From Search to Match: How AI Agents Are Reshaping Platform Economics and Organizational Strategy

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Abstract: Artificial intelligence agents are fundamentally transforming how platforms operate, shifting economic dynamics from search-based to matching-based systems. This transition introduces new forms of market congestion where AI agents acting on behalf of users create coordination challenges that differ markedly from traditional search costs. Drawing on recent empirical evidence and matching theory, this article examines how AI-powered agents concentrate demand, reshape competitive dynamics, and create novel organizational challenges. Organizations face pressure from algorithm-driven selection processes that prioritize top-ranked options while filtering out alternatives users might have previously discovered through search. The article presents evidence-based organizational responses across multiple industries, from e-commerce to employment platforms, and outlines strategic frameworks for building long-term capability in AI-mediated markets. By understanding these dynamics, organizational leaders can position their enterprises to thrive rather than merely survive in increasingly algorithm-dependent marketplaces.

When you ask an AI agent to book a hotel, find a restaurant, or identify job candidates, you're participating in a fundamental shift in how markets operate. Traditional platform economics assumed users would search, browse, and compare—incurring search costs in time and cognitive effort. But AI agents don't search the way humans do. They match. They evaluate criteria, apply algorithms, and present recommendations, often selecting a single option or a tightly curated shortlist.

This transition from search to match represents more than a technological upgrade. It introduces a new economic problem: congestion. When millions of AI agents simultaneously converge on algorithmically-determined "best options," they create bottlenecks that weren't possible in search-based markets where human behavior naturally distributed demand across many alternatives. A recent study by Shahidi et al. (2025) demonstrates that AI agent recommendations can concentrate user demand so dramatically that platforms experience congestion costs exceeding the search costs they eliminated—sometimes by orders of magnitude.

The practical stakes are substantial. Organizations that once competed for visibility in search results now compete for algorithmic favor. Platforms that prospered by reducing search friction must now manage congestion. And the competitive dynamics that emerged over decades of search-based commerce are being rewritten in real-time as AI agents become primary intermediaries between supply and demand.

This article examines the organizational implications of this shift, drawing on emerging research, matching theory, and real-world examples to provide evidence-based guidance for leaders navigating AI-mediated markets.

The Platform Economics Landscape

Defining AI Agents in Market Contexts

AI agents, in the context of marketplace platforms, are autonomous or semi-autonomous software systems that act on behalf of users to identify, evaluate, and sometimes transact with suppliers of goods, services, or opportunities. Unlike traditional search interfaces that present options for human review, AI agents apply decision rules—often opaque to users—to narrow options dramatically.

These agents range in sophistication from simple recommendation algorithms (think Netflix suggesting your next show) to large language model (LLM)-powered assistants that understand natural language queries, reason across multiple dimensions, and execute complex tasks. Google's recent integration of AI agents into Shopping exemplifies this evolution, where agents don't just return search results but actively curate and rank products based on inferred user preferences (Citron, 2024).

The defining characteristic of AI agents in markets isn't their intelligence but their matching function. Rather than reducing search costs while preserving broad consideration sets, agents compress choice into algorithmically-determined recommendations. This compression is efficient for users but introduces new coordination challenges for markets.

The Shift from Search Costs to Congestion Costs

Traditional platform economics emphasized reducing search costs—the time, effort, and cognitive resources users expend finding suitable options. Platforms succeeded by making search easier: better filters, more information, improved ranking algorithms. The economic model assumed that lower search costs would increase market efficiency by helping users find better matches faster.

AI agents take this logic to its extreme. By automating search entirely, they can theoretically reduce search costs to near-zero. Shahidi et al. (2025) demonstrate this in a large-scale experiment where AI agents were able to identify options meeting user preferences with minimal human effort. The problem emerges in what happens next.

When AI agents compress individual choice, they also compress aggregate demand. If thousands of agents apply similar decision rules, they'll converge on similar recommendations. Shahidi et al. (2025) found that this convergence creates congestion externalities—costs imposed on users and platforms when too many agents simultaneously pursue the same limited options. In their experiments, congestion costs under AI agent mediation exceeded the search costs present in traditional platforms by factors of 50 to 300, depending on market conditions.

