AI in mobile advertising: Trends, tools, and compliance

Mobile advertising is now the backbone of the digital advertising industry. Consumers increasingly spend most of their time on smartphones, and advertisers have inevitably followed suit.

In fact, worldwide spending on mobile advertising has reached staggering proportions – $402 billion in 2024, set to rise further to $447 billion in 2025, constituting well over 56% of the total advertising spend in the digital space. The Asia Pacific market leads with a massive $231 billion in 2025, while North America trails with $164 billion in the same year.

Meanwhile, consumer expectations continue to soar, with 71% now calling for personalized experiences, while 76% feel frustrated if that’s not what they get.

Such high volumes in the mobile advertising space, combined with rising consumer expectations, bring the spotlight to one major industry tech trend: artificial intelligence.

Current uses of AI in mobile ads

Machine learning (ML) solutions already play a role in nearly all aspects of the mobile advertising chain. Let’s take a look at some of the most common applications.

AI in mobile ad targeting

In the realm of targeting, user behaviour patterns related to app usage, website visits, geographic information, demographics, and first-party information are used to predict the individuals marketers will be most likely to reach. AI-based demand-side platforms (DSPs), for example, use deep learning techniques to predict the likelihood of an ad click even before the ad is shown.

This form of targeting allows the marketer to allocate budget to the most promising audiences, boosting overall performance. Big data also enables real-time auction bidding based on multiple factors at once – device type, time spent in other apps, and even in-app purchase events, all of which play a critical role in advertising algorithms.

AI for personalization

Another important usage scenario is personalization. AI-powered dynamic creative optimisation (DCO) combines ad creative components based on each user's profile. Thousands of unique versions of the same marketing campaign can be created, swapping pictures, headlines, and offers based on age group, geo, in-app activity, and device model.

For example, a travel app can create a DCO campaign where users from countries with colder climates can view images of palm trees and sand beaches, while those living in cities can view ads of countryside ranches or retreats in the woods.

Generative AI makes it easy to develop unique materials (text copy, images, and video clips) for each user segment. With this, advertisers can generate a wide range of ad concepts spanning various art styles, languages, and cultural nuances, and the AI can then analyze the top performers to boost overall campaign effectiveness.

AI for retargeting and re-engagement

Retargeting and re-engagement marketing rely on AI-assisted data analysis to create personalized content based on user behaviour. In this case, user activity behaviour data is fed into an ML algorithm, which creates audience clusters based on the specific user journey and estimated financial value of each user (for instance, you could segment for the top 5% spenders that have been dormant for the last 10 days).

AI-assisted user segmentation tools can identify even more nuanced user behavior patterns, factoring in thousands of parameters, and then generate personalized ads according to the user’s unique scenario.

AI for creative assets and analytics

On the creative side, AI enables the automatic testing and creation of creative assets. This enables marketers to create multiple versions of advertisements and identify underperforming ones using AI.

For instance, a gaming business’s user acquisition team could use AI to create animated video trailers featuring 10 different characters or themes. AI can then pause the underperforming ones and iterate on the best-performing ones.

This has been seen to greatly improve the performance of advertisements. For example, TikTok reports that its Smart Performance Campaigns helped AppQuantum reduce cost-per-install by 40% and increase return on ad spend (ROAS) by 50% for its hit game Gold and Goblins.

AI-driven creative analytics is becoming increasingly important in the gaming industry. Studios like Supercell are already using machine learning to evaluate the effectiveness and integrity of their advertising content.

These systems can automatically identify poor-performing ads or flag those that may contain biased or offensive elements. By combining natural language processing (NLP) with image recognition, these tools help detect problematic messages before they reach the target audience. This proactive approach not only protects brand reputation but also ensures compliance with advertising laws.

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New trends and innovations

As AI capabilities mature, new advancements are rapidly reshaping how mobile advertisers build, deliver, and optimize campaigns. Let’s dive into some of the most important developments shaping the next wave of AI-powered mobile advertising.

