intermediation requires providing comprehensive data to external systems, creating information asymmetries favoring platform owners.
· The Relationship-Automation Paradox: B2B marketing theory emphasizes relationship quality as competitive advantage (Palmatier et al., 2006), yet algorithmic intermediation eliminates direct buyer-seller relationships. When AI agents mediate transactions, relationship capital built with human buyers becomes irrelevant.
These paradoxes reveal that algorithmic intermediation is not simply "digital transformation with algorithms" but represents a qualitatively different competitive context requiring new theoretical development.
2. Theoretical Foundations and Conceptual Gap
To address this puzzle, we build on three streams of literature, each of which provides a partial lens but reveals a critical conceptual gap.
First, dynamic capabilities theory addresses how organizations "integrate, build, and reconfigure internal and external competences to address rapidly changing environments" (Teece, Pisano, & Shuen, 1997, p. 516). This framework, disaggregated into sensing, seizing, and transforming (Teece, 2007), is perfectly suited for analyzing adaptation to turbulent contexts (Eisenhardt & Martin, 2000). However, most research examines technological change or business model innovation (Teece, 2018; 2014). The specific mechanisms through which organizations sense, seize, and transform when the nature of the customer fundamentally changes—from human to algorithmic—remain undertheorized.
Second, digital transformation literature focuses on using digital technologies to change operations, customer relationships, and value creation (Vial, 2019; Verhoef et al., 2021). Digital maturity models assess progress across various dimensions (Kane, Palmer, Phillips, Kiron, & Buckley, 2015). The gap here is one of focus: these frameworks assess organizations' ability to digitize human-facing processes and optimize human customer experiences. The shift to algorithmic intermediation requires the opposite: back-end data infrastructure, API performance, and standards compliance—capabilities optimizing machine interpretation.
Third, literature on AI capabilities and platform strategy examines organizational needs for deploying AI (Mikalef & Gupta, 2021) or strategies for participating in digital ecosystems (Gawer, 2014; Parker, Van Alstyne, & Choudary, 2016). This work illuminates capabilities for using AI internally or meeting technical standards (Hein et al., 2020). The gap is clear: this literature does not address the capabilities required when external AI agents become the primary evaluators of an organization's offerings.
Synthesizing these streams reveals that existing frameworks do not adequately conceptualize organizational readiness for a world where AI agents become the primary customers. We address this gap by developing algorithmic readiness as a theoretical construct capturing the distinctive organizational requirements for competing in algorithmically-mediated markets.
3. Conceptualizing Algorithmic Readiness
We define algorithmic readiness as the organizational capacity to compete effectively when autonomous AI agents mediate commercial transactions and act as primary evaluators of organizational offerings.
This capacity encompasses three interrelated dimensions aligned with dynamic capabilities theory, but with distinctive mechanisms specific to algorithmic intermediation contexts:
1. Sensing Algorithmic Shifts: Beyond recognizing that algorithms mediate purchasing (surface awareness), sensing involves detecting when algorithmic evaluation criteria diverge from human preferences, reverse-engineering algorithmic requirements through experimentation, and interpreting platform policy changes as signals of evolving algorithmic standards.
2. Seizing Through Strategic Data Provision: Beyond developing technical infrastructure, seizing involves managing the transparency-protection paradox (providing sufficient data for algorithmic evaluation while protecting competitive information), building algorithmic visibility capital (accumulated credentials improving evaluation independent of current performance), and optimizing for multiple competing algorithms simultaneously.
3. Transforming Through Dual-Mode Operations: Beyond reconfiguring processes, transforming involves maintaining parallel systems optimized for human perception and algorithmic evaluation, creating hybrid roles bridging technical and commercial functions, and navigating metric multiplicity (human satisfaction vs. algorithmic visibility KPIs).
Algorithmic readiness differs fundamentally from related constructs. Digital maturity assesses digitization of human-facing processes; algorithmic readiness assesses optimization for machine evaluation. AI absorptive capacity (extending Cohen & Levinthal, 1990) would address the ability to recognize and apply external AI innovations; algorithmic readiness addresses being evaluated by external AI.
