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border-top","layout":"SIMPLE","id":"container-1a17086b50",":items":{"text_894059747":{"id":"text-a5e783cc84","additionalClasses":"seo-hub-hero__related-topic-label","text":"\u003Cp\u003EAI in Industry Topics:\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text":{"id":"text-b0bab2339e","additionalClasses":"related-topics ","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-cybersecurity/\"\u003EAI in Cybersecurity\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-finance/\"\u003EAI in Financial Services\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-healthcare/\"\u003EAI in Healthcare\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-manufacturing/\"\u003EAI in Manufacturing\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-retail/\"\u003EAI in Retail\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small"}},":itemsOrder":["text_894059747","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container","appliedCssClassNames":"snowflake-flexible-column-container-gray-10-bg"},"flexible_column_cont_663228916":{"id":"flexible-column-container-87ebf782d7","propertiesId":"hub-body","type":"2-column-60-40","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"medium","bottomPadding":"medium","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"propertiesCSSClasses":"page-section","backgroundImageOption":"none","flexible_column_content_container_1":{"additionalClasses":"longform-content","layout":"SIMPLE","id":"hub-body-content",":items":{"text__0":{"id":"text-c28911ec6c","text":"\u003Cp\u003EThe advertising industry is implementing new forms of AI and automation while reassessing the foundations beneath the old ones. In a \u003Ca href=\"https://www.iab.com/insights/2026-state-of-data-report/\" target=\"_blank\"\u003E2026 survey conducted by the Interactive Advertising Bureau\u003C/a\u003E (IAB), 60%–75% of advanced-measurement users said current approaches fall short on rigor, timeliness, trust or efficiency. About half were already scaling AI, with most of the remaining respondents expecting to follow within two years.\u003C/p\u003E\n\u003Cp\u003EAs the use of AI in advertising expands, teams have more room to test ideas, adapt creative, refine targeting and iterate while a campaign is still running. But as \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/generative-ai/\"\u003Egenerative AI\u003C/a\u003E and \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/agents/\"\u003Eagentic AI\u003C/a\u003E expand what advertisers can do, they also increase the importance of the information and governance surrounding the system.\u003C/p\u003E\n\u003Cp\u003EDennis Buchheim, Global Head of Media, Entertainment and Adtech/Martech at Snowflake, \u003Ca href=\"https://www.snowflake.com/en/blog/advertising-media-predictions-2026/\"\u003Eputs it this way\u003C/a\u003E: “The technology is moving fast, but what really determines success is whether you can control, govern and audit what’s happening.”\u003C/p\u003E\n\u003Cp\u003EAdvertisers using AI now must address both sides of the equation — the expanding capabilities of the technology and the data foundation and controls required to keep decisions aligned with each campaign’s purpose.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-advertisers-use-ai-across-the-campaign-lifecycle":{"id":"title-v2-bca9de3fe2","additionalClasses":"anchor-title anchor-title--how-advertisers-use-ai-across-the-campaign-lifecycle","type":"heading2","lines":["How advertisers use AI across the campaign lifecycle"],":type":"snowflake-site/components/title-v2"},"text_how-advertisers-use-ai-across-the-campaign-lifecycle_0":{"id":"text-375ac6da61","text":"\u003Cp\u003EAdvertising performance depends on how well brands and agencies manage a campaign’s many moving parts. AI gives them a way to evaluate more of those variables together, adapt them while the campaign is active and identify problems before they consume additional spend.\u003C/p\u003E\n\u003Ch3\u003EAudience targeting and predictive modeling\u003C/h3\u003E\n\u003Cp\u003EAudience development begins with a campaign’s intended outcome: a first purchase, a renewal, a product upgrade or reduction in churn. Propensity models rank customers or prospects according to the likelihood of that outcome, while lookalike modeling extends a known audience — often recent converters or high-value customers — to people who share relevant characteristics. Estimates of customer lifetime value, churn risk and product affinity provide ways to prioritize spend and shape the message.