Automated trafficking & naming
Generate trafficking sheets, placement names, and QA checklists.
A Practical Use-Case Map for the Canadian Market
Artificial intelligence is already reshaping every stage of the advertising workflow—from strategy and creative to activation, measurement, and monetization. This reference organizes AI use cases into clear categories and maturity levels, aligning Canada-relevant applications. Use this as a living guide to brief teams, scope proofs of concept, evaluate vendors, and align governance with real-world practice across the Canadian ecosystem.
Generate trafficking sheets, placement names, and QA checklists.
Automatically validate creative files against tech specs and publisher requirements.
Detect broken tags, duplicate pixels, and unauthorized data collection.
Use on-device models to build interest cohorts without raw data exfiltration.
Form cohorts from consented data using privacy-preserving clustering.
Detect and enforce consent flags; route data to compliant destinations.
Probabilistic/ML-based stitching of users across devices and platforms.
Predict user lifetime value to guide targeting, bidding, and suppression.
Find new users similar to high-value seed audiences without direct identifiers.
Score users for likelihood to convert, churn, or respond to offers.
Maintain lineage and conduct bias/stability audits for ad decisioning models.
Classify content suitability against frameworks and advertiser preferences.
Detect bots, click farms, spoofed devices, and domain/app fraud.
Detect toxicity, hate speech, and policy violations in UGC and comments.
Predict viewability before buying; optimize toward viewable inventory.
Transform call transcripts into audience signals and suppression lists.
Automate post-click support and lead qualification with guardrails.
Design offer structures and eligibility rules for margin and conversion.
Rewrite queries and rank results to improve discovery and conversion.
Recommend products/content and personalize experiences across channels.
Generate 3D models and AR try-ons for products within ad experiences.
Generate creative with built-in filters for logos, IP, and sensitive content.
Use AI-generated presenters or models for scalable creative production.
Generate on-brand headlines, CTAs, and long-form ad copy variants.
Check creative against brand guidelines; enforce tone, claims, and visual rules.
Assemble and personalize creative elements in real time based on context and user signals.
Translate and culturally adapt creative while preserving brand voice and compliance.
Upscale, background removal, and spec compliance for retail product images.
Auto-cut, resize, and reformat videos per placement specs and performance patterns.
Create product images, backgrounds, and layouts for ads and landing pages.
Create synthetic voiceovers for video spots and audio ads in multiple languages.
Embed agents inside ads or LPs to answer questions and drive conversions.
Adjust offers and bundles based on inventory, margin, and user intent signals.
Personalize CTV creatives at the edge using household-level context.
Generate scenes, product try-ons, or storylines in real time.
AI co-hosts that demo products and answer Q&A during livestream commerce.
Adapt landing page content and UI components per audience and context.
Design experiments to quantify lift from attention-optimized placements.
Suggest budget shifts based on marginal ROAS and uncertainty.
Link creative features to outcomes; recommend variants to test next.
Model deduplicated reach, frequency capping, and wear-out across platforms.
Auto-design geo/PSA tests and produce lift estimates with guardrails.
Estimate channel contribution with marketing mix models and multi-touch attribution.
Model conversions with sparse signals using Bayesian/causal methods server-side.
Autonomous agents that propose, launch, and iterate campaigns with human-in-the-loop approval.
Predict attention/engagement likelihood to prioritize inventory.
Incorporate carbon signals to optimize for lower-emission inventory.
Predict win rate and conversion to set optimal bids by placement and user cohort.
Auto-adjust budgets and detect under/over-delivery or spend spikes.
Classify page/app context and match ads to relevant content without IDs.
Discover, cluster, and negative-match queries to capture profitable demand.
Optimize bids and placements within retail networks and product grids.
Insert/manage AI-use disclosures and watermarks per policy.
Monitor vendors/models for risk, drift, and policy compliance.
Create privacy-preserving datasets for modeling and experimentation.
Automate DCR workflows: schema matching, QA, and analysis templates.
Detect inadvertent PII in UGC/creative before launch.
Scan creative for regulated claims, disclosures, and required suppressions.
Score publisher content for eligibility in AI answers and negotiate monetization.
Adjust paywall rules to balance ad revenue, subs, and retention.
Auto-tag articles/videos for better ad matching and archives.
Predict floor prices and optimize revenue across direct and programmatic.
Draft creative briefs from business goals, research, and past campaign learning.
Simulate campaign delivery and outcomes using sandboxed digital twins.
Cluster audiences by behaviors and affinities to size opportunities and prioritize targets.
Track competitor creative, spend, placements, and messaging to identify whitespace.
Simulate spend scenarios across channels with expected outcomes and constraints.
Summarize market, consumer, and category reports into insights to inform briefs and strategy.
Monitor signals (search, social, content) and forecast emerging trends relevant to brand and media.
Maturity
How proven and operational a use case is in the Canadian market.
Assessed by: adoption, outcomes vs. benchmarks, operational readiness, governance standards, and stack interoperability.
Are you an IAB Canada member supporting any of these AI use cases in Advertising? Or you have a unique use case you want to share? Submit your details so we can include you on the map.