Retail is entering a structural shift where the “customer” is increasingly an AI agent acting on behalf of a human researching, comparing, negotiating, and completing purchases end-to-end. McKinsey describes agentic commerce as shopping powered by AI agents that act independently through multi-step action chains, aligned to human intent.
For CIOs and CTOs, agentic commerce is not a marketing trend. It is an architecture, integration, security, and governance challenge because agentic AI agents don’t just “recommend.” They plan and execute: they call APIs, move data across systems, trigger workflows, and initiate transactions across your commerce, OMS, CRM, payments, and service stack. This is why Gartner expects agentic AI to autonomously resolve a large share of common service interactions over the next few years, with meaningful operating cost impact.
The leadership question is straightforward: how do you become agent-ready with the right agentic architecture, systems integration, and governance guardrails without creating a new layer of risk, cost, compliance exposure, and operational fragility? What is agentic commerce in retail, and what is materially different about it?
What is agentic commerce and what is materially different?
Agentic commerce is the evolution from assistive AI (answering questions) to operational AI (completing outcomes). McKinsey frames it as agents that anticipate needs, navigate options, compare trade-offs, negotiate, and execute transactions often across multiple systems, steps, and tools.
Microsoft’s enterprise framing is similar: agents don’t simply deliver information; they reason, act, and collaborate, bridging the gap between knowledge and outcomes through tool use, workflows, and automated decisioning.
From a technology standpoint, AWS describes the shift as moving beyond rule-based software agents toward LLM-enabled agentic systems that can plan, call tools, and act in more flexible, context-aware, and data-driven ways.
Why this matters for retail leaders now
McKinsey estimates that by 2030, agentic commerce could drive up to $1T in orchestrated US B2C retail revenue, with $3T–$5T globally. At the same time, Gartner warns that over 40% of agentic AI projects may be canceled by end of 2027 due to cost, unclear business value, poor scoping, and weak operating models.
The implication is clear: the opportunity is significant but execution discipline is the differentiator, especially around integration readiness, governance-by-design, and production reliability.
What changes in retail when agents become the primary buyers?
1) Discovery becomes “agent-to-catalog,” not “customer-to-page”
Traditional SEO and on-site search assume a human is scanning results. Agents behave differently:
- They prefer structured attributes over marketing copy
- They compare across sources in seconds
- They optimize for constraints (price, delivery date, warranty, sustainability criteria)
Retailers should expect a rise in agent-mediated product discovery where brand storytelling still matters but must be parseable and verifiable.
What to do now
- Improve product structured data (attributes, compatibility, sizing, certifications)
- Make policies machine-interpretable (returns, shipping, warranties)
- Ensure inventory and pricing signals are consistent across channels
2) Loyalty shifts from “brand affinity” to “agent preference”
In an agent-driven world, shoppers may say, “Buy my usual,” or “Reorder the one that lasted longest,” and the agent will decide. Loyalty becomes partly a function of:
- reliability (on-time delivery, low defect rates)
- clarity (no surprises in returns or warranty)
- trust (authentic reviews, fewer counterfeit issues)
- integration (easy agent checkout, predictable tracking)
If your brand is hard for agents to evaluate or transact with, you risk being filtered out upstream before a human ever sees you.
3) Merchandising becomes an algorithmic conversation
Merchandisers will increasingly optimize for:
- “How would an agent rank us for X intent?”
- “What evidence proves our claims?”
- “Which bundles reduce decision complexity?”
The best merchandising teams will treat agents as a new “audience segment” with its own behavior and conversion drivers.
4) Customer service becomes proactive and automated
Agentic AI can collapse service cycles:
- “Where is my order?” becomes agent-accessible tracking
- Returns can be initiated automatically when criteria are met
- Substitutions can be negotiated at delivery time (with user-approved rules)
Shopify’s direction here is notable: Sidekick is positioned to automate complex tasks and workflows inside merchant ops.
5) Fraud and abuse evolve fast
As Visa has noted, merchants are already seeing massive growth in AI-driven shopping activity and are responding with protocols to separate trusted agents from malicious bots.
Retailers will need stronger identity, rate limiting, bot mitigation, and crucially agent authentication rather than blanket blocking.
Agentic AI in retail: High-ROI use cases that are practical today
Below are enterprise-ready use cases where AI agents for retail can drive measurable outcomes without depending on speculative capabilities.
1) Agentic customer service that actually resolves issues
Goal: reduce cost-to-serve and improve customer experience.