\u003C/p\u003E\n\u003Cp\u003EThe most likely buyer isn’t always the most valuable person to target. When a campaign is intended to drive incremental sales, spend shouldn’t concentrate on customers who were already planning to purchase. Uplift and persuasion models estimate whose likelihood of acting changes after exposure.\u003C/p\u003E\n\u003Ch3\u003ECreative generation and dynamic creative optimization\u003C/h3\u003E\n\u003Cp\u003EAdvertising teams often need to produce the same idea in dozens of forms: different aspect ratios, products, audience segments, markets, languages and channels. Generative AI can reduce some of the production burden by drafting copy, developing visual concepts and adapting approved material for new formats.\u003C/p\u003E\n\u003Cp\u003EAt a larger scale, AI can also change how teams explore creative options. Advertisers can develop a wider range of initial directions and eliminate weak variants earlier. Dynamic creative optimization (DCO) carries variation into delivery. Headlines, images, offers and calls to action (CTAs) can be assembled according to the audience, placement or surrounding context, with campaign results informing which combinations continue to run.\u003C/p\u003E\n\u003Cp\u003EAs production expands, so does the need for control over the source material, however. Prices, product claims, disclosures and brand language should come from approved content, while regional or regulated campaigns require rules governing geography, eligibility and required wording. Generative AI can widen the creative field, but the final decision must rest with people who understand the brand, product and audience.\u003C/p\u003E\n\u003Cp\u003EAdvertisers should preserve records of the models, prompts, source assets and approvals used to create material campaign content. Where required — or where consumers could otherwise be misled — AI-generated or materially manipulated media should be clearly labeled. Provenance standards can supplement visible disclosures by helping platforms and reviewers verify how an asset was created or modified.\u003C/p\u003E\n\u003Ch3\u003EProgrammatic bidding and budget optimization\u003C/h3\u003E\n\u003Cp\u003EProgrammatic advertising automates decisions that have to be made within fractions of a second. AI can help improve the estimates behind those decisions, with the ability to analyze more signals.\u003C/p\u003E\n\u003Cp\u003EAcross the wider campaign, pacing and optimization models help advertisers avoid spending too quickly, leaving budget unused or continuing to fund a channel after its performance has deteriorated. These models also provide a basis for reallocating media among publishers, audiences and placements as new results become available.\u003C/p\u003E\n\u003Cp\u003EThe optimization target determines what those adjustments reward. Return on ad spend (ROAS) metrics connect media cost with attributed revenue, but even that result depends on complete conversion data and a defensible method for deciding which sales the campaign should receive credit for.\u003C/p\u003E\n\u003Cp\u003EFor a clearer view of contribution, advertisers often combine platform metrics with experiments, incrementality analysis and marketing mix modeling to distinguish campaigns that actually changed behavior.\u003C/p\u003E\n\u003Ch3\u003EPersonalization and message sequencing\u003C/h3\u003E\n\u003Cp\u003EPersonalization extends far beyond choosing a product recommendation. It can also determine which message best fits the customer’s current relationship with the brand, what messages the customer has already seen and whether another exposure is likely to change behavior.\u003C/p\u003E\n\u003Cp\u003EA new prospect will need introductory creative, while an existing customer will need onboarding, replenishment or information tied to a product already owned. After a purchase, suppression rules can remove acquisition messages before the same customer sees them repeatedly across other channels.\u003C/p\u003E\n\u003Cp\u003ECoordination is difficult when each system sees only one part of the interaction history. Email, ecommerce, mobile apps, store purchases and media exposure can each produce a different version of the customer, leading to duplicated messages, conflicting offers and poor frequency control. AI helps manage this complexity when the identity, customer state and channel permissions remain consistent across the workflow.