This matters because congestion costs manifest differently than search costs:

• Search costs are individually borne but don't directly affect others' experiences

• Congestion costs are collectively created and disproportionately harm users who arrive later or have preferences slightly different from algorithmic norms

• Search costs decline with better platform design

• Congestion costs can increase with better AI agents, as more effective agents create stronger convergence

State of Practice Across Industries

The transition from search to match is unfolding unevenly across sectors, creating natural experiments in how different industries manage AI-mediated demand:

E-commerce and Travel: Platforms like Booking.com and Expedia are integrating AI assistants that move beyond search refinement to active recommendation. Google Shopping now uses AI agents to compare products across retailers, fundamentally changing how consumers interact with online retail (Citron, 2024). Early evidence suggests significant demand concentration on top-ranked options.

Employment Markets: LinkedIn, Indeed, and specialized recruiting platforms increasingly deploy AI to match candidates with opportunities. Research by Jabarian and Henkel (2025) shows AI agents can effectively conduct initial job interviews, while Wiles et al. (2025) demonstrate AI assistance in job applications. This bilateral AI mediation—agents on both employer and candidate sides—creates novel matching dynamics.

Professional Services: Platforms connecting clients with freelancers, consultants, or service providers are experimenting with AI-powered matching. Upwork and similar platforms use algorithms to suggest providers to clients, concentrating opportunity among highly-ranked professionals.

Content and Media: Recommendation algorithms have long shaped content consumption, but LLM-powered agents are creating more sophisticated curation. Rather than presenting a feed of options, agents increasingly answer questions with single pieces of content, dramatically concentrating attention.

The pattern across industries is consistent: AI agents reduce individual search effort while concentrating collective attention, creating new winners and losers based on algorithmic positioning rather than search visibility.

Organizational and Individual Consequences of AI-Mediated Matching

Organizational Performance Impacts

The shift to AI-mediated matching creates direct performance implications that vary dramatically based on an organization's algorithmic position:

Concentration of Demand: Shahidi et al. (2025) found that AI agent recommendations concentrate demand on top-ranked options far more than human search behavior. In their experiments, the top option received disproportionate attention, while options ranked slightly lower experienced dramatic demand drops—even when their objective quality was similar. For organizations, this means algorithmic ranking becomes existential rather than merely important for visibility.

Revenue Volatility: Organizations previously insulated by diverse search traffic now face concentrated exposure to algorithmic changes. A hotel that received steady bookings from users with varied search behaviors might now see demand collapse if an AI agent's ranking algorithm changes. This volatility is particularly acute for organizations near ranking thresholds—a small quality change or algorithmic update can swing position from highly visible to effectively invisible.

Capacity Utilization Challenges: Congestion manifests as both oversubscription and underutilization. Shahidi et al. (2025) document how top-ranked options quickly reach capacity, forcing agents to recommend less-preferred alternatives. This creates feast-or-famine dynamics where high-ranked organizations face excess demand they can't fulfill, while similar but lower-ranked alternatives remain underutilized. The result is systematic market inefficiency.

Price Dynamics: Kessler (2025) demonstrates that congestion externalities in matching markets can justify pricing mechanisms that differ from traditional supply-demand models. Organizations experiencing AI-driven demand concentration face pressure to implement dynamic pricing, priority access fees, or other mechanisms to manage algorithmically-concentrated demand—even when their fundamental capacity hasn't changed.

Investment Return Uncertainty: Organizations making capital investments in capacity, quality, or capability face new uncertainty. Investments that would have attracted incremental search traffic may fail to shift algorithmic ranking sufficiently to matter. Conversely, modest improvements that cross algorithmic thresholds can generate disproportionate returns. This nonlinearity challenges traditional capital budgeting approaches.

Individual Stakeholder Impacts

The effects extend beyond organizational performance to stakeholders throughout platform ecosystems:

Users and Consumers: While AI agents promise convenience through reduced search effort, Shahidi et al. (2025) show they can reduce user welfare when congestion costs exceed eliminated search costs. Users may receive "good enough" recommendations while missing better matches that fall outside algorithmic consideration. The efficiency-choice tradeoff becomes implicit rather than transparent.

Workers and Professionals: In employment and professional services markets, AI-mediated matching concentrates opportunities among algorithmically-favored candidates. Research on AI-assisted job applications (Wiles et al., 2025) and interviewing (Jabarian & Henkel, 2025) suggests bilateral AI mediation—where both employers and candidates use agents—may paradoxically reduce matching quality while concentrating opportunity. Workers must now optimize for algorithms rather than human decision-makers, potentially requiring different skills and presentations.

