Agentic Commerce: Unleashing AI-Driven Disruption Across Global Retail

As 2025 closes, a transformative wave sweeps through retail: agentic commerce. Defined by AI agents autonomously managing shopping—from intent discovery to checkout—this model is poised to upend the centuries-old paradigms of how goods are discovered, purchased, and delivered globally. With pilot deployments accelerating in markets like the United States, United Kingdom, Mexico, and Canada, the industry faces an inflection point rivaling the ascent of search engines in the early 2000s. Retail decision makers now grapple with the dual imperatives of technological adaptation and consumer trust, all amid a climate where up to 25% of referral traffic for leading players is already funneled by algorithms, not traditional browsing. What follows is an exposé into agentic commerce: its mechanisms, disruption patterns, early market lessons, and the profound implications for the future of shopping, loyalty, and cross-sector strategy.

From Manual Discovery to Conversational Commerce: The Agentic Paradigm Shift

Historical context—retail’s friction points: For decades, retail has struggled to connect fragmented discovery, evaluation, and purchase steps. Consumers jump from social media inspiration, to marketplace searches, to manual review aggregation before finally checking out—each step a potential drop-off. E-commerce promised convenience, yet the “shopping funnel” remained shallow and leaky.
Agentic commerce reimagines this funnel: AI agents—powered by conversational large language models (LLMs) and unified commerce data—now interpret nuanced shopper intent. Whether via voice, chat, or behavioral cues, they proactively curate products, bundle complementary items (“outdoor running kit for cold weather”), apply loyalty points, handle checkout, and even automate post-purchase support. As noted by commercetools, this shift collapses the journey into a single prompt, eliminating friction and boosting average order value (AOV) by 15–25% for early adopters.

The Disruptive Mechanics: Coordination, Autonomy, and New Loyalty

Core agent capabilities and market impact: The defining strength of agentic commerce is its ability to coordinate every aspect of the transaction autonomously. As McKinsey describes, this progression—from chaos to coordination—relies on real-time personalization, dynamic pricing, fraud detection, inventory sourcing, and post-purchase automation. Agents anticipate needs, negotiate prices, and execute purchases with minimal human input.
Hyper-personalized journeys: No longer passive recommenders, these agents navigate vast product catalogs, rotate homepage content, and bundle items based on rich user histories. Examples like Frasers Group in the UK and Liverpool in Mexico validate that when agents handle everything, checkout friction evaporates, cart sizes increase, and consumer engagement spikes 20%+.

Market Tipping Points: Data, Adoption, and Traffic Reallocation

Quantifying the shift—referral and adoption metrics: Traditional e-commerce platforms now see up to 25% of referral traffic sourced via agentic AI, according to Bain. While still less than 1% of total transactions, rapid surges signal a forthcoming tipping point—especially as third-party consumer agents, like those connected to ChatGPT, begin negotiating directly with retailer agents.
Trust and brand loyalty in flux: Kearney projects 60% of global consumers will use AI agents for shopping by 2027, with “bot logic” often eroding traditional brand loyalty. Half of consumers are still cautious with fully autonomous purchases, but positive experiences build trust and accelerate adoption.

Global Hotspots: A Comparative Lens on Early Agentic Commerce Markets

United States: Scale, Innovation, and Accelerating Referral Traffic

Market dynamics: The United States, sporting a $1.1 trillion e-commerce sector, holds both the greatest risk and the boldest opportunity. Agentic AI already drives up to 25% of referral traffic for top platforms like Amazon and Walmart. These giants are piloting agent-to-agent (A2A) and merchant-to-merchant (M2M) models, whereby agents not only handle consumer shopping but also source inventory from competitors, turning rivals into collaborators.
Disruptive projections: By 2027, A2A models could command 20–30% of e-commerce transactions if consumer trust continues its upward arc. Bain-Similarweb benchmarks highlight the vulnerability of legacy platforms, as agentic systems start bypassing traditional site architectures entirely.