\u003C/p\u003E\n\u003Ch3\u003EBrand safety and fraud detection\u003C/h3\u003E\n\u003Cp\u003ECampaign performance depends partly on what happens around the ad. An otherwise strong campaign can stumble if it appears beside unsuitable content or when impressions and clicks come from invalid traffic.\u003C/p\u003E\n\u003Cp\u003EFor brand safety, contextual models examine the subject, tone and surrounding material across webpages, images and video. This context provides a more nuanced assessment than keyword blocking alone, which can exclude appropriate journalism while missing risky content that contains no obvious prohibited term.\u003C/p\u003E\n\u003Cp\u003EFraud detection models can identify risky device behavior, implausible engagement, unusual traffic timing and discrepancies among systems that may indicate bots or other forms of invalid activity.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"card_v2_how-advertisers-use-ai-across-the-campaign-lifecycle_0":{"id":"card-v2-2224270126","additionalClasses":"seo-customer","configurationStatus":{"configured":true,"message":""},"text":{"id":"text","text":"\u003Cp\u003ESamsung Ads uses Snowflake and Snowpark to manage and analyze petabytes of advertising data across its global platform, helping brands connect with Samsung TV audiences through cross-screen advertising. With Snowflake as a unified platform and Snowpark supporting both Python and SQL, Samsung Ads can transform data in one place, improve governance and scalability, share data across regions and accelerate time to market for new products.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text"},"layoutStyle":"horizontal","title":{"id":"title","type":"heading4","lines":["Samsung Ads"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/customers/all-customers/video/samsung-ads/"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Watch the customer story"},"image":{"id":"image","lazyEnabled":true,"isLcpImage":false,"alt":"Samsung Ads logo","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--e68905bb-3029-435f-b07e-895f4dbc8779/samsung-ads%25403x.png?quality=85&preferwebp=true","height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card",":type":"snowflake-site/components/card-v2"},"title_ai-measurement-and-privacy-safe-data-collaboration":{"id":"title-v2-625a379e5f","additionalClasses":"anchor-title anchor-title--ai-measurement-and-privacy-safe-data-collaboration","type":"heading2","lines":["AI, measurement and privacy-safe data collaboration"],":type":"snowflake-site/components/title-v2"},"text_ai-measurement-and-privacy-safe-data-collaboration_0":{"id":"text-f283d71a41","text":"\u003Cp\u003EThe information needed to plan, activate and measure advertising is usually split across organizations. Publishers and platforms hold audience, impression and placement data, while advertisers hold customer identities, transactions and other business outcomes. Connecting those events requires a reliable view of identity, a shared definition of the outcome and a way for organizations to collaborate without exposing unrestricted customer data.\u003C/p\u003E\r\n\u003Cp\u003EAs third-party identifiers have weakened, that work has shifted toward first-party relationships, identity resolution and controlled data collaboration.\u003C/p\u003E\r\n\u003Ch3\u003EClosed-loop measurement and attribution\u003C/h3\u003E\r\n\u003Cp\u003EClosed-loop measurement connects campaign exposure with an outcome recorded in the advertiser’s own systems. Purchases, qualified leads, subscriptions and store visits provide a more direct view of business performance than clicks or platform-configured conversions alone.\u003C/p\u003E\r\n\u003Cp\u003EThis metric can’t prove the campaign caused the result, however. Targeting may favor customers with stronger purchase intent to begin with, making it difficult to tell how much of the observed conversion rate came from the advertising itself. Under these conditions, traditional attribution can give a campaign credit for a sale that would have happened regardless.\u003C/p\u003E\r\n\u003Cp\u003EIncrementality analysis focuses on the difference created by the advertising. Holdout tests, randomized experiments and causal-inference methods compare exposed and unexposed groups, helping advertisers estimate lift rather than simply assign credit.