How AI (and GenAI) is changing the Advertising Industry 

Artificial Intelligence (AI) has had a significant impact on the advertising industry, with most brands and agencies optimistic and excited about its potential.

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However, AI has also been an abused concept within advertising for many years. Although the origins of usage of artificial intelligence for media and advertisers can be traced back several decades, the field has seen significant progress particularly in recent times thanks to the widespread adoption of a specific type of AI, which is Generative AI (or GenAI or GAI), capable of creating texts, images, videos, or other data using generative models, often in response to human prompts. As a result, the explosion of Gen AI has lead to a stunning growth of overall AI technologies in our industry.

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‍According to Statista, the global market revenue of AI in marketing is expected to grow from $27.4 billion in 2023 to $107.4 billion in 2028 (36 billion U.S. dollars in 2024, +31% YOY). 

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In less than one year, our knowledge and direct usage of artificial intelligence has expanded significantly, with tools such as ChatGPT, DALL-E, and Midjourney becoming common in everyday use for marketing and advertisers professionals. These applications are proper examples of generative AI, which is revolutionizing the way marketers approach content creation, personalization, and optimization. By harnessing the power of GenAI algorithms, marketers can create more engaging and effective advertising campaigns that resonate with their target audiences and drive tangible results. 

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However, AI capabilities extend far beyond content creation, and AI is currently involved in several phases of advertising operations, even if few companies have reached complete end-to-end AI enablement. 

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The Real AI Opportunity lies in Process Improvements and Outcomes

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According to a recent study by MNTN and adexchanger, creative concepting and brainstorming is currently the second most common use case for AI usage (60% of respondents) after programmatic media buying and optimization (selected by 65% of respondents among brands and agencies leaders). In the next 12-18 months, however, brands and agencies will completely redefine the impact of AI for their advertising operations, with campaign budgeting and management becoming the expected preferred use case for AI implementation for 73% of respondents (+170% growth vs. today).

Our industry is then expected to experience a major shift in AI adoption for advertising operations, with budgeting and data measurement becoming a major  focus for brands and agencies in the upcoming months. As also confirmed by WebFX, 52.5% of marketers already flag Data Analysis as the top AI Marketing use case (vs. 44.5% for Content Creation).

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Many media and advertising professionals still view AI as an independent tool or feature, rather than an integral enhancement to their workflows. There is a prevalent notion that an AI revolution will suddenly transform everything, as if flipping a switch to a new era. However, the most effective use of AI is gradual, evolving naturally within processes or features that require expedited human input or the processing of vast quantities of data for optimal outcomes.

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As showed by MNTN and adexchanger, in fact, the leading opportunities for AI in advertising programs are connected to operations improvements and results rather than creativity:

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• According to 92% of brands and agencies, AI’s biggest opportunity lies in improving the efficiency of existing processes;
• According to 82% of brands and agencies, the second AI's biggest opportunity is connected to increasing productivity and outputs.

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The Role of Automation

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Efficiency and effectiveness in advertising are no easy tasks to achieve, even with the help of AI. To succeed, advertisers need to also introduce and master automation in their daily operations, balancing appropriate human oversight and an integrated approach for strategic control, compliance, and scalability. 

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On the one hand, automation is able to free up time for strategic initiatives handling repetitive and predictable tasks efficiently; on the other hand, AI complements automation by accelerating cognitive tasks and empowering human thinking for informed decisions. 

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As digital strategies become increasingly complex across multiple channels, automation is crucial for speeding up campaign execution and removing mundane tasks. A robust automation platform efficiently manages campaign rollouts, budget distribution, and ad operations across various channels, freeing strategists to refine their approaches. Moving to effectiveness, automation and AI together allow for the large-scale analysis of data, leading to a deeper understanding of performance metrics and related optimizations.

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The connection between AI and automation is also a must-have to avoid redundancy and fragmentation, adding complexity instead of operational improvements. As of today, theresanaiforthat.com, one of the leading AI tools aggregators in the world, lists over 12,253 AIs available for 15,350 tasks and 4,846 jobs. The risk of adopting too many AI tools with poor connection and automation is high.

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Beyond GenAI: Predictive and Prescriptive Models

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As shown by previous polls, the use of generative AI for scripts, images, music, videos or voice overs will soon become widely established and adopted. Other AI models like predictive AI and prescriptive AI will then gain momentum.

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Predictive AI refers to the use of artificial intelligence to make predictions about future events or behaviors based on historical data. In the context of advertising, predictive AI can be used to analyze customer data and predict their future behavior, such as which products they are likely to buy, what kind of content they are likely to engage with, and how they are likely to respond to different marketing messages. For example, predictive AI can help advertisers identify the most valuable audience segments to target, optimize their ad spend, and measure the effectiveness of their campaigns.

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Prescriptive AI goes a step further by suggesting specific actions or strategies based on the predictions made. It uses a wide range of data, including structured and unstructured data, to create actionable recommendations that can help organizations make informed decisions. An example of prescriptive AI are the suggestions provided by MINT ARM for optimal budget allocation. Our suggestions engine provides you with budget improvement suggestions at all levels of your campaign, while also reminding you about the original budget brief.

As AI continues to evolve and integrate into the technologies we utilize, it becomes increasingly challenging to differentiate among various additional AI types (generative AI, predictive AI, prescriptive AI and more). AI technologies have quickly evolved into potent tools for boosting efficiency and effectiveness in advertising: the main challenge for all marketers will be selecting the right tools, avoiding redundancy across platforms, and improving existing workflows through tailored adoptions.

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