United Kingdom: Enterprise Adoption and Ecosystem Retention

Frasers Group case study: The UK, with retail valued at over £500B, uses agentic commerce as both a competitive differentiator and an ecosystem retention strategy. Frasers Group leverages commercetools AI Hub and Agent Gateway, integrating with ChatGPT to deliver seamless discovery, real-time stock evaluation, ETA projections, and automated returns. This approach keeps users in-brand, even as agents scan across multiple marketplaces.
Pace of innovation: The British market stands out for advanced pilots and rapid consumer uptake, with industry observers warning traditional high street retailers to accelerate “agent-discoverable” platform development to remain relevant.

Mexico: Emerging Powerhouse with Scalable Models

Liverpool’s leap: Mexico’s $50B+ e-commerce sector is expanding at a remarkable 25% annual rate. Retailer Liverpool demonstrates how agentic commerce adapts to developing markets, deploying frictionless ChatGPT-powered shopping and proactive bundling—even as logistics and fulfillment challenges persist. According to Bain, Latin American consumers show initial caution, but trust rises quickly, especially among younger demographics, when agentic platforms deliver value.

Canada: Cross-Border Experimentation and AI Integration

Multi-merchant queries: Canada, with $60B in e-commerce, is a testbed for cross-border agentic commerce. Agents routinely evaluate merchant inventories for complex shopping prompts such as “Canadian Rockies outfits,” optimizing style, budget, and delivery options in real time. Google Cloud notes high AI adoption rates, with Canadian retailers leveraging U.S. supply chains for expedited fulfillment.
Innovation in sparse markets: Canadian pilots highlight agentic commerce’s potential where retail density is lower—agents simply “query the web” for the best fit, regardless of merchant boundaries.

Comparative Perspectives: Disruption Timelines and Strategic Imperatives

Differentiating market readiness: The agentic commerce revolution is not uniform. U.S. retailers face immediate threats (>25% AI referral traffic for some players) and must rapidly deploy A2A/M2M protocols to avoid disintermediation. The UK pioneers branded agent experiences; Mexico accelerates scalable adoption amid strong e-commerce growth; Canada focuses on interoperability and logistics innovation.
Table – Early Adopter Markets:
| Market | Early Adopter | Key Strength | Disruption Timeline |
|----------|------------------------|------------------------|-------------------------|
| US | Amazon/Walmart | Scale, AI traffic | Immediate (2026) |
| UK | Frasers Group | ChatGPT integration | Advanced pilots |
| Mexico | Liverpool | E-commerce growth | 1–2 years acceleration |
| Canada | Cross-border agents | M2M logistics | Testing phase |
This landscape creates a patchwork of strategies—from immediate API optimization in the U.S. to adaptive cross-border protocols in Canada.

Emerging Patterns: Tactical Shifts and Innovative Practices

Unified commerce data: Implementing agentic commerce requires integrating disparate data silos into a 360-degree operations view, enabling real-time accuracy and rapid decision-making (McKinsey). Retailers must audit infrastructure, aiming for sub-second query latency and 95%+ data accuracy.
Branded agent launches: Platforms like Frasers and Liverpool show the power of branded agents—integrated directly into digital storefronts via commercetools, ChatGPT, and third-party scanning APIs. The result: bundle AOV uplift >15%, increased retention, and system-wide engagement spikes.
Fraud mitigation and compliance: With half of consumers expressing caution, anomaly detection and consent verification must underpin all agentic transactions. The leading edge of retail now targets <0.5% false positives with advanced monitoring tools.

Innovative Collaboration: Agent-to-Agent (A2A) and Merchant-to-Merchant (M2M)

Breaking competitive boundaries: In the agentic future, competitors may become collaborators. Agents scan not just in-house stock, but also external inventories, automatically sourcing out-of-stock products from rivals via seamless APIs. As explained by Google Cloud, M2M models are being tested across North America, with Canadian retailers leading cross-border fulfillment experiments.
Optimization and margin preservation: Dynamic markdowns, micro-fulfillment allocation, and real-time inventory balancing ensure that while competition intensifies, margins can be preserved through strategic agent negotiation.