\u003C/p\u003E\r\n\u003Ch3\u003EIdentity resolution across fragmented signals\u003C/h3\u003E\r\n\u003Cp\u003EThe same customer can appear under a variety of identifiers, including an email address, account ID, browser identifier, connected-device ID and point-of-sale record. Unless those records are connected, reach is overstated, frequency limits break and conversion measurements are inaccurate.\u003C/p\u003E\r\n\u003Cp\u003EDeterministic identity resolution uses stable identifiers such as authenticated accounts or hashed email addresses. Probabilistic approaches estimate relationships from less direct signals, including devices, locations and behavior. Because probabilistic matches carry uncertainty, confidence thresholds should reflect the use case.\u003C/p\u003E\r\n\u003Ch3\u003EData clean rooms for advertising collaboration\u003C/h3\u003E\r\n\u003Cp\u003ECampaigns involve a variety of parties. Advertisers bring customer and transaction data, publishers contribute exposure and authenticated-audience data, and identity or measurement partners supply additional linking or analytical capabilities.\u003C/p\u003E\r\n\u003Cp\u003EA \u003Ca href=\"https://www.snowflake.com/en/fundamentals/what-is-a-data-clean-room/\"\u003Edata clean room\u003C/a\u003E gives those participants a controlled environment for comparing data without granting unrestricted access to the underlying records. Audience overlap, reach analysis, activation and campaign measurement can all occur under approved policies.\u003C/p\u003E\r\n\u003Cp\u003EThe clean room governs the collaboration, but consent, contracts and applicable privacy requirements remain the responsibility of the participating organizations.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003ELearn how Snowflake Data Clean Rooms support audience overlap, lookalike modeling and attribution analysis across the marketing lifecycle:\u003C/i\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"yt_ai-measurement-and-privacy-safe-data-collaboration_0":{"id":"embed-b12b98ee9b","youtubeVideoId":"eRRvcyaSfFs","layout":"responsive","youtubeAspectRatio":"56.25","youtubeAutoPlay":false,"youtubeLoop":false,"youtubeMute":false,"youtubePlaysInline":false,"youtubeRel":false,"embeddableResourceType":"core/wcm/components/embed/v1/embed/embeddable/youtube","type":"EMBEDDABLE",":type":"snowflake-site/components/youtube"},"text":{"id":"text-658f332578","text":"\u003Ch3\u003ERetail media measurement\u003C/h3\u003E\r\n\u003Cp\u003ERetail media networks combine advertising inventory with detailed purchase data, creating a direct connection between campaign exposure and sales at the SKU, category or customer-segment level.\u003C/p\u003E\r\n\u003Cp\u003EThis visibility gives brands a more precise way to evaluate advertising close to the point of purchase, while the retailer can return approved analysis without handing over unrestricted customer records.\u003C/p\u003E\r\n\u003Cp\u003EThe result still depends on shared rules. Advertisers and retailers need to agree on attribution windows, eligible transactions, returns and whether the analysis reflects attributed revenue or incremental sales.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_the-data-foundation-behind-ai-advertising":{"id":"title-v2-dcc5938cfb","additionalClasses":"anchor-title anchor-title--the-data-foundation-behind-ai-advertising","type":"heading2","lines":["The data foundation behind AI advertising"],":type":"snowflake-site/components/title-v2"},"text_the-data-foundation-behind-ai-advertising_0":{"id":"text-df75c2e40f","text":"\u003Cp\u003EA shared data foundation gives advertisers a common view of customers, campaigns, creative, media and outcomes before that information is reused across models, applications and agents. Definitions, permissions and lineage can remain attached to the data rather than being recreated inside every tool.\u003C/p\u003E\n\u003Cp\u003EBuchheim \u003Ca href=\"https://www.snowflake.com/en/blog/media-entertainment-adtech-martech-leadership/\"\u003Eemphasizes the importance of the data foundation\u003C/a\u003E: “The intelligent use of data is not a nice-to-have. It’s the only way to achieve operational efficiency, better understand customer journeys, enable more personalized ads and media experiences, and drive desired business outcomes.”