Entertainment and Media Guide to AI

AI in entertainment & media part 2 icon - headphones icon

Read time: 8 minutes

The use of AI is reshaping the advertising industry landscape, and its impact on media planning and media buying is just beginning to be realized. Brands are turning to AI (directly and through their agencies) to offset the myriad of issues that they have faced in recent years, including strained budgets and fragmented audiences, which has led to the need for media planning and buying to be done efficiently, within budget and across multiple audience segments. This is where AI comes in and where it is touted by the advertising industry as making it easier for brands to make data-driven decisions, optimize campaigns and improve ROI.

In terms of media planning – the process by which advertisers decide where, when and how often an ad should run to maximize engagement and ROI – some of the key AI use cases include its ability to create bespoke media plans within minutes and to work more efficiently by automating data analysis, targeting personalization and campaign optimization. For example, during development, one media agency conducted “human vs. machine” parallel tests, in which the agency’s AI tool and an experienced team of media planners were tasked with creating a media plan that would optimize reach over three overlapping audiences. The planners were unable to achieve the task, as each time they improved reach for one audience, they lost reach for the other two. The AI media planner, however, was apparently able to solve the problem in 90 seconds.

AI can not only identify patterns and insights that are otherwise invisible to human perception but also reduce the time, effort and agency resources needed to optimize marketing campaigns to their fullest potential. By relegating such labor-intensive tasks to AI-powered tools, advertising agencies can focus on the art of media planning – the creative thinking, intuition and strategy needed to create impactful campaigns and media placements, which AI is unable to replicate.

AI may also become a game changer in the world of programmatic buying. By using AI to analyze audience data and adjust bids in real time to reach the most valuable audiences at the most effective times, advertisers can gain more efficient, targeted and cost-effective ad placements and improved campaign performance.

The largest global agency holding companies and other media agencies have already begun unveiling their own AI platforms for media planning and media buying, and many recognized the potential of AI and how it would revolutionize the agency business model years ago. Back in 2020, Group M global chief executive Christian Juhl predicted that in five years, Group M would look “more like a software company than it will a media agency. It will automate, it will have really hard technology connections with the major media providers in the world... We will have more people doing programmatic and AI and algorithmic optimizations than we will have sending IOs [traditional insertion orders for ads].”

We are starting to see evidence of global agency holding companies investing significantly in this space, either through research and development or corporate acquisitions in order to stay ahead of the curve, including WPP’s acquisition of Satalia, an AI technology company; Publicis’s launch of its AI platform, Marcel; and Dentsu’s launch of its M1 AI platform.

Key takeaways
  • Transparency, bias and confidentiality issues can abound due to the “black box” nature of AI tools, which requires brands to ensure they exert a sufficient level of oversight and control over the data inputs and instruction parameters used for such tools
  • Brands must clearly define the ownership rights of automated media plans, the brand’s usage rights to data that is fed into an AI tool, and ensure they are limiting the agency’s ability to use the same AI tool for competing brands within the same holding company
  • Brands should not overly rely on AI tools to address brand safety and invalid traffic concerns, but should instead use a combination of automated tools and the brand’s own tags, data audits of DSPs and ad server log level information to verify campaign performance