Examples of agent actions:
- Resolve order status issues by querying OMS/WMS, initiating carrier claims, and issuing proactive notifications
- Automate returns and refunds (eligibility check → label generation → refund initiation → inventory update)
Why CIOs care: Gartner projects agentic AI could autonomously resolve 80% of common customer service issues by 2029, contributing to a 30% reduction in operational costs.
2) Autonomous merchandising operations (promotion and pricing execution)
Goal: faster promotion cycles, fewer margin leaks, improved sell-through.
Examples:
- Agent monitors sell-through and inventory, proposes promotion changes, pushes updates to promo engines, and logs rationale for audit
- Dynamic pricing recommendations with guardrails (MAP rules, margin floors, competitive constraints)
3) Inventory and replenishment agents that reduce stockouts
Goal: improve availability while lowering working capital pressure.
Examples:
- Agent flags store-level anomalies, recommends transfers, and triggers replenishment workflows
- Supplier-facing agent prepares draft POs and aligns on delivery windows
4) “Agent-ready commerce” for B2B procurement
Goal: frictionless reordering for enterprise accounts.
Examples:
- Contract-aware purchasing agent checks price books, applies approved terms, and completes replenishment within spend policies
- Agent generates quotes, routes approvals, and finalizes orders
The reference architecture for agentic commerce
A credible agentic commerce architecture is less about a single model and more about controlled action execution.
Layer 1: Experience + channels
Web, mobile, contact center, social, in-store associate tools.
Layer 2: Agent orchestration and “tooling”
- Agent orchestrator (task planning, tool selection, human-in-the-loop approvals)
- Tool connectors (CRM, OMS, ERP, WMS, PIM, pricing engine, loyalty, payments, fraud)
Layer 3: Data foundation
Customer 360, product graph, inventory truth, event streams, and governed retrieval (so agents operate on authoritative data).
Layer 4: Governance, security, and observability (non-negotiable)
- Identity + delegated authorization (what an agent can do on behalf of a customer/employee)
- Risk controls, audit trails, and outcome monitoring (to avoid “silent failures” and hallucinated actions)
- API productization: consistent contracts, versioning, quotas, and policy enforcement
McKinsey highlights that enabling agentic commerce will require evolving integration and trust enablers (including protocols and delegated authorization patterns) and rethinking identity and loyalty for agent-mediated transactions.
Where ACI fits in an Agentic Commerce program
1) Strategy and use-case prioritization (business-first, ROI-led)
ACI can run an Agentic Commerce Readiness Assessment that maps your highest-value journeys and operational workflows then selects 2–3 pilots with clear KPIs (conversion, AOV, returns reduction, service deflection, inventory turns). This aligns with ACI’s retail focus areas spanning online-to-offline journeys, merchandising, and supply chain transformation.
Typical prioritized use cases:
- Personal shopping / concierge agent (guided discovery → bundle → checkout with approvals)
- Post-purchase agent (order changes, returns/exchanges, warranty claims)
- Merchandising agent (assortment recommendations, promo optimization, content generation with guardrails)
- Inventory and fulfillment agent (substitutions, allocation, exception handling)
2) Data foundation and integration (what makes agents reliable)
Agentic systems fail when product, inventory, policy, and customer data is inconsistent or inaccessible. ACI’s positioning emphasizes turning disconnected data into outcomes via AI, automation, and cloud platforms.
Concretely, ACI can help you:
- Modernize the retail data layer (customer, product, pricing, inventory, orders, returns)
- Implement RAG/search patterns over catalogs, policies, and knowledge bases
- Integrate agents with core systems (commerce platform, OMS, CRM, CDP, support tools)
3) Agent design + orchestration (from “copilot” to “autonomous with controls”)
ACI has published work around building enterprise agentic architectures and multi-agent systems, and positions a next-gen agentic AI platform (neXus.ai) for enterprise-grade agents.
What ACI would deliver here:
- Agent workflows (plan → tool use → verification → action)
- Tooling adapters for commerce/CRM/service platforms
- Guardrails: permissions, spend thresholds, approval checkpoints, and fallbacks
ACI Infotech helps retailers operationalize agentic commerce from identifying the right journeys to building governed agents integrated with your catalog, OMS, CRM, and support ecosystem. With applied AI/ML engineering and production-grade LLMOps, ACI enables retailers to deploy agents that can plan, act, and learn within strict guardrails improving conversion, reducing service load, and increasing operational agility without compromising trust