\u003C/p\u003E\n\u003Ch3\u003EUnified first-party data\u003C/h3\u003E\n\u003Cp\u003EFirst-party advertising data includes customer profiles, transactions, loyalty activity, website and app events, subscriptions, customer-service interactions and campaign history. Media data adds impressions, reach, frequency, cost, placement and creative.\u003C/p\u003E\n\u003Cp\u003EA \u003Ca href=\"https://medium.com/snowflake/modern-marketing-starts-with-a-customer-360-on-snowflake-a54829fc9b9e\" target=\"_blank\"\u003Ecustomer 360\u003C/a\u003E connects the relevant parts of that history to a governed identity. Context includes consent, channel eligibility, lifecycle stage, recent exposure and previous offers.\u003C/p\u003E\n\u003Cp\u003ECampaign records require the same consistency. Common IDs and definitions should connect planning, media buying, serving, measurement and finance so that spend, creative and results can be reconciled without rebuilding the campaign from several reports.\u003C/p\u003E\n\u003Cp\u003EFreshness requirements should match the use case. Programmatic bidding operates within very short time windows, audience refreshes often run daily, for example, while marketing mix models usually rely on weekly or monthly data.\u003C/p\u003E\n\u003Ch3\u003EShared advertising and measurement definitions\u003C/h3\u003E\n\u003Cp\u003ECommon advertising metrics often hide incompatible definitions. Reach might refer to people, households, devices or platform identities. A conversion might mean a purchase, lead, store visit or any event configured in the platform. ROAS might rely on gross revenue, net revenue or margin, with different rules for returns and discounts.\u003C/p\u003E\n\u003Cp\u003EAI systems need the approved meaning of a label, not just the name. For example, a conversational interface comparing campaigns should know which revenue measure applies and which attribution logic produced it. Semantic definitions preserve these rules alongside the underlying data. Once campaign, audience and outcome metrics carry consistent meaning, they can be reused across dashboards, models and agents without rebuilding the logic each time.\u003C/p\u003E\n\u003Ch3\u003EGovernance across the advertising lifecycle\u003C/h3\u003E\n\u003Cp\u003EAdvertising data includes personal information, proprietary campaign plans, publisher pricing, product-launch details and confidential performance results. \u003Ca href=\"https://www.snowflake.com/en/data-governance/data-security/rbac/\"\u003ERole-based access controls\u003C/a\u003E can help scope appropriate access to this sensitive data.\u003C/p\u003E\n\u003Cp\u003ELineage gives advertisers a traceable record of how an audience, model input or performance result was produced. It shows which source data was used, which rules and transformations were applied and which model or attribution method generated the final output.\u003C/p\u003E\n\u003Cp\u003EAs AI systems take on more work, governance also has to cover their actions. Generative models need approved source material and controls over the claims they produce. Predictive models require evaluation as audiences and market conditions change. Agents need explicit permissions and approval thresholds before they alter budgets, activate audiences or publish creative.\u003C/p\u003E\n\u003Cp\u003EModels and agents should also be monitored after deployment for changes in input data, performance, cost and behavior. Version histories, decision logs, test results and rollback procedures give teams a way to investigate an unexpected budget shift, audience change or creative output and restore a previously approved configuration.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_the-data-foundation-behind-ai-advertising_0":{"id":"text-ba5b8d3a6b","additionalClasses":"callout callout--warning","text":"\u003Cp\u003E\u003Cstrong\u003EQUICK TIP\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EMatch an AI agent’s permissions to the risk of the action. For example, the agent may analyze results automatically, while major budget changes, audience activation and creative publication require approval.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"title_responsible-ai-in-advertising":{"id":"title-v2-f080ac3c38","additionalClasses":"anchor-title anchor-title--responsible-ai-in-advertising","type":"heading2","lines":["Responsible AI in advertising"],":type":"snowflake-site/components/title-v2"},"text_responsible-ai-in-advertising_0":{"id":"text-3e3b9234df","text":"\u003Cp\u003EResponsible AI centers on three areas for advertising use cases: how customer data is collected and applied, how much authority automated systems receive and how generated or selected creative reaches the market. Weak controls in any area can expose the organization to privacy violations, wasted spend or public brand damage.