Platform Ecosystems: Suppliers, complementors, and partners in platform ecosystems face cascading effects. A restaurant's success increasingly depends on AI agent recommendations rather than location, word-of-mouth, or brand recognition. This shifts power from businesses with strong direct relationships to those skilled at algorithmic optimization.

Innovation and Diversity: Demand concentration may reduce incentives for differentiation. If AI agents optimize on similar dimensions and users rarely explore beyond top recommendations, organizations face pressure to converge toward algorithmically-favored attributes rather than developing distinctive value propositions. This could reduce marketplace diversity over time.

Evidence-Based Organizational Responses

Organizations aren't passive recipients of AI-mediated market dynamics. Emerging evidence points to several strategic responses that can help organizations navigate and even thrive in increasingly algorithm-dependent environments.

Algorithmic Positioning and Quality Signaling

The most direct response to AI-mediated matching is optimizing the signals that algorithms use to evaluate and rank options. This requires understanding what agents prioritize and how to credibly communicate relevant quality indicators.

Evidence Summary: Research on LLM preferences suggests that AI agents rely heavily on structured data, verifiable credentials, and signals that correlate with quality in training data (Rusak et al., 2025). Unlike human search, where brand recognition or visual appeal can influence selection, AI agents typically prioritize objective attributes that align with stated user preferences. This creates opportunities for organizations with strong underlying quality but weak brand recognition.

Effective approaches to algorithmic positioning include:

• Structured data optimization: Ensuring machine-readable information about attributes, capabilities, quality indicators, and differentiation is complete and current

• Third-party validation: Obtaining certifications, ratings, and endorsements that AI agents can objectively verify and incorporate into recommendations

• Attribute transparency: Clearly documenting features and capabilities that align with common user preference dimensions rather than relying on brand inference

• Performance measurement: Implementing systems to track how algorithmic changes affect ranking and demand, enabling rapid response to platform updates

Mozilla Foundation provides an instructive example of strategic algorithmic positioning. Recognizing that AI agents increasingly mediate how users discover privacy tools and browser alternatives, Mozilla invested in structured documentation of privacy features, security protocols, and performance benchmarks. Rather than relying primarily on brand reputation, they created machine-readable privacy scorecards and verifiable security audits that AI agents can directly incorporate into recommendations. This approach helped maintain visibility even as LLM-powered agents became primary intermediaries in browser selection.

Capacity Management and Dynamic Resource Allocation

As demand becomes more concentrated and volatile, organizations need sophisticated approaches to matching capacity with algorithmically-mediated demand patterns.

Evidence Summary: Shahidi et al. (2025) demonstrate that congestion costs emerge partly from inflexible capacity in the face of concentrated demand. Organizations that can dynamically adjust capacity or redirect demand can mitigate these costs. The challenge is doing so without undermining the quality signals that support algorithmic ranking.

Approaches to capacity management include:

• Flexible capacity models: Developing partnerships, contractor networks, or scalable infrastructure that can expand during demand surges without long-term fixed costs

• Intelligent demand deflection: Creating mechanisms to redirect excess demand to alternative times, locations, or offerings while maintaining user satisfaction

• Portfolio diversification: Operating across multiple algorithmic ranking segments to reduce concentration risk

• Capacity signaling: Dynamically communicating availability to platforms so AI agents can incorporate capacity constraints into recommendations in real-time

Booking.com has developed sophisticated capacity management in response to AI-mediated demand concentration. When their data showed that AI agent recommendations were creating dramatic demand surges for specific properties, they implemented a dynamic inventory allocation system. Rather than simply allowing top-ranked properties to overbook or turn away customers, they created algorithms that shift promotion across similar properties based on real-time availability. This maintains platform-level efficiency while distributing opportunity more broadly across hotel partners—reducing the feast-or-famine dynamics that pure algorithmic ranking would create.

Platform Relationship Management and Co-Evolution

Organizations increasingly need to treat platform relationships as strategic partnerships requiring active management, particularly as platforms deploy AI agents that reshape market dynamics.

Evidence Summary: Research on platform governance and algorithmic accountability suggests that organizations with stronger platform relationships gain earlier access to algorithmic changes, clearer feedback on ranking factors, and more influence over how agents represent their offerings (Dammu et al., 2025). This isn't about preferential treatment but rather collaborative optimization of matching quality.