Post-Purchase Automation: Beyond the Sale

Proactive support and retention: Agentic commerce doesn’t end at checkout. Post-purchase agents automatically send setup guides, initiate returns with shipping labels, and refund customers—cutting support tickets by up to 30%. These features build trust and foster brand loyalty, even as “bot logic” replaces old-fashioned affinity.

Barriers to Widespread Adoption: Data, Trust, and Interoperability

Current friction points: Data silos hamper agentic accuracy, while fraud concerns remain high. Interoperability between agents—especially across global markets—requires common standards and protocols. Bain underscores the urgency of building trust among both consumers and algorithms, calling it the key hurdle for industry-wide transformation.
50% of consumers remain wary of total automation: Retailers must focus on making value “obvious to algorithms” and transparent to humans, blending autonomous efficiency with human oversight where needed.

Early Adopter Case Studies: Benchmarks and Lessons

Frasers Group (UK): ChatGPT-enabled agents deliver ranked product lists, proactive ETAs, and cross-channel loyalty integration, ultimately retaining users in-ecosystem.
Liverpool (Mexico): Mirrors Frasers’ strategy, proving agentic scalability in developing retail contexts.
U.S. and Canadian leaders: Platforms at the vanguard now see up to 25% of traffic via agentic referrals, with live M2M pilots reshaping supply chains and fulfillment routes.

Strategic Recommendations: 10 Steps for Retail Decision Makers

Phase-driven roadmap:

1. Audit Data Infrastructure (Q1 2026): Unify silos, aim for <1s query latency, 95% data accuracy.
2. Launch Branded Agents (Q2 2026): Integrate with commercetools/ChatGPT; target >15% AOV uplift.
3. Enable Agent Discoverability (Immediate): Optimize APIs for scanning/referral share to 10%.
4. Pilot A2A/M2M (H1 2026): Partner competitors for out-of-stock; seek 5% collaboration revenue.
5. Fraud/Compliance Framework: Implement anomaly detection, consent verification; <0.5% false positives.
6. Personalization Engine: Deploy real-time homepage/notifications; drive 20%+ engagement.
7. Post-Purchase Automation: Automate returns/setup, reduce tickets by 30%.
8. Market-Specific Rollouts: US, UK/Mexico, Canada—scale pilots as per market readiness.
9. Metrics Dashboard: Track AOV, conversion, retention vs. Kearney’s 60% adoption benchmark.
10. Talent/Partnerships: Hire AI experts, partner with Google Cloud/commercetools; allocate 5–10% IT spend.

Perspectives from New Entrants: Risks and Opportunities

For traditional retailers and new viewers: The agentic commerce revolution is not simply a technical upgrade—it’s a wholesale redefinition of value chains, consumer engagement, and competitive dynamics. Those unaccustomed to algorithmic gatekeeping may fear loss of brand identity, margin erosion, or data dependency.
Counterpoint—empowerment versus disintermediation: While agentic systems can bypass websites entirely, they also offer a chance for smaller merchants to surface in curated agent bundles, leveling the playing field for those who invest early in unified data and agent discoverability.

"Agentic commerce is not just the next chapter of e-commerce—it's a new book entirely, written in the language of algorithms, partnerships, and seamless consumer experiences. The retailers who teach their agents to make their value obvious won't simply survive; they'll lead."

Conclusion: The Bot-Driven Future Demands Immediate Strategic Action

Agentic commerce stands poised to transform global retail beyond recognition, compressing the path to purchase, automating personalization, and fundamentally changing what it means to shop—and to compete. The data is unequivocal: markets with advanced pilots see dramatic uplifts in traffic, engagement, and revenue, while those clinging to legacy models risk rapid irrelevance. The playbook is clear: unify commerce data, launch branded agents, forge agent-to-agent partnerships, and invest in both technical talent and AI-driven infrastructure. The strategic imperative? Act now, or watch as bot-driven ecosystems rewrite the rules, leaving behind those slow to adapt.

For cross-functional leaders, this is more than a technology story—it's a business survival manual. The agentic revolution will not only determine which companies thrive, but also how consumers experience commerce itself. The time for reflection is past. The time for decisive, cross-disciplinary action is now.