AI in advertising

This week Interactive Advertising Bureau (IAB) released a new report on AI use cases in advertising. It is very comprehensive and a good starting point to understand how AI is impacting the advertising business.

Their use case map is a little hard to read in one go. So I have reformatted their content in a easy to read format below.

Use cases of AI in advertising (from IAB Report)

🌱 = emerging use case

Audience Insights

• Real-time sentiment analysis & trend detection: Analyzes multimodal signals including text visual and audio to surface fast-moving patterns and consumer moments that may not be evident through language alone. Helps brands understand shifting consumer attitudes early-stage demand signals and cultural momentum.
• Customer value & engagement modeling: Uses AI-driven predictive models to analyze behavioral and transactional data to forecast customer outcomes including likelihood to convert churn risk and customer lifetime value while identifying real-time moments of high engagement or risk to deliver targeted actions and optimize outcomes.
• Lookalike and behavioral cohort prediction: Identifies and targets new audience segments that mirror high-value customers’ behaviors and attributes increasing campaign relevance and effectiveness.
• Synthetic data generation for modeling and experimentation 🌱: Creates realistic privacy-safe artificial data sets that mimic real-world customer data to train test and refine models without exposing sensitive information
• AI-powered customer experience mapping and journey optimization 🌱: Analyzes customer interactions across channels to map journeys identify pain points and suggest optimizations for smoother more engaging customer experiences.
• Cross-sell/upsell recommendation engines 🌱: Uses machine learning to recommend complementary products or upgrades based on a customer’s profile past purchases and behavior.
• AI-powered customer identity mapping/unification across platforms 🌱: Consolidates fragmented customer data from various sources into unified profiles using AI matching and deduplication enabling consistent targeting and personalization.
• Synthetic audience testing 🌱: Leverages synthetic personas to test creative and media strategies before campaign launch to estimate performance across underserved or hard-to-measure segments.
• Alternative-data discovery & evaluation 🌱: Uses AI to discover and evaluate new external data sources like social media trends or partner data that can improve customer insights. Automatically checks for value and privacy risks and if it passes all hurdles automatically imports that data into the company’s systems.
• Predicting customer value & behavior 🌱: Uses AI to analyze customer behavior and predict both lifetime value and likely next actions. Helps marketers better segment audiences and target high-value users with greater precision.
• Voice of customer analysis from surveys: Uses natural language processing to extract actionable insights from structured internal feedback (surveys reviews customer support) about satisfaction pain points and improvement areas. Focuses on direct customer feedback not public data.

Media Strategy & Planning

• AI-driven audience targeting and segmentation with privacy-safe methods: Uses AI to identify precise audience segments based on behavior and demographics without relying on personal identifiers ensuring compliance with privacy regulations.
• Budgeting & Cross-Channel Media Allocation: Applies predictive analytics to allocate media budgets dynamically across channels optimizing spend for maximum return based on forecasted performance.
• Dynamic media mix modeling 🌱: Continuously updates the ideal mix of media channels using machine learning adjusting for performance trends and external factors in real time.
• AI-assisted campaign briefing and planning 🌱: Employs AI tools to generate draft campaign briefs suggest targeting options and streamline planning by analyzing objectives audience data and market insights.
• Contextual targeting using AI-based content analysis: Analyzes page content including text images sentiment and metadata to serve ads in relevant and/or emotionally appropriate contexts without using third-party cookies. Ensures brand assurance tone alignment and contextual relevance.
• Seasonality and market condition predictions 🌱: Uses AI to analyze past data and anticipate the impact of seasonal trends (like holidays) or market shifts (like inflation) enabling proactive adjustments to plans.
• Opportunity identification in emerging channels 🌱: Scans new platforms (e.g. social platforms CTV) and behavioral trends to spot early advertising opportunities and emerging consumer touchpoints.
• Audience attention prediction and media selection 🌱: Predicts where and when audiences are most likely to engage and helps select media channels and placements that maximize attention and effectiveness.
• Dynamic summaries of inventory performance: Generates dynamic visual summaries of available ad inventory and performance data to guide media planning decisions. Enables real-time identification of high-performing markets or platforms optimizing media mix and allocation strategies.
• AI-driven knowledge search and summarization 🌱: Equips marketing and planning teams with AI tools to efficiently search summarize and synthesize insights from internal data and resources. Enhances strategic decision-making by delivering fast actionable intelligence from reports performance metrics and industry research.
• Keyword expansion 🌱: Uses AI to expand keyword lists for search and social campaigns by identifying semantically related terms long-tail variations and competitor-derived keywords. Enhances campaign reach targeting precision and discovery.
• Competitor insights including spend analysis: Leverages AI to analyze competitor activities including advertising spend patterns creative approaches and channel mix providing marketers with strategic insights to refine their own campaigns.