\u003C/p\u003E\n\u003Ch3\u003EPrivacy, consent and appropriate use\u003C/h3\u003E\n\u003Cp\u003EFirst-party data should be collected and used according to applicable privacy requirements, contractual restrictions and the organization’s commitments to customers. Consent needs enough specificity to determine which advertising and personalization activities are permitted.\u003C/p\u003E\n\u003Cp\u003EWhen records are connected across systems or organizations, the resulting profile can be more sensitive than any individual source. Identity resolution, customer 360 and data clean room programs should preserve purpose limitations rather than treating technical access as permission.\u003C/p\u003E\n\u003Ch3\u003EHuman oversight\u003C/h3\u003E\n\u003Cp\u003ERoutine changes can operate within defined limits, while decisions with larger financial, legal or reputational consequences should require human review. A bidding system, for example, can adjust spend within an approved range while routing a major reallocation to the media team.\u003C/p\u003E\n\u003Cp\u003EThe same principle applies to AI agents. Reading campaign results, drafting a recommendation, changing an audience and launching a campaign represent different levels of risk. Tool permissions and approval thresholds should reflect those differences.\u003C/p\u003E\n\u003Ch3\u003ECreative quality and brand safety\u003C/h3\u003E\n\u003Cp\u003EProducing more variants doesn’t guarantee better advertising. Generative AI output can be generic, visually inconsistent or inaccurate, even when it appears polished.\u003C/p\u003E\n\u003Cp\u003EApproved source assets, creative rules and evaluation criteria provide a basis for review. Before publication, teams should assess factual accuracy, brand consistency, accessibility, required disclosures and the context in which the ad will appear.\u003C/p\u003E\n\u003Cp\u003EReview continues after launch. Audience response, placement quality and uneven performance can expose problems that weren’t visible during production, and those findings should inform both the campaign and later model evaluation.\u003C/p\u003E\n\u003Ch3\u003EBias review and mitigation\u003C/h3\u003E\n\u003Cp\u003EModel evaluation should also address \u003Ca href=\"https://www.snowflake.com/en/data-governance/policy/data-ethics/data-bias/\"\u003Ebias\u003C/a\u003E — who’s included, excluded or disproportionately exposed. Advertisers may need to test delivery, error rates, offers and outcomes across relevant groups, particularly in regulated or high-impact categories.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_how-snowflake-supports-ai-in-advertising":{"id":"title-v2-8750bf1e1e","additionalClasses":"anchor-title anchor-title--how-snowflake-supports-ai-in-advertising","type":"heading2","lines":["How Snowflake supports AI in advertising"],":type":"snowflake-site/components/title-v2"},"text_how-snowflake-supports-ai-in-advertising_0":{"id":"text-f222dc32be","text":"\u003Cp\u003EAdvertising workflows draw on customer, campaign, creative, impression and outcome data held across several systems and organizations. Within \u003Ca href=\"https://www.snowflake.com/en/solutions/industries/advertising-media-entertainment/\"\u003ESnowflake’s AI Data Cloud for Advertising, Media and Entertainment\u003C/a\u003E, those data types can be governed and analyzed together while preserving separation among advertisers, agencies, publishers, adtech providers and measurement partners.\u003C/p\u003E\n\u003Cp\u003EFor predictive targeting, bidding and measurement, Snowflake supports model development and inference near the data used by the application. \u003Ca href=\"https://www.snowflake.com/en/product/features/cortex/\"\u003ECortex AI\u003C/a\u003E supports tasks such as extracting information from briefs, classifying media content and analyzing creative assets alongside campaign and customer data. Cortex Search retrieves relevant material from approved sources, while Cortex Analyst provides natural-language access to governed metrics through semantic models.