Agentic AI in commerce is already moving from lab demos to real customer touchpoints. Therefore, businesses should watch how autonomous agents will make decisions, place orders, and shape pricing and measurement. Additionally, these changes touch every layer of commerce — from B2C stores and streaming measurement to EDI flows and B2B platforms. However, the shift brings promise and risk. In this post I walk through five practical angles — market scale, measurement, operations, platform strategy, and consumer trust — to help leaders plan for the next five years.

## Bain’s Forecast: Agentic AI in Commerce Could Reach $300–$500B by 2030

Agentic AI in commerce may not be a niche idea for long. According to Bain & Co., agentic AI could account for as much as 25% of U.S. ecommerce by 2030. Therefore, the market opportunity is large: Bain estimates agentic commerce could reach $300 billion to $500 billion in the U.S. alone by the end of the decade. Additionally, this projection suggests agentic systems — tools that can act autonomously on behalf of consumers — will shift how shoppers discover, compare, and buy products.

However, the change is not purely technical. It is behavioral. Consumers will trade manual browsing for delegated decision-making. Therefore, brands and retailers should expect new demand patterns and new winners. For example, discovery and merchandising will need to account for agent preferences and optimization rules. Meanwhile, customer acquisition channels could change as agents prefer efficient, well-structured product data and predictable fulfillment.

Ultimately, the business impact is straightforward. Companies that adapt product metadata, API access, and fulfillment promises for agentic interactions will likely capture more of this emerging spend. Conversely, firms that treat agentic AI as just another interface will risk losing share. Therefore, start by testing agent-facing experiences and by investing in predictable delivery and clear product signals that automated agents can trust.

Source: Digital Commerce 360

Measurement and Data: How Agentic AI in Commerce Raises the Stakes for Streaming and Audience Metrics

Agentic AI in commerce will rely on better measurement and richer data. Therefore, news that Nielsen and Roku deepened their data-sharing pact matters beyond TV ratings. The agreement gives Nielsen continued access to Roku’s data while Roku gains better streaming TV ratings. Additionally, better measurement means advertisers and commerce platforms can more reliably connect ad exposure to downstream actions, including purchases initiated by agentic AI.

However, agentic shopping adds complexity. Agents may act on behalf of users across devices and channels, and they may respond to signals that current measurement systems miss. Therefore, improved partnerships between platforms and measurement firms become essential. For example, enhanced streaming metrics can help advertisers understand how content influences agent behavior. Meanwhile, measurement firms will need to expand their models to account for autonomous decision flows and cross-device agent activity.

Ultimately, enterprises should treat measurement as infrastructure. Therefore, companies that invest in first-party data, measurement partnerships, and transparent attribution will be better positioned when agents start executing purchases at scale. Additionally, this is an opportunity for platforms and measurement vendors to innovate new KPIs that reflect agent-driven conversions rather than just clicks or impressions.

Source: Marketing Dive

EDI and Automation: Tangentia’s AI Agent Shows How Agentic Tools Can Solve Operational Friction

Agentic AI in commerce is not only about front-end shopping. Therefore, Tangentia’s addition of an AI agent to its EDI platform is an early example of agents automating core back-office flows. Tangentia said its TiA (Tangentia Intelligent Automation) EDI Agent aims to reduce errors and manual intervention in electronic data interchange operations. Additionally, the company designed the agent to work alongside existing EDI systems rather than replace them.

However, the practical lesson is broader. Many enterprise processes are brittle because they require human triage when exceptions occur. Therefore, agents that can triage, propose fixes, or even act to remediate without full human handoff can cut cycle times and lower costs. Meanwhile, integration-minded designs — agents that complement, not replace, legacy systems — reduce risk and speed adoption.

Ultimately, this points to a pragmatic path for enterprises. Start by identifying high-friction flows with clear rules and predictable exceptions. Therefore, pilot agentic automations where the business logic is stable and the costs of mistakes are manageable. Additionally, emphasize observability and human-in-the-loop controls so teams can audit agent actions. In this way, agents become reliable helpers that improve throughput and free humans for higher-value work.

Source: Digital Commerce 360

B2B Platforms and Partnerships: Why Ambev’s BEES Deal Matters to Buyers and Sellers

B2B commerce will be affected as agentic AI in commerce grows. Therefore, look at Ambev’s agreement to continue using AB InBev’s BEES B2B commerce platform as a concrete example of platform strategy. The BEES platform is a core system for taking orders from retailers and small businesses across markets. Additionally, the deal shows how large sellers rely on centralized platforms to streamline ordering and distribution.

However, agentic agents will change how businesses place repeat and complex orders. Agents could optimize vendor selection, pricing, and delivery schedules across multiple suppliers. Therefore, B2B platforms like BEES will need to expose better APIs, clearer product data, and guardrails for agent behavior. Meanwhile, platform operators must balance openness with controls to prevent agents from gaming pricing or overloading supply chains.

Ultimately, companies that own platform relationships will gain leverage. Therefore, suppliers and distributors should standardize product catalogs and fulfillment promises so agents can evaluate options reliably. Additionally, buyers should negotiate transparency and auditability into platform contracts to ensure agentic decisions remain aligned with human goals. In short, the B2B layer will be as strategic as the consumer storefront in an agent-driven world.

Source: Digital Commerce 360

Trust and Governance: Instacart’s Pricing Pullback Shows the Risks of Opaque Agentic Decisions

Agentic AI in commerce can deliver convenience, but it also surfaces trust issues. Therefore, Instacart’s decision to disable its AI-enabled item pricing tests after customer backlash is a clear warning. The company paused its dynamic pricing experiment following concerns about transparency and fairness. Additionally, Instacart admitted the program fell short of customer expectations.

However, the lesson extends beyond a single company. Consumers expect pricing to be understandable and fair, particularly when algorithms are involved. Therefore, any agentic systems that affect price or availability must include transparent explanations and easy recourse. Meanwhile, regulators and consumer advocates are already focused on algorithmic fairness, so companies should assume scrutiny will grow.

Ultimately, governance matters. Therefore, firms launching agentic pricing or recommendation systems should build clear policies, audit trails, and customer-facing explanations from day one. Additionally, include human oversight in early rollouts and measure both performance and fairness. By doing so, companies can harness agentic efficiency without eroding the trust that underpins long-term customer relationships.

Source: CX Today

Final Reflection: Bridging Opportunity and Trust

Taken together, these stories sketch a near-term roadmap for agentic AI in commerce. Bain’s market projection highlights scale and urgency. Therefore, measurement deals like Nielsen and Roku’s show the data muscle required to connect content and commerce. Additionally, Tangentia’s agent demonstrates how back-office automation will be transformed. Meanwhile, Ambev’s BEES deal underscores platform strategy in both B2C and B2B channels. However, Instacart’s pricing pullback reminds us that speed without transparency undermines trust.

Therefore, companies should act on three practical priorities. First, prepare product and fulfillment signals so agents can make good decisions. Second, invest in measurement and partnerships to track agent-influenced outcomes. Third, build governance and transparency into pricing and recommendation systems. Ultimately, the winners will be organizations that combine operational reliability with clear policies that customers can understand. The future of commerce will be more automated and efficient. However, it will succeed only if companies keep trust at the center of every agentic interaction.