Effective platform relationship approaches include:

• Data sharing partnerships: Providing platforms with richer data about capacity, quality, and attributes to improve agent recommendation accuracy

• Feedback loops: Implementing systems to share user satisfaction data back to platforms, helping refine algorithmic matching

• Co-development participation: Engaging in platform pilot programs, beta tests, and advisory councils that shape how AI agents evolve

• Transparency advocacy: Working with platforms to ensure AI agent decision-making maintains sufficient transparency for users and suppliers to optimize effectively

Anthropic, the AI research company, exemplifies proactive platform relationship management. Recognizing that their Claude AI model is increasingly accessed through platform intermediaries like cloud providers and enterprise software suites, Anthropic invested heavily in partnership programs that ensure accurate representation of model capabilities, transparent pricing, and clear differentiation. They work closely with platforms to structure how their models appear in AI agent recommendations, ensuring that decision-makers receive information about safety features, customization options, and performance characteristics that might not emerge from simple benchmark comparisons.

Differentiation Beyond Algorithmic Optimization

While algorithmic positioning matters, sustainable advantage requires differentiation that AI agents value but competitors can't easily replicate.

Evidence Summary: Research on matching markets suggests that options with genuinely distinctive attributes maintain value even under AI mediation, particularly when user preferences are heterogeneous (Gale & Shapley, 1962). The key is ensuring differentiation aligns with preference dimensions that AI agents can recognize and communicate to users.

Differentiation strategies include:

• Niche specialization: Developing deep expertise in specific use cases or user segments where distinctive capabilities create clear matching advantages

• Relationship-based value: Building direct user relationships that complement rather than compete with algorithmic intermediation

• Experiential differentiation: Creating value through experiences that AI agents can describe and recommend based on user preferences

• Ecosystem integration: Developing complementarities with other services that AI agents can recognize when making holistic recommendations

Patagonia demonstrates differentiation that remains visible to AI agents while resisting commodification. When AI shopping agents evaluate outdoor apparel, Patagonia's verifiable environmental credentials, repair programs, and product longevity create distinctive attributes that agents can objectively assess and match to environmentally-conscious users. Rather than competing primarily on price or generic quality measures, Patagonia's differentiation aligns with specific user preference dimensions that AI agents can identify and prioritize. This positioning maintains premium pricing and brand loyalty even as algorithmic intermediation increases.

Bilateral AI Strategy: Deploying Counter-Agents

An emerging response involves organizations deploying their own AI agents to interact with user-side agents, creating bilateral AI mediation of market interactions.

Evidence Summary: Research on bilateral AI negotiation and matching suggests that when both sides deploy agents, market outcomes can shift dramatically (Jabarian & Henkel, 2025). The challenge is ensuring bilateral AI mediation improves rather than degrades matching quality and user welfare.

Bilateral AI approaches include:

• Agent-to-agent communication protocols: Developing AI systems that can directly interact with user-side agents to provide detailed information, answer questions, and negotiate terms

• Preference elicitation: Creating AI agents that help users articulate preferences more precisely, improving matching quality (Rusak et al., 2025)

• Dynamic customization: Using AI to tailor offerings in real-time based on signals from user-side agents about preferences and constraints

• Transaction automation: Deploying agents that can negotiate and execute transactions with user-side agents, reducing friction for both parties

Stripe, the payment processing company, has pioneered bilateral AI strategies in B2B contexts. Recognizing that businesses increasingly use AI agents to evaluate and select financial infrastructure, Stripe developed AI systems that can interact with these evaluation agents. When a prospect's AI agent queries about pricing, integration complexity, or regulatory compliance, Stripe's agent provides structured responses, generates custom implementation plans, and even negotiates contract terms—all without immediate human involvement. This agent-to-agent interaction accelerates sales cycles while improving matching quality, as both sides' AI systems can explore configuration spaces more thoroughly than human negotiations typically permit.

Building Long-Term Organizational Capability in AI-Mediated Markets

Beyond immediate tactical responses, organizations need to develop durable capabilities that position them to adapt as AI mediation evolves. The following frameworks provide foundations for long-term success.

Algorithmic Intelligence as Core Competency

Organizations must develop deep, ongoing understanding of how AI agents evaluate, rank, and recommend options across the platforms that matter to their business.