AI creative at scale

As we’ve seen, generative AI is already making its way into the creative process for advertising. Beyond Amazon and Meta’s AI offerings, tools like DALL·E and Stable Diffusion are being used to produce ad visuals for mobile apps and videos. Early experiments – such as Nike’s campaign featuring an AI-generated Serena Williams – have shown promising results.

Cross-device and IoT

AI will become more involved in advertisements across multiple devices. If the user has viewed an apparel ad on their mobile device, AI can ensure that the same type of ad appears on the smart TV or smartwatch at a convenient time.

Internet of Things (IoT) data from connected car systems and wearable devices can help train AI-driven advertising models. For example, if a connected car system detects that the driver has stopped for a break, AI can deliver a relevant mobile ad—such as a nearby café promotion.

AI innovations for a privacy-first era

With privacy regulations shifting, AI techniques like federated learning and on-device machine-learning models are becoming increasingly prominent. Google’s Privacy Sandbox illustrates this shift by tapping into device-level information for ad targeting, rather than sharing user-specific data. Going forward, more AI solutions will likely focus on aggregated insights or simulated data sets that protect user anonymity.

Such advancements are being seen across the globe. In Asian markets, mobile advertising has been surging faster than in the West. In China specifically, super-apps WeChat and Douyin rely on AI-driven algorithms that deliver personalized advertisements in messaging and video content without regulatory impediments. In Southeast Asia, mobile ad spend is increasingly driven by mobile games as well as ecommerce-integrated ad formats.

In Europe, privacy-focused government regulations have pushed advertisers to rely on anonymized data when using AI in campaigns. As a result, Apple’s SKAdNetwork has effectively become the default attribution framework. Meanwhile, in North America, AI-driven ad creatives are already mainstream, and marketers now treat them as a standard part of the workflow.

Ethical, privacy, and regulatory considerations

AI’s expanding role in mobile advertising comes with important privacy obligations. Laws like GDPR and the Digital Markets Act in Europe, CCPA in California, and the forthcoming APRA legislation in the US all set strict rules for how personal information can be used. GDPR, for instance, requires advertisers to secure user consent before running targeted ads.

This has pushed the industry toward using aggregated and anonymized data instead of individual identifiers. Apple’s SKAdNetwork and Google’s Privacy Sandbox reflect this shift, offering advertisers only high-level metrics on installs and conversions. Here, AI and machine-learning models help bridge the gap by combining anonymized data with information from users who have opted in to tracking.

Fairness, bias, and consumer trust

Another area of concern is fairness and bias. ML algorithms trained on historical data can reinforce existing biases – for example, showing more housing or financial ads to one gender or ethnic group than another – so advertisers must actively monitor their models.

A recent IAB study found that more than 60% of ad-industry executives say their company has already used AI to create ads, yet only about half of Gen Z and Millennial consumers feel positive about AI-generated advertising, compared with the 80% of ad-industry respondents who assume they do. These findings show that marketers must take responsibility for managing AI carefully, including regularly auditing models for bias and safety to meet strict guidelines.

Global regulations and emerging compliance requirements

Large-scale regulations are imminent. The EU AI Act (proposed legislation) introduces the requirement for “high-risk” AI systems to satisfy accountability requirements; this could apply to mobile advertising platforms based on ML for the purposes of targeting.

In the case of China, legislation has been enacted stating that AI-generated content must be clearly marked (effective as of Sept 2025). This would mean that a promotional video produced by generative AI software could not escape being labeled as “synthetic media” (to distinguish the content as artificially produced). The implication here for advertisers operating across multiple countries is that what is legal or desired in the US today could very well be prohibited elsewhere.

