Radial: Strategic Adoption of Agentic AI in Commerce: Priorities for CX Leaders

Consumer adoption of agentic AI is fundamentally driven by perceived value, particularly in areas like cost savings and efficiency, coupled with an unwavering expectation of control and security. Radial’s The Dawn of AI and Agentic Commerce: Key Consumer Research on Agentic AI report reveals critical insights into these dynamics.

Consumers primarily embrace AI when it delivers distinct value, such as speeding up the shopping process or reducing product costs. Forty-seven percent of shoppers, for instance, want AI to help them find the best prices for an item, significantly outpacing other use cases like choosing a replacement when an item is out of stock (12%) . This indicates that initial AI deployments should target clear, measurable value propositions for the customer. Beyond deals, consumers also expect operational consistency and quality, with 32% citing product quality as the top reason to buy from a brand and 62% stating that declining quality erodes trust.

A critical factor for comfort with agentic AI is the ability to maintain control and ensure security. Over one-third of consumers (34%) would only allow an AI agent to take approved actions, and 23% prefer that AI only make suggestions. A significant 21% do not want an AI agent acting on their behalf at all . For transactional processes, 53% of consumers require approval for every purchase, 41% demand two-factor authentication for transactions, and 39% want the ability to review or cancel purchases without penalty. The option to speak with a human if needed is also paramount, desired by 42% of all consumers, with higher preference among Baby Boomers (59%) and Gen X (47%) . This strong preference for human fallback and explicit control underscores the need for hybrid AI-human operating models.

What to Do:

• Prioritize Foundational CX: Ensure core operations—product quality, inventory accuracy, delivery timelines, and transparent communication—are robust before scaling advanced AI.
• Design for Explicit Control: Implement AI agents with clear approval workflows, human-in-the-loop mechanisms, and user-configurable settings that define AI autonomy levels (e.g., “suggest only” vs. “act with approval”).
• Ensure Data Security and Privacy: Deploy AI within a stringent security framework. Require multi-factor authentication for AI-initiated transactions and leverage established, secure payment orchestration platforms rather than entrusting AI agents with raw payment data.
• Guarantee Recourse: Clearly communicate refund policies and mechanisms for errors made by AI agents (65% of shoppers require a refund guarantee for mistakes).
• Integrate Human Escalation: Establish clear, easily accessible pathways for customers to connect with human agents when AI solutions prove insufficient or when concerns arise (61% of shoppers require human support).

What to Avoid:

• Over-Automating Sensitive Tasks: Do not deploy AI for complex or high-risk transactions (e.g., reordering, customer service inquiries involving sensitive data) without robust human oversight and clear opt-out mechanisms.
• Ignoring User Feedback on Autonomy: Assuming consumers are ready for fully autonomous AI agents will lead to distrust and abandonment.
• Introducing Hidden AI Fees: Avoid pricing models that add AI-related fees, as this can negate the perceived value of efficiency and cost savings, deterring adoption.

Strategic Implementation: Operating Models and Governance for Agentic AI

The adoption of agentic commerce is not merely a technological upgrade; it requires a fundamental evolution of business models, operating procedures, and governance frameworks. Enterprises must approach this strategically to mitigate risks and ensure sustainable growth.

Implementing agentic AI necessitates careful consideration of internal capabilities and potential challenges. Retailers face risks including business model evolution, potential lost sales from poor experiences, significant infrastructure and technology challenges, and complex data accuracy and governance issues . Moreover, reputational damage from misdirected AI decisions or legal ambiguities around AI actions presents a material risk. To address these, a flexible AI strategy that integrates with diverse platforms and providers, rather than exclusive partnerships, builds resilience .

Establishing clear operating models with defined roles and responsibilities is crucial. This includes roles for AI model oversight, data stewardship, policy enforcement, and customer experience management for AI interactions. Governance must extend beyond internal operations to cover data flows, consent management, and compliance across geopolitical boundaries, especially as AI agents may operate across multiple jurisdictions . Continuous monitoring and red-teaming of AI agent behavior are essential to identify and correct biases, ensure accuracy, and prevent unintended outcomes.

Immediate Priorities (First 90 Days):

• Form a Cross-Functional AI Governance Committee: Include stakeholders from CX, Legal, IT, Product, and Risk. Task them with developing initial policy frameworks for AI agent deployment, data usage, and ethical guidelines.
• Conduct a Customer Trust Audit: Identify existing customer pain points and areas where AI could provide immediate, low-risk value (e.g., product search, basic order tracking). Assess current privacy and security perceptions.
• Pilot AI Agents on Low-Risk, High-Value Tasks: Deploy AI agents for tasks like deal aggregation, personalized product recommendations (with explicit opt-in), or real-time inventory checks. Set clear performance thresholds (e.g., 95% accuracy for information retrieval, 99% uptime) and monitoring protocols.
• Define Human-in-the-Loop Protocols: Establish clear thresholds and escalation paths for AI agents to hand off to human customer service representatives (e.g., if sentiment analysis indicates frustration, if a query is outside the AI’s domain, or if a transaction exceeds a predefined value of $500). Ensure SLAs for human response times (e.g., 3-minute response for critical AI escalations).