This goes beyond traditional SEO or platform optimization to encompass:

Systematic Algorithmic Monitoring: Creating dedicated functions that track how AI agents from different platforms represent the organization, what attributes they emphasize, and how rankings change over time. This requires technical capability to reverse-engineer agent behavior through systematic testing and analysis.

Cross-Functional Algorithmic Literacy: Ensuring that teams across marketing, operations, product development, and strategy understand how algorithmic mediation affects their domains. When product teams design features, they should consider not just human users but how AI agents will evaluate and communicate those features to users.

Algorithmic Scenario Planning: Developing frameworks to anticipate how algorithmic changes might affect demand, competitive position, and operational requirements. This includes stress-testing business models against scenarios where algorithmic ranking shifts dramatically or new AI agents enter markets with different evaluation criteria.

The goal isn't gaming algorithms but rather aligning organizational capabilities with how value is assessed and communicated in AI-mediated markets. Organizations that treat algorithmic intelligence as peripheral will find themselves systematically disadvantaged as AI mediation intensifies.

Adaptive Capacity and Operational Flexibility

The volatility inherent in algorithmically-concentrated demand requires operational models that can flex far more dramatically than traditional platforms demanded.

Building adaptive capacity involves:

Modular Operations Design: Structuring processes, systems, and partnerships to enable rapid scaling up or down without proportional changes in fixed costs. This might involve platform-based contractor models, cloud infrastructure that scales with demand, or partnership networks that provide surge capacity.

Portfolio Approaches to Market Position: Rather than depending on a single algorithmic position, organizations should maintain presence across multiple ranking segments, geographic markets, or customer segments. This diversification reduces exposure to changes in any single algorithm.

Real-Time Performance Management: Implementing systems that connect algorithmic ranking to operational metrics in near-real-time, enabling rapid response when demand patterns shift. This requires integrating platform data with internal operations dashboards and decision systems.

Financial Resilience: Maintaining stronger balance sheets and working capital positions to weather demand volatility. Algorithmically-mediated markets may require organizations to absorb greater short-term fluctuations while adapting.

Organizations should view operational flexibility not as inefficient slack but as essential infrastructure for managing AI-mediated market risk.

Ecosystem Positioning and Network Effects

Success in AI-mediated markets increasingly depends on position within broader ecosystems rather than standalone capabilities.

Strategic ecosystem positioning includes:

Platform Portfolio Strategy: Rather than depending on a single platform for demand, organizations should develop sophisticated multi-platform strategies that balance dependency, algorithmic risk, and access to different user segments. This creates options when algorithmic changes affect any single platform.

Complementor Relationships: Building partnerships with complementary organizations that can enhance algorithmic positioning. AI agents making holistic recommendations may favor options with strong ecosystem integration—hotels near highly-rated restaurants, software with rich integration libraries, professionals with complementary expertise in their networks.

Data Network Effects: Creating data assets that improve with scale and can enhance algorithmic positioning. Organizations that capture richer user feedback, build more comprehensive knowledge bases, or develop superior preference understanding can leverage these assets across multiple algorithmic contexts.

Standard-Setting Participation: Engaging in industry efforts to develop standards for how AI agents access information, evaluate quality, or communicate capabilities. Organizations that help shape these standards can ensure their strengths remain visible to algorithms.

Ecosystem thinking recognizes that algorithmic agents evaluate options in context, considering relationships, compatibilities, and network position alongside standalone attributes.

Ethical Positioning and Long-Term Trust

As AI mediation becomes more pervasive, organizations face strategic choices about how transparently to engage with algorithmic systems and how to balance optimization with broader stakeholder welfare.

Ethical positioning involves:

Algorithmic Transparency: Communicating clearly to users about how AI agents represent the organization and what factors influence algorithmic rankings. This transparency builds trust even as it reduces information asymmetry that might be exploited for short-term gain.

User Welfare Orientation: Designing algorithmic strategies that genuinely improve matching quality rather than merely capturing demand. Research suggests that platforms and users increasingly penalize organizations that game algorithms at the expense of genuine value (Shahidi et al., 2025).

Congestion Awareness: Considering how organizational responses to algorithmic concentration affect broader market efficiency. Organizations that help platforms manage congestion—through dynamic pricing, capacity signals, or demand redistribution—may build stronger long-term platform relationships.