Creative & Personalization

• Automated creation and editing of copy, images, and video, including AI-powered tools like outpainting, inpainting, background removal, and upscaling: Generates ad copy, visuals, video content, and edits live video using AI, enabling fast creation of creative variations and scaling content production.
• AI-supported campaign activation & ecommerce personalization: Uses AI to turn advertiser briefs into ready-to-launch campaigns automate QA troubleshoot setup issues and surface cross-channel performance insights.
• Interactive & Immersive Content Creation (chatbots, games, AR/VR, 3D, ads) 🌱: Uses AI to design and deliver dynamic, engaging experiences that respond to user input or immerse audiences in branded environments. This includes interactive ads (e.g., virtual try-ons, responsive video), AI-generated chatbots and mini-games, and AR/VR/3D content (e.g., immersive product demos or branded worlds). Drives higher engagement and connection across formats and platforms.
• Voice and audio content creation 🌱: Generates audio assets like voiceovers podcasts and jingles using AI-powered voice synthesis.
• Real-Time creative personalization & optimization: Uses AI to dynamically tailor and optimize ad creative across paid and owned channels based on real-time behavioral contextual and performance signals. Enables 1:1 personalization for individual users as well as scaled optimization in programmatic campaigns to improve engagement relevance and conversion.
• Cultural adaptation and localization of creative assets 🌱: Adjusts creative elements like language visuals and references to resonate with diverse cultures and local contexts using AI-driven analysis.
• AI-powered creative testing and optimization: Runs automated tests of multiple creative elements (headlines images CTAs) to identify and optimize high-performing combinations based on real-time feedback generating high-performing ads at scale and continuously optimizing based on real-time feedback. Uses AI to generate and test creative variations (headlines images CTAs) identifying high-performing combinations based on real-time feedback. Also incorporates brand guidelines and historical campaign performance to inform strategic creative direction such as DCO strategy messaging tone and content formats ensuring alignment with brand identity and past learnings.
• Creative briefing assistants and concept exploration 🌱: Employs AI tools (e.g. text or image generators) to help teams brainstorm create initial drafts and shape creative concepts faster and more collaboratively.
• AI-powered competitive creative analysis with multimodal recognition 🌱: Uses AI to analyze competitors’ creative content across multiple formats including visual audio video and design to identify trends brand signatures and audience engagement patterns. Provides holistic insights to refine creative strategies and differentiate brand expression across channels.
• Dynamic creative adaptation for channel and format: Automatically adjusts creative elements (images text layout) to fit the specifications and audience preferences of each marketing channel maximizing impact and consistency across platforms.
• Automated social content generation: Focuses on AI tools that help human marketers produce posts images captions and scheduled content but with human oversight and strategy driving the output.
• AI-powered virtual product placement in media 🌱: Uses AI to seamlessly integrate branded products into existing media including TV shows films and digital content creating scalable non-intrusive ad placements that enhance brand visibility and engagement.
• AI-generated product detail page content 🌱: Uses AI to generate optimize and localize PDP images and copy tailored to retailer-specific requirements. Balances creative effectiveness with fixed brand language (e.g. claims/disclaimers) and employs pre-syndication scoring against consumer competitor and brand signals to enhance performance across retail platforms.
• AI-Powered Music Selection for Ads 🌱: Uses AI to select music tracks and sound effects that align with an ad’s mood target audience and creative goals. Enhances emotional resonance storytelling and brand alignment through optimized audio choices.

Owned & Earned Media

• AI-powered social content agents 🌱: Autonomous agents that generate and manage brand-aligned content for social media including text visuals and engagement either without human overview or without it reducing manual effort.
• Content scheduling and optimization: Uses AI to determine the best times and platforms for publishing content to maximize reach and engagement.
• SEO content optimization: Optimizes web content (including keywords structure and metadata) for better visibility in search engine results and AI-powered discovery tools.
• AI-native content visibility optimization 🌱: Optimizes how brand content and ads appear in AI-native environments (e.g. ChatGPT Perplexity Claude) by using AI to analyze response patterns optimize metadata and enhance content discoverability. Ensures brand accuracy and relevance in AI-generated search results and conversational responses capturing organic visibility in emerging channels.
• AI tools for monitoring and improving brand representation in AI responses 🌱: Uses AI to evaluate how a brand is represented in large language model outputs and other generative AI interfaces (e.g. ChatGPT Perplexity). Identifies misrepresentation opportunities or inconsistencies in how the brand is surfaced in AI-generated responses and helps correct or optimize that presence through content metadata or strategic interventions.
• Predictive PR outreach and media relation management 🌱: Utilizes AI to forecast media interest and tailor outreach strategies for optimal engagement.
• Automated earned mention monitoring and analysis: Uses AI to continuously track and evaluate brand mentions across media channels (e.g. press blogs social). Measures sentiment reach and impact of earned coverage to inform PR performance brand perception and share of voice.
• Content authority and expertise scoring 🌱: Applies AI algorithms to evaluate and rank content based on credibility relevance and authoritativeness.
• Automated reputation management and response generation 🌱: Uses AI to monitor brand reputation across digital channels and detect any emerging harmful narratives sentiment shifts or misinformation patterns. Supports early mitigation through alerts and content response generation.
• Crisis prediction and mitigation planning 🌱: Leverages AI to identify potential crises early and formulate proactive mitigation strategies.
• AI-powered influencer identification and performance prediction 🌱: Implements AI to discover suitable influencers and predict their potential impact on campaign goals.
• AI-Powered content repurposing: Transform long-form owned content (e.g. blogs articles webinars) into channel-ready short formats like social posts emails or infographics with AI. Boosts reach relevance and efficiency across owned and earned media channels.