Governance and Risk Controls:

• Policy Frameworks: Develop clear policies for AI agent data access, usage, and retention, aligning with GDPR, CCPA, and other relevant privacy regulations. Implement strict consent management for PII.
• Performance Monitoring and Auditing: Establish continuous monitoring of AI agent performance (e.g., First Contact Resolution rates for AI, Customer Effort Score for AI interactions, error rates, complaint rates). Implement audit trails for all AI agent actions and decisions for accountability.
• Ethical AI Guidelines: Integrate ethical considerations into AI development and deployment, focusing on fairness, transparency, and accountability. Conduct regular bias assessments for recommendation engines.
• AI Agent Guardrails: Programmatically limit AI agent actions based on predefined rules (e.g., spending limits up to $100 without explicit user approval, no data sharing outside defined parameters).

Leveraging Partnerships for Scalable Agentic Commerce

The complexity of implementing agentic commerce, from managing payment infrastructure to integrating disparate logistics systems, often requires strategic external partnerships. These collaborations can accelerate adoption, reduce development overhead, and ensure robust, secure operations.

Modern brands will need flexible, technology-driven logistics solutions to ensure inventory availability, timely product delivery, and streamlined returns. AI agents need to access and digest vast datasets from retailers’ Order Management Systems (OMS), Warehouse Management Systems (WMS), and Transportation Management Systems (TMS), alongside payment and customer information . Integrating these systems to provide real-time data to AI agents is a significant undertaking that can be streamlined through partnerships.

Managed Payment Orchestration (MPO) solutions are particularly critical. As shopping transitions from direct human interactions to agent transactions, payment infrastructure shifts, requiring new approaches to policies, authorization, approvals, and fraud prevention (Anderson, 2026). An MPO provider can manage the strategic and tactical elements of payment orchestration, navigating complex payments ecosystems, providing fraud detection, and ensuring compliance. This disintermediates payment processing from the AI platform itself, addressing consumer security concerns regarding AI handling payment information. Similarly, third-party logistics (3PL) partners can provide the necessary infrastructure and expertise for AI-oriented distribution and fulfillment, allowing enterprises to focus on their core competencies and agentic innovation.

What ‘Good’ Looks Like:

• Seamless Integration: All core commerce systems (CRM, ERP, OMS, WMS, TMS, billing, payment platforms) are integrated via APIs, providing AI agents with real-time, accurate data.
• Automated, Secure Transactions: Payment orchestration handles secure processing, fraud detection (e.g., identifying transaction anomalies with a 99% detection rate), and compliance, reducing risk for AI-initiated purchases.
• Optimized Logistics and Fulfillment: AI agents can leverage real-time inventory and delivery data from 3PL partners to provide accurate information to customers, improving customer satisfaction (e.g., 10% reduction in “where is my order” inquiries).
• Enhanced CX Metrics: Measurable improvements in key CX metrics, such as a 15% increase in First Contact Resolution (FCR) for AI-handled queries, a 5-point increase in Customer Satisfaction (CSAT) due to personalized experiences, and a reduction in post-purchase complaint rates by 10%.
• Operational Efficiency: Reduced manual effort in managing complex payment and logistics workflows, allowing internal teams to focus on strategic initiatives.

What to Do:

• Evaluate MPO Providers: Select partners with proven expertise in secure payment processing, fraud prevention, and compliance across relevant jurisdictions.
• Assess 3PL Capabilities for AI Integration: Partner with logistics providers who offer robust APIs and data feeds compatible with AI agent systems, supporting real-time inventory, tracking, and returns management.
• Prioritize Interoperability: Ensure partners can facilitate integration across emerging AI commerce protocols (e.g., Universal Commerce Protocol for product discovery and checkout, Agent Payments Protocol).
• Define Clear SLAs with Partners: Establish service level agreements for uptime, data accuracy, security incident response, and performance metrics for all integrated services.

Summary

The dawn of agentic commerce presents both immense opportunities and significant challenges for senior marketing and CX leaders. While the promise of frictionless, personalized shopping experiences is compelling, its successful realization depends on a strategic, human-centric approach. Building consumer trust through transparency, providing explicit control, ensuring robust security, and delivering undeniable value are not merely best practices but prerequisites for adoption.

Enterprises must invest in sound governance frameworks, develop flexible AI strategies, and be prepared to evolve their operating models. Crucially, leveraging expert partnerships for complex areas such as payment orchestration and logistics will enable scalable, secure, and compliant agentic commerce deployments. By focusing on these core tenets, CX leaders can navigate this transformative era, delivering superior customer experiences and driving long-term value for their organizations.

Source: Radial. (2026). The Dawn of AI and Agentic Commerce: Key Consumer Research on Agentic AI. Radial.