3.1 Novel Mechanisms and Theoretical Extensions
We identify three novel mechanisms that extend dynamic capabilities theory beyond simple application to this context:
· Algorithmic Decoupling Detection (Sensing Mechanism): A critical sensing challenge involves detecting when algorithmic optimization criteria diverge from end-user preferences. For example, procurement algorithms may optimize for cost and compliance, while human end-users value service quality and responsiveness. Organizations must sense this decoupling and decide whether to optimize for the algorithm (to win the contract) or the end-user (for post-contract satisfaction).
· Strategic Opacity Management (Seizing Mechanism): Organizations face a novel challenge: providing sufficient data transparency for algorithmic evaluation while maintaining competitive opacity. Complete transparency enables optimal algorithmic assessment but reveals proprietary information to competitors and platforms. Complete opacity protects information but prevents algorithmic evaluation. Organizations must strategically manage this transparency-opacity boundary—a mechanism not addressed in standard seizing literature.
· Dual-Mode Marketing and Sales Operations (Transforming Mechanism): Algorithmic intermediation requires organizations to maintain parallel systems: one optimized for human perception (creative marketing, relationship selling) and another for algorithmic evaluation (structured data, API performance). These systems require different capabilities, metrics, and resource allocations, yet must coordinate strategically. This dual-mode requirement extends beyond ambidexterity (exploration vs. exploitation) to simultaneous optimization for fundamentally different evaluator types.
4. Theoretical Propositions
We develop seven propositions specifying relationships between algorithmic readiness capabilities and organizational outcomes, with explicit attention to causal mechanisms, boundary conditions, and moderating factors.
· Proposition 1: The relationship between algorithmic intermediation sensing speed and competitive advantage follows an inverted-U pattern moderated by industry standardizability and platform market share.
o Mechanism: Early sensing enables capability development before competitive pressure intensifies, creating temporal advantages through learning-by-doing. However, once standards stabilize (which happens faster in standardizable industries), imitation becomes feasible and pioneering advantages erode.
o Boundary Conditions: This relationship holds only when: (a) algorithmic intermediation actually progresses, and (b) standards eventually emerge.
· Proposition 2: Organizations with superior data infrastructure capabilities receive more favorable algorithmic evaluation scores, but this relationship is moderated by product differentiation and relationship strength with end-users.
o Mechanism: Data infrastructure quality (completeness, accuracy, API performance) translates into competitive advantages independent of underlying product quality.
o Counterintuitive Prediction: Organizations with superior data infrastructure may initially experience lower selection rates if that comprehensive data reveals quality variation that algorithms penalize, while incomplete data from competitors masks deficiencies.
· Proposition 3: Organizations successfully transforming commercial capabilities toward enablement orientation (structured data provision, technical integration competencies, verifiable credentials) capture greater share of agent-mediated transactions. However, this relationship exhibits path dependence—organizations that maintain strong human relationship capabilities alongside algorithmic optimization achieve superior long-term performance.
o Mechanism: Exclusive algorithmic optimization creates vulnerability. Maintaining human relationship capabilities provides strategic options and hedges against uncertainty (e.g., end-users overriding agent recommendations).
· Proposition 4: Algorithmic readiness capabilities exhibit complementarity effects—the value of each capability dimension (sensing, seizing, transforming) increases with the strength of the other dimensions.
o Mechanism: Complementarity operates through multiple channels. Superior sensing without seizing creates awareness without action (bottleneck effect). Superior seizing without sensing creates misallocated investment (misdirection effect). Balanced development enables positive feedback loops.
o Moderator: Environmental dynamism moderates this effect; in dynamic environments, capability gaps create substantial vulnerability, intensifying the need for balance.
