Industry
Retail
Omnichannel CX and Intelligent Innovation – The Future of Retail
HIGHLIGHTS
- The next frontier in retail media will be delegated decision-making, where agents, not humans, will be the true shoppers. This requires retail media networks (RMNs) to evolve into intelligence networks that don’t just sell ad space but orchestrate AI-ready data, commerce logic, and decision APIs.
- As retail media evolves from influencing humans to influencing algorithms, the winners won’t be brands with the most ad inventory but those with a strong technology architecture and robust data that comply with ethical standards.
- By investing in an agentic infrastructure, adopting new models of measurement, creating agent-readable offers, prioritising data ethics, and integrating with agentic ecosystems, retail media networks can thrive in this new landscape.
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The rise of AI shoppers
Retail media networks have reshaped how brands connect with consumers, turning first-party data into a new form of digital currency. But just as the industry begins to master the mechanics—clean rooms, offsite activation, closed-loop measurement—a new disruption is already emerging. It is agentic commerce.
Agentic commerce is powered by autonomous, goal-driven artificial intelligence (AI) agents that don’t just recommend products but also make decisions, manage lists, optimise baskets, and even negotiate value on behalf of consumers. If the last decade was about precision marketing, the next would be about delegated decision-making—where agents, not humans, are the true shoppers. And that will fundamentally rewrite how retail media works.
From ad exposure to action influence
Typically, retail media is built on exposure: impressions, clicks, and conversions tracked across banners and sponsored products. But in an agentic world, there’s no eyeballs to target—only an algorithmic agent deciding between brand A and brand B based on data, constraints, and user-defined goals (see Table 1).
| Old RMN model | Agentic commerce model |
| Ad placement on a digital shelf | Influence within an AI agent’s decision engine |
| Bid for visibility | Bid for recommendation weight |
| Impressions and clicks | ‘Agentic actions’ (add-to-cart, substitute, reorder) |
| Attribution and return on ad spend (ROAS) | Causal lift on agentic decision outcomes |
Table 1: The value chain shift
Retailers and advertisers will need to stop thinking in pixels and start thinking in policies—rules, weights, and optimisation inputs that shape how agents interpret value. In other words, agentic retail media won’t sell space—it will sell influence inside a machine’s reasoning loop.
Data liquidity: The new currency of retail media
In a cookieless world, retail media has thrived because of deterministic, first-party data. But agentic systems will take this further, requiring real-time, contextual data streams to make micro-decisions.
Agents will weigh price elasticity, inventory, delivery time, sustainability scores, and even user health goals to decide what to buy. Retailers that can expose these data attributes through secure application programming interfaces (APIs) and real-time feeds will control the agentic supply chain of decisioning. This means the new currency of retail media is data liquidity—how swiftly, cleanly, and contextually a network can feed agents with the right signals.
AI influence
Imagine a near future where millions of household AI agents autonomously shop weekly groceries. If those agents are integrated with multiple retailers—Instacart, Walmart, Kroger, Amazon—the fight for share of wallet will not happen in search results, but inside algorithmic preference frameworks.
Retailers will need to optimise for agent influence:
- Is your brand represented in the agent’s preference graph?
- Is your inventory and price data available in machine-readable formats?
- Can your promotions be interpreted as value rules instead of visual creatives?
This is where RMNs must evolve into retail intelligence networks—platforms that do not just sell ad space but orchestrate AI-ready data, commerce logic, and decision APIs.
Measurement will be another challenge. Traditional attribution assumes a user journey that can be tracked—views, clicks, conversions. But in an agentic economy, there is no clickstream; just a sequence of autonomous decisions driven by multi-factor optimisation.
To survive, RMNs will need new models of measurement:
- Causal lift measurement: Did the brand’s bid or promotion meaningfully change the agent’s selection behaviour?
- Action-level attribution: What percentage of substitutions or reorders were agent-driven?
- Policy optimisation metric: Which data signals most influence agentic actions across networks?
The key performance indicator (KPI) of the future will not be the click-through rate, it will be the AI influence rate (AIR)—how often your brand or product is selected by an autonomous agent in a competitive decision frame.
Guidelines for pivoting to agentic AI
Agentic commerce changes the locus of power:
- Consumers become curators of intent, setting parameters like price range, brand affinity, or health goals.
- Agents become decision intermediaries—interpreting, optimising, and acting.
- Retailers and brands must compete not for human attention, but for AI recommendation logic.
In that world, retail media becomes not just an advertising channel but a machine-to-machine negotiation layer, where data, incentives, and outcomes are continuously exchanged between buyer and seller algorithms.
Hence, RMNs will have to transform into adaptive marketplaces for AI-driven decision commerce, powered by transparent APIs, dynamic pricing, and ethical guardrails.
Here’s a blueprint for readiness:
- Invest in an agentic infrastructure: Build APIs that expose product, price, and inventory data in real-time.
- Adopt AI-native measurement: Move from ROAS to causal and behavioural lift metrics.
- Design for AI negotiation: Create agent-readable offers, promotions, and loyalty structures.
- Prioritise data ethics and explainability: Transparency will drive trust—both for regulators and consumers.
- Build AI partnerships: Integrate with agentic ecosystems (e.g. Google Gemini, OpenAI’s shopping agents, Amazon Rufus, NVIDIA frameworks) to ensure your network is visible within agent environments.
As retail media evolves from influencing humans to influencing algorithms, the winners won’t be brands or retailers with the most ad inventory but those with a strong technology architecture and robust data that comply with ethical standards. Essentially, it’s about creating an environment where the AI agent perceives your brand as the most logical and reliable option when fulfilling a consumer’s intent.