Is AI Revolutionizing Mobile Ads or Amplifying ML's Power?

Mobile advertising has long been powered by machine learning (ML), with algorithms fine-tuning ad targeting, bid strategies, and personalization for years. But when AI burst onto the scene in November 2022 with the release of ChatGPT, it took center stage. Soon, AI began leveling up technology across industries by accelerating automation, personalization, and content generation far beyond previous capabilities. For instance, Walmart now uses AI in Route Optimization technology to optimize its supply chain, and Coca-Cola uses AI systems in vending machines to help the company understand changing consumer preferences and predict product trends. Tech giants like Google and Meta enhance their businesses with breakthrough technology like Gemini (formerly Bard) to summarize search results and Meta Advantage to automatically generate ad creatives.

These advances show how AI is improving product usability, streamlining workflows, and automating tasks at a level not previously possible. But does this AI boom actually shake up an ML-heavy industry like mobile advertising, or is it just the latest buzzword? While the industry already uses advanced ML technologies like neural networks, AI promises even more sophisticated tools. The question now becomes: how will AI bring new capabilities that build on the strong ML foundation, and can it really make mobile advertising smarter, more automated, and more effective?

Understanding the Differences Between ML and AI

To understand whether AI is just hype or genuinely impactful, it’s key to first distinguish between ML and AI. Machine learning, a subset of AI, focuses on using data to train algorithms that can make predictions. In mobile advertising, ML has been crucial for analyzing huge data sets to optimize ad placements, detect fraud, and deliver personalized experiences. It’s the engine behind the predictive models that drive performance across the industry.

AI, on the other hand, is a much broader concept that encompasses not only ML but other technologies that simulate human intelligence and decision-making. For example, AI doesn’t just make predictions—it can understand natural language, generate new content, and adapt with greater autonomy. While ML stays within the data it’s trained on, AI is designed to create more flexible and sophisticated solutions that push the boundaries of what was previously possible.

What AI Brings to Mobile Advertising

AI is often marketed as the next big thing in mobile advertising, with promises of smarter ad targeting, better bid optimization, and enhanced fraud detection. But these are areas where ML already excels—especially with neural networks optimizing campaign performance. So, what does AI bring to the table that’s truly new?

Some of the biggest advancements are in Natural Language Processing (NLP), Large Language Models (LLMs), and generative AI. ChatGPT famously uses these technologies to generate human-like text based on the input it receives. When it comes to mobile marketing, these advancements allow AI to better understand and create ad copy, making ads more relevant and impactful. Besides predicting which ad copy will work best, these technologies can also write custom messaging for different audience segments on the fly.

Building on that, another area where AI has the potential to revolutionize mobile advertising is in creative generation and optimization. While ML requires manual input to set up tests or design new creatives, AI can autonomously generate new creatives, select the winning design, and show the ad to target users—all in real time. Additionally, computer vision technology enables AI to analyze visual elements within ads, enhancing creative exploration and testing while ensuring that the imagery resonates with target audiences. Although much of this technology is still evolving, it has the potential to transform how ads are created and deployed in the near future.

AI can also improve predictive analysis in privacy-centric environments, most notably by aggregating large pools of data and then generating new, simulated data. By simulating data such as probabilistic user profiles, inferred interests, predicted behavior patterns, and modeled conversion paths, AI simulated data can help to fill in the gaps left by SKAN, AAK, or Privacy Sandbox. As a result, this simulated data helps ML models optimize campaigns and remain effective without fingerprinting or collecting prohibited user data. Simulated data can also help predict trends and generate more insights for advertisers. Appsflyer already has a similar example of this using ML. Combined with Bayesian statistics, first-party data, and SKAN data, Appsflyer models data points and gives advertisers a better understanding of their SKAN campaigns’ performance.

AI’s Impact on Internal Operations

Beyond its role in ad delivery, AI can also enhance internal operations within mobile advertising companies. It can make processes more efficient and reliable, speeding up development cycles and platform maintenance. For example, AI can assist engineers by detecting bugs, optimizing code, and streamlining platform updates—meaning faster rollouts and improvements.

Another common example is AI-powered chatbots, like ChatGPT, which can handle both customer and internal support inquiries 24/7. By automating routine interactions and freeing up human resources for more complex tasks, mobile advertising companies can focus on higher priority tasks more quickly and free up bandwidth for their teams.

Additionally, AI can monitor the health of servers, databases, and other critical infrastructure components, predicting potential failures or bottlenecks before they occur. This proactive approach reduces downtime and ensures that ad delivery systems run smoothly, which is crucial for maintaining campaign performance.

Does AI Matter In an Industry Like Mobile Advertising?

The answer is yes—but with some caveats. AI is not a replacement for ML but rather a complement that introduces new capabilities. While ML has been meticulously refined in mobile advertising over the past 15 years and is cost-effective to maintain, AI, with its complex nature, demands far more resources to build and sustain. It also requires significantly larger data pipelines to operate efficiently. Not all mobile advertising companies may have access to these extensive data sets, potentially leading to suboptimal campaign performance and longer time-to-goal.

Moreover, much of the AI tech being discussed is still aspirational. While capabilities like real-time ad creation or simulated data hold promise, we’re not yet able to fully measure their impact on programmatic advertising without further testing.

As AI continues to evolve, its role in mobile advertising will likely grow, introducing more sophisticated ways to create, target, and deliver ads. However, it’s essential to recognize that AI is a tool designed to enhance, not replace, the powerful and foundational ML systems already in place. At Bidease, for example, LLMs translate app store data into English and then generate vectors that help our ML models make better predictions. By strategically integrating AI where it can make a real difference, mobile advertisers and their partners can stay ahead of the curve while continuing to leverage the proven strengths of ML.