Media Buying & Activation

• Autonomous pacing and spend optimization agents 🌱: Deploys AI agents to dynamically adjust ad spend and pacing for maximum ROI.
• Rules-based media execution agents 🌱: Utilizes predefined rules within AI systems to automate media buying decisions and placements.
• Cross-channel delivery orchestration agents 🌱: Coordinates ad delivery across multiple channels using AI to ensure cohesive campaign execution.
• Send-time and channel optimization for outreach: Applies AI to determine the optimal timing and channels for message delivery to enhance engagement.
• Real-time bidding optimization: Uses AI to adjust bids in real-time auctions maximizing ad placement efficiency.
• Fraud detection and prevention: Employs AI to identify and prevent fraudulent activities in advertising transactions.
• Inventory forecasting and opportunistic buying 🌱: Leverages AI to predict inventory availability and capitalize on advantageous buying opportunities.
• Audience fatigue prediction and frequency capping optimization 🌱: Utilizes AI to detect audience fatigue and adjust ad frequency to maintain engagement levels.
• Supply path optimization via AI: Applies AI to streamline the ad supply chain reducing costs and improving efficiency.
• Dynamic pricing and merchandising optimization: Uses AI to dynamically adjust pricing and inventory allocation in response to demand competition and market signals maximizing revenue and efficiency in programmatic media and e-commerce environments.

Measurement & Analytics

• AI-driven attribution and conversion path analysis: Uses AI to analyze customer journeys and attribute conversions to appropriate touchpoints.
• Performance forecasting and campaign health monitoring 🌱: Employs AI to forecast marketing performance by analyzing historical data campaign inputs and real-time signals. Supports proactive decision-making through forward-looking insights campaign health monitoring and strategic scenario modeling.
• Creative effectiveness scoring 🌱: Applies AI to evaluate the impact of creative assets on campaign success.
• Audience engagement dashboards powered by AI: Provides AI-enhanced dashboards that offer insights into audience interactions and behaviors.
• Conversational analytics AI Assistants 🌱: Utilizes AI assistants to interpret and analyze conversational data for actionable insights.
• Automated anomaly detection and alerting 🌱: Deploys AI to identify irregularities in data investigate the irregularities and trigger timely alerts.
• Natural language querying of marketing data 🌱: Enables users to interact with marketing data using natural language facilitated by AI.
• Marketing data cleaning and preparation agents 🌱: Uses AI agents to automate the cleansing and structuring of marketing datasets.
• Post-campaign analysis automation and insight generation 🌱: Applies AI to streamline post-campaign evaluations and extract meaningful insights.
• Cross-platform/cross-campaign learning and optimization 🌱: Utilizes AI to analyze and optimize strategies across various platforms and campaigns.
• Automated data collection and integration into data platforms: Automates the collection and integration of performance and campaign data into data platforms for streamlined analysis. Reduces manual data handling ensuring accurate and timely insights for optimization and reporting.
• AI-Powered sponsorship ROI modeling 🌱: Uses machine learning to estimate the long-term ROI of sponsorship investments (e.g. event branding sports partnerships) by modeling brand lift media value and conversion paths.
• Emotion-based creative resonance measurement 🌱: Uses AI to analyze facial expressions and micro-emotions in real time while viewers engage with ads providing granular insight into emotional responses such as joy confusion or indifference. Supports creative testing and validation based on emotional resonance rather than attention alone.
• Federated & clean-room model training 🌱: Uses AI to train models across multiple data sources (e.g. brands or partners) without moving or sharing raw data. This allows companies to generate insights collaboratively while preserving privacy and staying compliant with regulations like GDPR and CPRA.
• Automated data quality and change detection 🌱: Uses AI to automatically monitor marketing data for quality issues structural changes or unexpected patterns over time. Detects problems early alerts teams and helps prevent broken models or dashboards.