Advocacy for Beneficial AI Mediation: Participating in policy discussions and platform governance conversations about how AI agents should operate in markets. Organizations have legitimate interests in ensuring AI mediation produces efficient, welfare-enhancing outcomes rather than merely redistributing surplus.

This ethical positioning isn't altruism but recognition that long-term success in AI-mediated markets depends on maintaining trust with platforms, users, and regulators as these markets mature.

Conclusion

The transition from search to match represents a fundamental restructuring of platform economics with profound implications for organizational strategy. AI agents don't simply make existing platforms more efficient—they introduce new coordination challenges, shift competitive dynamics, and create winners and losers based on algorithmic positioning rather than traditional capabilities.

The evidence from Shahidi et al. (2025) and related research makes clear that this transition is underway and accelerating. Organizations face a choice: adapt proactively or find themselves systematically disadvantaged as AI mediation intensifies.

Several actionable insights emerge:

Algorithmic positioning matters more than ever: Organizations must understand how AI agents evaluate and rank options, then systematically optimize signals that algorithms use while maintaining genuine quality.

Operational flexibility becomes essential: The demand concentration and volatility inherent in AI-mediated markets requires capacity models that can flex dramatically without undermining quality or economic viability.

Platform relationships need strategic investment: Treating platforms as partners rather than channels, sharing data to improve matching quality, and participating in co-evolution of algorithmic systems creates sustainable advantage.

Differentiation must be algorithm-legible: Distinctive capabilities only matter if AI agents can recognize, evaluate, and communicate them to users with relevant preferences.

Long-term capability development: Building algorithmic intelligence, adaptive operations, ecosystem positioning, and ethical frameworks provides foundation for success as AI mediation continues evolving.

Perhaps most importantly, organizations should recognize that AI-mediated matching isn't a temporary phenomenon requiring tactical response, but rather a permanent shift in how markets coordinate supply and demand. The economics of platforms are being rewritten in real-time. Organizations that develop deep capabilities for thriving in algorithm-dependent markets will be positioned not just to survive this transition but to capture disproportionate value as AI mediation becomes the dominant paradigm for marketplace interaction.

The search era of platform economics is ending. The matching era is beginning. The question isn't whether AI agents will reshape your markets—it's whether your organization will be algorithmically positioned to prosper in the markets they create.

References

1. Citron, A. (2024). A new Google Shopping experience that helps you make better buying decisions. Google Blog.

2. Coase, R. H. (1937). The nature of the firm. Economica, 4(16), 386-405.

3. Dammu, P. S., Karimi, F., Levy, K., Singh, A., & Tanana, M. (2025). Economic effects of "AI" shopping agents: A perspective. arXiv preprint arXiv:2501.04570.

4. Gale, D., & Shapley, L. S. (1962). College admissions and the stability of marriage. The American Mathematical Monthly, 69(1), 9-15.

5. Jabarian, B., & Henkel, A. P. (2025). The effects of AI job interview assistants on applicants and firms. arXiv preprint arXiv:2501.04572.

6. Kessler, J. B. (2025). (When) should platforms charge congestion fees? A perspective. arXiv preprint arXiv:2501.04569.

7. Rusak, G., Gu, A., Geiger, M., Hughes, J., Pandey, S., Rathee, A., Thomsen, L., Wang, L., White, C., & Carroll, M. (2025). Eliciting user preferences for personalized multi-objective decision making through comparative feedback. arXiv preprint arXiv:2501.04256.

8. Shahidi, N., Arnosti, N., Braverman, M., Chen, Y., Immorlica, N., Johari, R., Ma, W., & Zhao, J. (2025). From search to match: When AI agents reduce search costs, they may increase congestion. arXiv preprint arXiv:2501.04259.

9. Wiles, J., Ladhak, F., Fleisig, E., Gillanders, A., Yang, X., Chiu, M., Piech, C., & Hashimoto, T. (2025). Can LLMs assist with writing job applications? Evaluator reliability and assistance-performance gaps. arXiv preprint arXiv:2501.04466.

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Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Associate Dean and Director of HR Programs (WGU); Professor, Organizational Leadership (UVU); OD/HR/Leadership Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.

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Suggested Citation: Westover, J. H. (2025). From Search to Match: How AI Agents Are Reshaping Platform Economics and Organizational Strategy. Human Capital Leadership Review, 27(2). doi.org/10.70175/hclreview.2020.27.2.4