Agentic Commerce: How AI Agents Will Transform Canadian Retail

Agentic commerce is a model where AI shopping agents assist consumers in finding products, comparing options, and completing purchases—reducing obstacles throughout the buying journey. McKinsey describes an “automation curve” whereby shoppers gradually delegate more tasks to agents. Retailers that make their product data, policies, and offers readable to machines will be easier for agents to locate and work with.

Retail is entering a new phase of digital transformation: not just personalization, but delegation. In agentic commerce, the customer remains human—but AI shopping agents increasingly handle discovery, decisions, and transactions. McKinsey suggests this evolution won't occur through a single leap to full autonomy. Rather, it will progress along an “automation curve,” shaped by the extent to which people are willing to delegate parts of the commerce journey to machines.

What is agentic commerce?

Agentic commerce refers to shopping and purchasing experiences where an AI agent can search, compare, and potentially complete a transaction on a customer’s behalf—often with a simple approval step.

Think of it as moving from:

• browsing websites and filtering manually, to

• stating an intent (“I need a carry-on suitcase under $120 that meets Air Canada carry-on rules”), and letting an agent handle the details.

What’s new: McKinsey’s “automation curve” for agentic commerce

McKinsey’s latest report introduces the agentic commerce automation curve, mapping how shopping evolves at varying levels of delegation—beginning with rules-based convenience and advancing toward more autonomous, multi-step, multi-agent coordination.

The critical message for retailers is straightforward: agents will increasingly operate upstream, activated by intents like calendar events, reminders, or low-supply alerts. If your offer can't be understood by machines, you risk becoming invisible in agent-mediated shopping journeys.

How AI shopping agents work

Practically, shopping agents typically:

1. interpret user intent (constraints, preferences, budgets, timing),

2. gather options across various merchants,

3. evaluate trade-offs,

4. present a shortlist (or make a recommendation), and

5. complete checkout following approval—sometimes without the user ever viewing a traditional product page.

Why machine-readable retail data becomes the competitive edge

In an agentic world, your “shop window” isn’t solely your website—it’s your data.

Retailers that succeed tend to make key elements structured and machine-readable, such as:

• product attributes and availability,

• shipping and returns policies,

• pricing rules and promotions,

• brand promises (e.g., sustainability claims, warranties),

• and the problems that products solve (not just specifications).

This is because agents require clean, reliable inputs to effectively compare offers and complete transactions confidently.

Practical steps for retailers (what to do now)

Even if fully autonomous shopping hasn’t become mainstream yet, you can prepare without betting the business on hype:

1) Audit your product data for “agent-readiness”

Examine completeness, consistency, and structure across core categories and high-margin lines. Prioritize standard fields and minimize ambiguity.

2) Make policies easy for machines to parse

Returns windows, delivery thresholds, warranties, and exclusions should be explicit and structured—not hidden in lengthy paragraphs.

3) Treat AI discovery as a channel

Optimize so agents can reliably discover and interpret your offers—this is the next stage of SEO: not only ranking for human users but also being usable by machines.

4) Prepare for agent trust, identity, and payments

As more transactions occur through agents, identity verification, fraud control, and secure checkout processes become critical. Industry initiatives are emerging to make autonomous commerce scalable and viable.

Summary and next steps

Agentic commerce is the shift towards AI-mediated shopping, where agents reduce friction and handle more steps from discovery to checkout. McKinsey’s “automation curve” suggests retailers should plan for gradual delegation—not one sudden shift. Businesses that act early on machine-readable retail data and policy clarity will be simpler for agents to locate, compare, and trade with.

Next steps:

• Identify 1–2 categories to make “agent-ready” first (data + policies + offers).

• Build metrics around conversion, returns, and customer satisfaction in those categories.

• Consider agent discovery and checkout as an emerging channel—then iterate.

FAQs

Q1: What is agentic commerce?
Agentic commerce involves shopping where an AI agent can search, compare options, and potentially make a purchase on the customer’s behalf, often requiring just a simple approval step.

Q2: How do AI shopping agents work?
They convert user intent into constraints, gather options, assess trade-offs, and either propose a shortlist or complete checkout post-approval.

Q3: What benefits does agentic AI offer retailers and customers?
It can reduce purchasing friction, enhance match quality, and speed up decisions—while encouraging retailers to improve data quality and policy clarity so agents can transact reliably.

Q4: What does McKinsey mean by the “automation curve”?
It’s a framework explaining levels of delegation, from basic convenience automation to more autonomous agent-driven shopping journeys.

Q5: What should retailers do first?
Begin by standardizing product and policy data so it's machine-readable, then treat AI/agent discovery as a channel you actively optimize for.