· Proposition 5: The competitive basis shifts from persuasion capabilities (creative marketing, relationship selling) toward enablement capabilities (data quality, API performance, verifiable credentials) as algorithmic intermediation intensifies, but this shift is moderated by product characteristics and regulatory factors.
o Mechanism: The competitive basis shifts because the evaluation criteria change. Humans respond to emotional appeals; algorithms evaluate based on quantifiable attributes.
o Moderator: For commoditized products, the shift is strong. For differentiated or complex products, human evaluation retains importance, moderating the shift.
· Proposition 6: Organizations investing in algorithmic readiness experience curvilinear performance effects over time.
o Mechanism: Initially, investments create costs without returns (investment phase), followed by performance improvements (growth phase), and potential performance decline if organizations over-optimize for algorithmic evaluation at the expense of human customer satisfaction (overspecialization phase).
· Proposition 7: Strategic opacity management capability—the ability to provide sufficient data for algorithmic evaluation while protecting competitive information—becomes increasingly valuable as algorithmic intermediation intensifies.
o Mechanism: Organizations developing sophisticated data provision strategies will achieve superior competitive outcomes compared to organizations pursuing either full transparency (revealing too much) or complete opacity (being algorithmically invisible).
5. Discussion and Contributions
This research makes several theoretical contributions.
First, we extend dynamic capabilities theory by identifying how sensing, seizing, and transforming mechanisms operate in a novel context: where the customer fundamentally changes from human to algorithmic. We introduce three novel sub-mechanisms—algorithmic decoupling detection, strategic opacity management, and dual-mode operations—that extend beyond a simple application of established theory.
Second, we resolve theoretical paradoxes that existing frameworks cannot address. The transparency-opacity, control-dependency, and relationship-automation paradoxes represent genuine theoretical tensions. Our framework explains how organizations navigate these paradoxes through strategic capability building.
Third, we establish algorithmic readiness as a distinct construct from digital maturity, AI absorptive capacity, and platform complementor capability. While related, algorithmic readiness addresses the specific phenomenon of competing when AI agents become primary evaluators, requiring dedicated theoretical attention.
Fourth, we develop a set of testable propositions with specified causal mechanisms, boundary conditions, and moderating factors. These propositions, including counterintuitive predictions, provide a rich foundation for future empirical falsification and theoretical advancement.
6. Identifying Key Research Gaps
Our conceptualization of algorithmic readiness as a dynamic capability is a necessary first step. However, it also brings into sharp focus several critical research gaps that the field must now address. This framework provides the vocabulary to ask new and more precise questions.
First, there is a micro-foundations gap. While our framework identifies organizational capabilities, we lack a deep understanding of the managerial cognition that underpins them. How do individual managers and teams make sense of opaque algorithmic feedback? What cognitive biases (e.g., anthropomorphism, confirmation bias) shape their 'reverse-engineering' experiments (our Sensing dimension)? Understanding the managerial cognition behind sensing is a critical, and as-yet unexplored, area.
Second, current theory often simplifies the context to a 'firm-versus-algorithm' dyad. The reality is an ecosystem-level gap. Firms operate in an environment of multiple, competing, and often interacting algorithms (e.g., Google's search algorithm, Amazon's supplier algorithm, a B2B procurement platform's algorithm). We lack theory on algorithmic ecosystem strategy, such as how firms optimize for multiple, conflicting algorithmic evaluators simultaneously, or how algorithmic 'collusion' (intended or emergent) on platforms might shape supplier markets.
Third, there is a critical performance measurement gap. While we propose a link between readiness and outcomes (Propositions 6 & 7), the field lacks validated constructs to measure algorithmic readiness itself. Developing and validating a 'Readiness Scorecard' or a set of key performance indicators (e.g., 'Algorithmic Visibility Score', 'Data Provision Efficiency') is a crucial, high-priority task for empirical work. Without this, testing our propositions—and providing practical benchmarks for managers—remains exceptionally difficult.
7. Planned Future Directions
As the author, my research program is now focused on addressing these gaps. This working paper serves as the theoretical foundation for several interconnected empirical projects currently in development, which I am actively pursuing.
