AI-Powered Intent Layers: Amazon & Walmart’s 2026 Plan

Most retailers still think competition is about price, shipping speed, and store execution. Amazon and Walmart are betting on something else for 2026: owning “intent”—the signals that reveal what a shopper is likely to do next—and turning that into a monetization layer that sits on top of commerce.

That matters well beyond retail. If intent becomes the dominant layer, then payments and fintech infrastructure become the plumbing that converts intent into revenue: underwriting, identity, fraud controls, routing, loyalty, subscriptions, and settlement. And AI is the engine that makes those layers intelligent enough to run at Amazon/Walmart scale.

This post is part of our AI in Retail & E-Commerce series, where we track how personalization, forecasting, and customer analytics are evolving into full-blown platform economics. Here’s the punchline: retail media networks and AI-driven data services are converging with payments—and it’s going to reshape how commerce businesses build partnerships, measure ROI, and manage risk.

“Intent” is the new retail monetization layer

Intent is the highest-value data asset in commerce because it predicts near-term spend. A cart add, a search term, a repeat replenishment interval, a store-visit pattern—these aren’t just marketing signals. They’re financial signals.

Amazon and Walmart already sit on massive volumes of first-party behavior, but the strategic shift is what they do with it:

• Retail media advertising that targets shoppers at the moment they’re most likely to buy
• Data products and AI-driven services that turn raw behavior into usable predictions
• Subscriptions and membership benefits that convert one-time buyers into annuity revenue
• Media-like distribution surfaces (onsite, in-app, streaming, in-store screens) that create more “inventory” for monetization

The reality? The store (physical or digital) becomes the stage. Intent becomes the script. Payments becomes the conversion engine.

Why intent beats “audience” for monetization

Traditional advertising sells audiences (“reach these demographics”). Retail media sells purchase intent (“reach people likely to buy toothpaste this week”). That’s why retail media budgets keep shifting from broad channels toward marketplaces and big-box ecosystems: marketers can tie spend to baskets.

And once you can predict baskets, you can also predict:

• likelihood of returns
• likelihood of fraud
• probability of subscription renewal
• sensitivity to fees and financing offers
• propensity to switch brands

Those are monetizable signals—if your infrastructure can operationalize them safely.

How Amazon and Walmart use AI to turn commerce into “media”

AI turns commerce surfaces into programmable monetization surfaces. This isn’t only recommendation engines anymore. It’s a stack of models that decide which product, offer, ad, and payment option to show—based on context.

The two companies will execute differently (Amazon has the cloud and marketplace DNA; Walmart has store density and grocery frequency), but the underlying AI mechanics look similar.

Retail media networks: AI decides what gets seen

Retail media doesn’t work if targeting is sloppy. Brands pay for outcomes, and outcomes require prediction quality.

AI is used to:

• score shoppers by category intent (e.g., “buying a new TV” vs. “replacing HDMI cables”)
• allocate ad placements based on margin, inventory levels, and conversion probability
• detect ad fraud and invalid traffic (especially as onsite and offsite placements expand)
• optimize frequency and creative selection to reduce wasted impressions

Here’s what many teams miss: retail media performance depends on the integrity of identity and transaction attribution. If your identity graph is weak or your payment events don’t reconcile cleanly, your ROAS story falls apart.

Data services: packaging predictions as products

The next step is selling insights, not impressions.

When a platform can forecast demand by ZIP code, detect shifting brand affinity, or model substitution behavior during price changes, that becomes a service:

• for CPG brands planning promotions
• for sellers managing inventory and pricing
• for financial partners underwriting merchant cash flow

In practice, the “product” is usually a bundle: analytics dashboards, APIs, and model outputs. AI makes those outputs timely. Payments data makes them provable.

Subscriptions and memberships: intent turned into annuity

Subscriptions (free shipping, fuel discounts, streaming bundles, grocery perks) are often framed as loyalty plays. They’re also risk models and payment reliability plays.

Once you’re paying monthly, the platform learns:

• whether your payment method is stable
• whether you’re rate-sensitive
• what benefits change your behavior

That creates opportunities for smarter billing, reduced churn, and more targeted benefits—while also increasing the platform’s negotiating power with brands and financial partners.

The hidden connection: intent monetization runs on payments infrastructure

Intent without conversion is just analytics. Monetization requires trusted payment execution. The more Amazon and Walmart monetize intent, the more they depend on a high-performance fintech stack beneath it.

This is where the story shifts from retail strategy to AI in payments and fintech infrastructure.

1) Fraud prevention becomes “intent-aware”

Fraud teams have always looked for anomalies. The best programs now look for intent mismatches.