\u003C/p\u003E\n\u003Cp\u003EWhere the workflow crosses company boundaries, \u003Ca href=\"https://www.snowflake.com/en/product/features/data-clean-rooms/\"\u003ESnowflake Data Clean Rooms\u003C/a\u003E support audience overlap, activation and measurement through approved analysis templates. Participants collaborate on \u003Ca href=\"https://www.snowflake.com/en/data-governance/\"\u003Egoverned data\u003C/a\u003E while controls limit access to the underlying records.\u003C/p\u003E\n\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/product/features/horizon/\"\u003ESnowflake Horizon Catalog\u003C/a\u003E provides governance, discovery and lineage across analytics, \u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/machine-learning/\"\u003Emachine learning\u003C/a\u003E and AI applications. Policies remain attached to the data as different teams and systems use it, helping organizations apply a more consistent control model across the campaign lifecycle.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_where-ai-in-advertising-will-go-next":{"id":"title-v2-f804c041db","additionalClasses":"anchor-title anchor-title--where-ai-in-advertising-will-go-next","type":"heading2","lines":["Where AI in advertising will go next"],":type":"snowflake-site/components/title-v2"},"text_where-ai-in-advertising-will-go-next_0":{"id":"text-e67f214159","text":"\u003Cp\u003EAI will keep taking on more of the work involved in planning, running and measuring campaigns. The real test is whether that leads to better advertising: clearer decisions, stronger creative, better targeting and a more accurate view of what actually worked. Strong outcomes depend on how well systems stay connected to the campaign’s objective, the permissions and policies governing their actions and the signals and context available.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"callout_where-ai-in-advertising-will-go-next_0":{"id":"text-95adfb7057","additionalClasses":"callout callout--general","text":"\u003Cp\u003E\u003Cstrong\u003EKEY TAKEAWAY\u003C/strong\u003E\u003C/p\u003E\n\u003Cp\u003EBetter advertising AI requires three things working together: reliable data, clearly defined business outcomes and controls over how models and agents act.\u003C/p\u003E","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-regular text-color-text-05"},"text_where-ai-in-advertising-will-go-next_1":{"id":"text-b72f264c31","text":"\u003Cp\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/\"\u003ESee how AI is being used in other industries >\u003C/a\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EExplore the AI in Industries Hub:\u003C/i\u003E\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-cybersecurity/\"\u003EAI in Cybersecurity\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-finance/\"\u003EAI in Financial Services\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-healthcare/\"\u003EAI in Healthcare and Life Sciences\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-manufacturing/\"\u003EAI in Manufacturing\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/artificial-intelligence/industries/ai-in-retail/\"\u003EAI in Retail\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text__0","title_how-advertisers-use-ai-across-the-campaign-lifecycle","text_how-advertisers-use-ai-across-the-campaign-lifecycle_0","card_v2_how-advertisers-use-ai-across-the-campaign-lifecycle_0","title_ai-measurement-and-privacy-safe-data-collaboration","text_ai-measurement-and-privacy-safe-data-collaboration_0","yt_ai-measurement-and-privacy-safe-data-collaboration_0","text","title_the-data-foundation-behind-ai-advertising","text_the-data-foundation-behind-ai-advertising_0","callout_the-data-foundation-behind-ai-advertising_0","title_responsible-ai-in-advertising","text_responsible-ai-in-advertising_0","title_how-snowflake-supports-ai-in-advertising","text_how-snowflake-supports-ai-in-advertising_0","title_where-ai-in-advertising-will-go-next","text_where-ai-in-advertising-will-go-next_0","callout_where-ai-in-advertising-will-go-next_0","text_where-ai-in-advertising-will-go-next_1"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-medium"},"flexible_column_content_container_2":{"additionalClasses":"hub-sidebar","layout":"SIMPLE","id":"hub-body-aside",":items":{"container":{"additionalClasses":"sticky-sidebar","columnClassNames":{"text_943981956_copy_":"aem-GridColumn 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