First, to address the performance measurement gap, future empirical work will include developing a survey instrument to operationalize the three core dimensions of algorithmic readiness (Sensing, Seizing, Transforming). This will allow us to move from the conceptual propositions in this paper to large-scale, falsifiable hypothesis testing.
Second, we are initiating a multi-year longitudinal case study of B2B suppliers in high-intermediation industries (e.g., enterprise software, electronic components). This qualitative work will move beyond the preliminary validation (Section 4) to trace how 'dual-mode' operations and 'strategic opacity' capabilities (Propositions 3 & 7) are actually built and evolve over time, providing a much-needed process-oriented view.
Third, we plan to examine the ecosystem dynamics gap by using agent-based modeling. The goal is to simulate the competitive outcomes of different 'strategic opacity' choices in environments with multiple, competing algorithms.
This multi-stage research program, which this paper seeds, is intended to build a robust and actionable theory of competition in the age of algorithmic commerce, which will be further expanded in my forthcoming book. I welcome feedback and collaboration from the scholarly community on these research streams.
8. Managerial Implications
This research provides several actionable insights for practitioners navigating algorithmic intermediation:
1. Assess Algorithmic Exposure and Readiness: Organizations should conduct systematic assessments of: (a) current and projected algorithmic intermediation in their markets, (b) existing capability gaps across the sensing, seizing, and transforming dimensions, and (c) competitive vulnerability from inadequate readiness.
2. Prioritize Infrastructure Over Persuasion: As algorithmic intermediation intensifies, investment priorities must shift from ephemeral perception-based marketing toward durable infrastructure. Product information management systems, API capabilities, and data quality processes provide lasting competitive advantages in algorithm-mediated contexts.
3. Develop Strategic Data Provision Capabilities: Organizations require sophisticated strategies for managing the transparency-protection paradox. This involves categorizing information by competitive sensitivity and determining optimal data provision levels. Strategic opacity management constitutes a new and critical competitive capability.
4. Maintain Dual-Mode Operations: Organizations should resist an "either-or" choice between optimizing for human or algorithmic evaluators. Successful approaches will maintain parallel capabilities: creative marketing for brand building alongside structured data for algorithmic visibility; relationship selling for complex decisions alongside technical integration for transactional efficiency.
5. Invest in Talent Development: Algorithmic readiness requires new hybrid capabilities, such as technical literacy in traditionally non-technical roles (marketing, sales) and commercial awareness in technical roles (IT, data science).
9. Conclusion
The emergence of autonomous AI agents as commercial intermediaries represents a fundamental shift in competitive dynamics. As algorithms progressively mediate purchasing decisions, the capabilities that define competitive success evolve fundamentally. Organizations require new frameworks for understanding these required capabilities when the "customer" becomes a computational system.
This article introduces algorithmic readiness as a critical organizational capability for competing in AI agent-mediated markets. We theorize its core dimensions through dynamic capabilities theory while identifying distinctive mechanisms specific to this new context. For scholarship, this research demonstrates the need for increased precision in how general capabilities manifest in particular contexts. For practice, understanding algorithmic readiness becomes increasingly urgent as platforms deploy agentic commerce capabilities. Organizations developing these capabilities proactively may establish durable advantages, while those delaying risk progressive marginalization as algorithms systematically favor better-prepared competitors.
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Author Note & Declarations
Working Paper Declaration: This article is a working paper distributed via SSRN. It is intended to elicit feedback and stimulate discussion. It has not been peer-reviewed and should not be cited as a final, published article.
Peer Review Note: A full-length, expanded version of this research, including detailed proposition development, methodology, and limitations. This working paper is being circulated to establish priority of ideas while a fuller empirical version undergoes peer review.
Book Reference: The author is currently expanding these theoretical frameworks in a forthcoming practitioner book on algorithmic commerce under contract with St. Martin's Press, expected 2027.