Examples:

• A shopper’s behavior suggests replenishment purchasing, but the transaction looks like a one-off high-risk electronics buy.
• Device and location patterns don’t match the customer’s typical journey.
• Cart velocity and payment retries resemble bot behavior rather than human browsing.

AI models can fuse behavioral, device, and transaction signals to make better decisions in real time:

• fewer false declines (which protects conversion)
• faster step-up authentication when needed
• smarter hold/review logic for high-risk orders

If you’re building fintech rails for commerce platforms, this is the bar: risk decisions must understand the shopping journey, not just the card event.

2) Routing and authorization optimization becomes a revenue lever

When intent is monetized through ads, every extra basis point of conversion matters. That pushes platforms to optimize:

• authorization rates by payment method and issuer
• smart routing for cost and performance
• retries and fallbacks that don’t trigger fraud rules
• tokenization and network credentials to keep payments “fresh”

In 2026, I expect more commerce ecosystems to treat payment orchestration as a growth function, not a back-office function—because the monetization layer depends on reliable conversion.

3) Attribution depends on clean payment events

Retail media lives or dies on measurement. But measurement is messy when:

• orders are split across shipments
• returns happen weeks later
• customers buy online but pick up in store
• multiple payment methods are used across a journey

AI can help with multi-touch attribution, but it still needs accurate, reconciled payment and order data. If your data model can’t tie “ad exposure → cart add → payment authorization → settlement → return” into one lifecycle, you’ll over-credit ads and undercount losses.

Snippet-worthy truth: Retail media is really a financial reporting problem disguised as a marketing channel.

4) Subscriptions change the economics of identity

A subscription relationship gives the platform stronger identity signals (account tenure, billing history, device consistency). That improves:

• fraud detection
• credit decisions for pay-over-time offers
• approval rates through trusted credentials

But it also raises the stakes on account takeover and credential stuffing. The winning stacks will combine:

• behavioral biometrics
• device intelligence
• step-up authentication tuned to lifetime value

What mid-market retailers and fintechs should do now (practical moves)

You don’t have to be Amazon or Walmart to build an intent layer. But you do need to get serious about data quality, decisioning, and the plumbing between commerce and payments.

Build an “intent ledger,” not just a customer profile

A customer profile is static. An intent ledger is event-driven and time-ordered.

Start capturing and standardizing:

• search, browse, add-to-cart, remove-from-cart
• store visits and pickup interactions (where applicable)
• promotion exposure and redemption
• payment attempts, declines, retries, and method changes
• returns, refunds, and chargebacks

The goal is simple: a single timeline of intent → transaction → post-transaction outcomes.

Treat fraud, marketing, and payments as one system

Most companies separate these teams and then wonder why performance plateaus.

Operational changes that actually move metrics:

1. Shared KPIs: pair ROAS with fraud rate and return rate. If ROAS rises while returns spike, you’re buying bad intent.
2. Unified experimentation: A/B test checkout changes with risk controls in the loop.
3. Closed-loop learning: feed chargeback and return outcomes back into targeting and offer models.

Monetize responsibly: governance is part of the product

When you monetize intent (ads, data products, AI services), trust becomes a business asset.

Non-negotiables for 2026:

• clear consent and preference management
• strong internal controls on who can query what
• anonymization where appropriate
• auditability for model decisions that affect eligibility or pricing

If governance feels like a drag, you’re thinking too short-term. Trust is what keeps the monetization layer durable.

People also ask: what does “intent” mean in retail AI?

Retail intent is the probability a shopper will take a specific action within a time window—buy, subscribe, renew, return, or churn—based on behavioral and transaction signals.

The best intent models use:

• recency and frequency of interactions
• category and brand affinity
• price sensitivity inferred from substitutions and promo behavior
• fulfillment preferences (ship, pickup, same-day)
• payment signals (method stability, decline patterns)

When those models feed retail media, personalization, and checkout decisions, monetization scales fast.

Where this goes in 2026: intent will price everything

As Amazon and Walmart build monetization layers above commerce, intent starts to influence pricing across the ecosystem—ad auctions, offer eligibility, subscription bundles, and payment options.

For payments and fintech infrastructure teams, that’s the opportunity and the warning. The opportunity: build AI-driven routing, risk, and identity systems that improve conversion while controlling loss. The warning: if you can’t connect commerce events to payment outcomes, you’ll be stuck optimizing one part of the funnel while the real profit moves elsewhere.

If you’re mapping your 2026 roadmap, I’d start here: Which intent signals do we have, which ones do we need, and how quickly can we turn them into safe decisions at checkout? That answer will determine whether you’re renting demand—or owning it.