First-party data, user permissions, and responsible deployment

In terms of data-usage compliance, Apple has already restricted many mobile apps from accessing the Identifier for Advertisers (IDFA) unless the user gives explicit permission. As a result, marketers now rely far more on first-party data – such as analytics and CRM information – along with contextual signals. AI helps here as well: machine-learning models can analyze the limited and often messy in-app event data available, or combine it with first-party sources, to build effective targeting strategies.

The Adjust Privacy Guide emphasizes this as a necessary approach for marketers operating under modern privacy laws. Ultimately, AI must respect user permissions around opting in or out. For instance, if a user declines targeted advertising, AI systems should shift toward context-based marketing instead.

AI governance and human oversight

Trust between the consumer and the business must remain the highest priority. According to the IAB, over 70% of marketers have already experienced an AI-related incident, with 40% of the affected campaigns having to be paused or pulled mid-run because of AI errors.

Marketing executives are now recognising the dangers of AI misuse for their brand. They’re pushing for internal frameworks, external audits, and stronger AI governance policies to ensure accountability.

In practice, mobile marketers often maintain a human checkpoint, using AI to surface potentially sensitive content instead of letting it run automatically. To support this, brands increasingly appoint a marketing-tech expert or a data-ethics owner to monitor AI processes and guide responsible deployment.

The golden rules of AI-powered mobile advertising

If you’re looking to incorporate AI in your mobile advertising strategy, here are a few golden rules to follow:

1. Start with first-party data

Collect and validate your own user data (in-app behaviour, purchase history, CRM), as this is the fuel that AI engines run on. This should involve improving the user consent management process so you can collect more data easily by providing advanced reasoning for data collection. This data can be used as the input for the AI analytics engine.

2. Invest in AI ad tech carefully

Review DSPs and creative platforms that offer AI-driven capabilities, including Meta Advantage+, Google Performance Max, and programmatic buying tools. Alongside this, establish a clear AI governance workflow with a checklist for AI-produced ads. For every AI ad, be sure to ask:

Does it match our brand voice?
Is any part of the content risky or inappropriate?
Does it follow our brand guidelines?

3. Use dynamic creative

Incorporate DCO into your campaigns to deliver personalized messages at scale. Leverage machine learning to run continuous A/B tests, automatically pausing underperforming assets and iterating on the best ones. When possible, enhance top-performing creatives with generative AI and use AI voice-overs to streamline localization.

4. Test AI-based targeting, but keep evaluating fairness.

AI lookalike models can help expand your audience, but it’s essential to review their performance regularly and check for bias or hallucinated outputs. Always scrutinize the reasoning behind your targeting and make sure your audience definitions are explicit and transparent.

5. Build AI fluency in the organization

Make sure your marketing and product teams understand the fundamentals of AI. Foster collaboration across marketing, data science, and legal teams. Workshops or training sessions on AI ethics and emerging advertising technologies can help teams stay aligned and avoid missteps.

6. Stay on top of regulations

Keep up to date with privacy laws such as GDPR, CCPA, emerging US federal proposals, and AI-related regulations. For each marketing initiative, ask: what data are we accessing, do we have explicit consent to use it, and are we able to transparently disclose where AI is involved?

Final thoughts

AI is already transforming mobile advertising by making hyper-targeted, personalized campaigns possible at scale.

For CMOs and product leaders, leveraging AI-driven tools is key to optimizing media spend – but it must be done within clear strategic and regulatory boundaries. With thoughtful use of data and machine learning, marketers can pinpoint high-value audiences and deliver more relevant messages, a tactic that has already shown strong ROI gains in categories like gaming.

At the same time, ethical and privacy considerations must be included from the start. Ensure compliance with international data regulations, keep humans in the loop for validation, and maintain transparency to build consumer trust.

AI’s capabilities – from customized ad formats and automated voice-overs to autonomous bidding and Amazon-style recommendation systems – are expanding rapidly. The companies that pair innovation with responsibility are the ones that will ultimately win, achieving faster iteration, deeper insights, and more effective mobile advertising worldwide.