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<\/div>\nAI<\/span><\/a> is bringing a new coordination lay\u00ader to ad tech<\/a> that is impacting every area of the streaming mone\u00adtization<\/a> workflow, including executing transactions, preparing inventory, understanding audience, and improving campaigns and measurement. Media planning is moving from static spreadsheets to living systems. The best teams are using AI to ask better questions, such as \u201cWhat if supply shifts mid\u00adflight?\u201d or \u201cWhat if this audience over-indexes on live?\u201d<\/span><\/p>\n AI makes it possible<\/a> to model delivery across live, VOD, and FAST together and to update forecasts as signals come in, instead of planning channel by channel.<\/span><\/p>\n How many people on the publisher side are currently using AI in ad tech? \u201cI would say 35%, or maybe 40%,\u201d according to Luciano Marcos Escudero, VP of media engineering at Globant.<\/a><\/span> Even so, it sounds like there are some very prom\u00adising use cases that Globant<\/a> is working on for customers.<\/span><\/p>\n Some publishers are still testing the AI waters or just beginning to wade in. FreeWheel has encountered several publishers that are still evaluating things, while others are further along their adoption curve. Jeff Ellin, VP of product architecture at FreeWheel<\/a>, says, \u201cI think buyers are doing their homework earlier, and they already have an LLM of choice that they\u2019re ready to use so they can connect to it and put their data through it a lot faster. I met with 18 European broadcasters,\u201d he reports, \u201cand there really were a number of them saying, \u2018I am very interested in this. Can we start tomorrow?\u2019 \u201d<\/span><\/p>\n Other publishers are expressing skepticism or doubt, Ellin says, and few, if any, are fully informed about the technology or the opportunities. \u201cSome know a lot about it; some know nothing about it. This year, it\u2019s more of a learning world. But at some point, [AI is] going to make the buying and the selling happen.\u201d<\/span><\/p>\n Shailley Singh, COO and EVP of product at IAB Tech Lab<\/a>, says the real initial gains are to be found on the efficiency side: \u201cAI for the media ecosystem is creating this ability for advertising companies from brands to agencies to ad tech companies and publishers to create this super efficiency in how they value impressions, how they negotiate, and how they work and manage campaigns.\u201d<\/span><\/p>\n The industry is moving from basic machine learning into agentic workflows. From a standards perspective, IAB Tech Lab<\/a> sees AI being integrated into three primary areas:<\/span><\/p>\n \u201cIAB recently unified all of our AI standards under AAMP [Agentic Advertising Management Protocols]<\/a>,\u201d Singh says. \u201cInstead of ripping out the plumbing the industry relies on, we are extending it. We are integrating modern AI protocols like Model Context Protocol [MCP] and gRPC into existing standards like OpenRTB, AdCOM, and OpenDirect.\u201d<\/span><\/p>\n (This wouldn\u2019t be ad tech without more acronyms, but we\u2019ll unpack some of these later in the article.)<\/span><\/p>\n \u201cPredictive AI has been around for a very long time\u2014especially in the ad tech world\u2014around forecasting and using machine learning to predict what is going to happen in the future to help pick the right advertising at the right time,\u201d contends FreeWheel\u2019s Ellin. This may be so, but a lot has happened recently, and now, \u201cAI in ad tech\u201d has a number of new meanings.<\/span><\/p>\n \u201cTwo years ago, if you tried to talk to any media companies, they were a little bit resistant to introducing AI as part of the workflows. Now that is a much easier conversation,\u201d says Globant\u2019s Escudero. \u201cThere is not a workflow that we have identified so far that\u2019s completely automated. Most of the workflows that we\u2019re using with our customers are fully speeding up the process, but they\u2019re still having humans in the loop.\u201d<\/span><\/p>\n Research from IBM Institute for Business Value<\/a> estimates that spending for agentic AI will triple in the next year, and 46% of those interviewed in Microsoft\u2019s 2025 Work Trend Index Annual Report<\/a> have said they are already using agents to fully automate workflows or processes.<\/span><\/p>\n Ellin expects to see companies on the ad tech side \u201cunderstand and manage their inventory by using an LLM to find and package inventory to potential buyers who are looking for very specific audiences or types of inventory. Lately, we\u2019ve been providing this new infrastructure AI layer for our clients to build on top of, connecting us with their own AI.\u201d<\/span><\/p>\n Because it leverages the MCP, Ellin explains, \u201cthis AI infrastructure allows people to use natural language to interact with a third-party system\u2014in this case, our streaming hub, our buyer cloud platforms, and all the APIs that we\u2019ve historically exposed.\u201d<\/span><\/p>\n \u201cMost of the companies that are implementing AI have a much better understanding of their content, which allows them to do much better planning in terms of the ad insertion,\u201d says Escudero. Earlier ad workflows required a planner to differentiate assets like episodes, movies, and so forth and to reach out to talk about their inventory to agencies. \u201cThe process could take weeks just to identify the proper assets, targeting the different potential agencies, and then come back with a proper plan,\u201d Escudero explains. \u201cWith AI in place, there is a lot of advancement in terms of understanding the content before getting to inventory creation.\u201d<\/span><\/p>\n All new assets (and also old content) now come with the potential for viewing the metadata as part of the media ingestion workflow. \u201cThere\u2019s an extraction of metadata and understanding of the videos at the scene level,\u201d Escudero says. This includes what type of artifacts are in the scene and what type of activities are happening. This sort of context, in principle, provides value to advertisers.<\/span><\/p>\n Last year, Bitmovin launched a product called AI Scene Analysis<\/a> that provides contextual me\u00adtadata enrichment using multimodal AI models to extract metadata from content. These models can map content to IAB taxonomies, according to Jacob Arends, senior product manager for Bitmovin\u2019s AI Scene Analysis and Playback<\/a>.<\/span><\/p>\n \u201cIf it\u2019s a car chase scene, your content taxonomies might be car, road, and traffic, but your ad opportunities might be automotive and tra\u00advel, which can then be passed to the ad server, and that can then influence the decision of which ad is then served to the customer,\u201d Arends says. To accomplish this, Bitmovin uses an array of foundational models.<\/span><\/p>\n \u201cThe SCTE 35 markers send information to the ad server. It can be content ID for a show, or it could be about the scene or a list of taxonomy terms,\u201d says Olivier Cortambert<\/a>, head of solutions architecture for video streaming\/ad insertion at Yospace<\/a>. With previous workflows, he notes, \u201cthe ad server was just aware of what the content was.\u201d<\/span><\/p>\nThe Current State of AI Adoption<\/strong><\/h2>\n
<\/span><\/p>\nImproving Efficiency<\/strong><\/h2>\n
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IAB Tech Lab\u2019s AAMP Framework (Source: IAB Tech Lab)<\/em><\/p>\nAutomation and Agentic AI Workflows<\/strong><\/h2>\n
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A Model Context Protocol (MCP) schematic (Source: modelcontextprotocol.io)<\/em><\/p>\nNuts and Bolts of Metadata<\/strong><\/h2>\n
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Bitmovin\u2019s AI Scene Analysis pipeline (Source: Bitmovin)<\/em><\/span><\/p>\n
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