Industry Snapshot: What agentic AI means for performance marketing
Agentic AI is getting louder in adland - and a lot of the noise misses the point. It won't replace search, social, or marketplaces. It will add new layers of intent and discovery on top of them. That shift doesn't kill performance media; it forces it to grow up.
Agentic commerce is incremental, not disruptive
Consumers already use assistants for research and discovery, but it's additive, not a reset. Criteo research shows 40% of US shoppers now use agentic assistants for product research, while 96% still touch search, social, marketplaces, and brand or retailer sites in the same journey.
Translation: commerce spreads to more surfaces, but the proven channels still matter. Your job is to capture and activate the new intent signals without abandoning what already drives results.
From reactive signals to distributed intent
Performance marketing grew up on reactive inputs - keywords, site visits, retargeting pools, last-click wins. Agentic AI introduces conversational intent that shows up earlier, across multiple interfaces, sometimes before a click exists.
Intent becomes fragmented, contextual, and in motion. The brief shifts from channel-by-channel optimisation to finding high-value intent early and activating while you can still change the outcome.
Execution quality now decides the winner
As discovery turns conversational, weak execution gets exposed fast. Incomplete product data, outdated prices, unclear availability - performance stalls no matter how smart the AI layer appears.
Agentic commerce also calls out habits that many teams still lean on:
- Excessive frequency instead of relevancy
- Isolated optimisation instead of coordinated sequencing
- Volume goals over incrementality
These shortcuts don't hold. More impressions rarely means better performance - and often erodes it.
The role of platforms like Criteo
The opportunity isn't to build another consumer-facing agent. It's to power the data and decisioning layer behind agentic performance. Combining transactional data, a structured product catalogue, and real-time behavioural signals lets you move from "more targeting" to smarter activation.
That means deciding when to engage, when to suppress, and how to sequence exposure based on conversion likelihood. This isn't a black box. Human oversight, transparency, and accountability stay in the loop - the AI handles scale and complexity; marketers stay in control.
"More AI" doesn't mean better performance
Outcomes improve when intelligence is applied with discipline. The strongest results come from:
- Reducing wasted impressions
- Prioritising high-intent audiences
- Sequencing creative to match readiness
- Optimising for incremental growth, not surface metrics
Agentic AI pushes performance from a scale-first mindset to a precision-first one.
What this means for MEA advertisers
- Plan upstream: Don't wait for the final click. Capture early, conversational intent and shape demand before it hits lower-funnel channels.
- Evolve measurement: Update attribution to reflect agent-mediated paths while keeping eyes on outcomes like incremental sales and profit.
- Make data a KPI: Product feeds, availability, pricing accuracy, and consented identity signals are now core performance levers.
Practical next steps for performance teams
- Fix the feed: Standardise titles, attributes, and availability. Automate price and stock syncs. Broken inputs waste budget.
- Intent hierarchy: Map signals from highest to lowest value (conversational queries, product views, add-to-carts, recency). Bid and budget by intent, not just channel.
- Frequency with purpose: Set caps by audience and stage. Suppress recent purchasers; re-include when signals change.
- Sequence creative: Problem/solution for early intent, proof and comparisons mid-funnel, offer and urgency near conversion.
- Test for incrementality: Run holdouts or geo splits. Report net-new impact, not clicks or last-click wins.
- Build suppression logic: Pause spend when price is uncompetitive or stock is low; re-activate when conditions improve.
- Connect conversational signals: Pipe approved query data from assistants where possible, and mirror it in keyword, contextual, and retail media strategies.
A more demanding era for performance
Agentic AI doesn't simplify performance marketing. It raises the standard. Teams with strong data foundations, intelligent automation, and commercial rigor will grow. Teams leaning on blunt tactics will feel the drag.
The mandate is clear: turn complexity into clarity, and make sure that in an autonomous future, performance still performs.
Want to upskill your team for agentic-era performance? Explore AI training for marketing specialists here: AI Certification for Marketing Specialists.
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