Brand Assurance

• AI hallucination and misinformation detection 🌱: Employs AI to identify and mitigate the spread of false or misleading information.
• Deepfake and synthetic media detection 🌱: Uses AI to detect and address manipulated or synthetic media content.
• Regulatory compliance monitoring across local markets 🌱: Uses AI-driven systems to monitor and ensure compliance with diverse advertising laws
• Bias detection in targeting and creative 🌱: Applies AI to uncover and correct biases in audience targeting and creative content.
• Cultural sensitivity analysis 🌱: Leverages AI to assess content for cultural appropriateness and sensitivity.
• AI ethics monitoring and governance 🌱: Implements AI systems to oversee ethical considerations and governance in AI applications.
• Carbon footprint measurement for digital campaign 🌱: Calculates the carbon emissions associated with digital campaigns, including impressions, ad delivery, and server loads, to help marketers assess and reduce environmental impact.
• Automated competitive separation enforcement 🌱: Ensures competing brands or products do not appear adjacent to each other in advertising placements by using AI to track and enforce competitive separation rules across media channels.
• Automated content rights verification 🌱: Automatically checks content for proper licensing and rights management before it is published, reducing the risk of copyright violations and ensuring compliance.
• Malvertising and cloaking detection 🌱: Uses AI to detect and block malicious ad content (malvertising) and cloaking tactics that deceive ad reviewers or platforms. Ensures only legitimate and safe ads are delivered to users, protecting brand reputation and user trust.
• Account takeover detection and response: Leverages AI to detect unusual login behavior, access anomalies, and unauthorized ad changes indicative of account takeover attempts. Protects advertisers and publishers from fraud, reputational damage, and budget misuse by triggering real-time alerts and automated containment.
• AI-powered bias and cultural sensitivity detection: Uses AI to detect and flag potentially biased, stereotypical, or culturally insensitive content including language, imagery, tone, or voice across global markets. Also enables proactive application of inclusive best practices during content generation to support respectful, inclusive, and context-aware messaging that protects brand reputation across diverse audiences.
• AI-assisted IP risk detection for AI-modified assets: Uses AI to analyze uploaded or modified creative assets to identify potential IP conflicts, misuse, or compliance gaps before content is published. Flags scenarios where AI-generated or edited assets may require rights clearances, trigger brand liability, or violate indemnification terms. Helps advertisers and publishers preempt legal exposure when leveraging AI tools.
• AI-powered influencer content pre-screening: Analyzes influencer content, including language, imagery, past posts, and proposed sponsored assets, to ensure brand alignment and legal compliance before publication. Flags potential risks like off-brand tone, misinformation, or content that may trigger reputational harm.
• AI-Enabled creative accessibility compliance: Uses AI to evaluate and enhance the accessibility of advertising creatives by generating alt text, ensuring appropriate color contrast, and adding subtitles or audio descriptions. Supports inclusive design practices and compliance with accessibility regulations.
• Compliance QA agents for brand assurance: Leverages AI agents to autonomously check and enforce compliance for individual content pieces across brand assets ensuring proper disclosures (e.g. sponsorships AI labeling) and adherence to brand assurance guidelines. Operates both pre- and post-publication flagging issues for human review or automatic correction.

Content Protection & IP Licensing

• Autonomous IP violation detection agents 🌱: Uses AI to proactively monitor for unauthorized use of publisher content by comparing media assets (text images video) across digital platforms. Detects copyright violations and may initiate automated enforcement actions such as flagging alerts or takedown requests helping protect IP and uphold licensing rights.
• Automated licensing workflow engines 🌱: Automates the entire process of licensing content from tracking usage and terms to handling payments and renewals reducing manual intervention and speeding up processes.
• Yield forecasting for monetization optimization 🌱: Uses AI models to predict future ad inventory availability and expected revenue enabling publishers to optimize pricing inventory allocation and direct deal strategies.
• Content authentication chains 🌱: Maintains a tamper-evident record of content creation and edits using blockchain or other ledger technologies providing verifiable authenticity and a secure chain of custody.
• Smart contracts for licensing using blockchain 🌱: Implements blockchain-based contracts that automatically execute licensing terms (such as payments and rights revocation) without manual processes.
• AI-powered content valuation 🌱: Predicts the potential value of content (articles videos images) using AI analysis of metadata past performance and engagement metrics to support monetization strategies.
• AI-powered advertiser asset protection 🌱: Uses AI to detect unauthorized reuse mimicry or manipulation of advertiser-owned creative assets (images videos logos copy) across digital environments. Helps safeguard brand integrity and enforce IP rights for AI-generated or traditional advertising content.
• Content watermarking: Applies digital watermarks and metadata (C2PA standards) to track content origin ensure authenticity and detect unauthorized modifications or use.