AI has been part of the mobile user acquisition industry for many years, especially in the form of machine learning algorithms leveraged by tech companies to optimize advertising campaigns. It’s no surprise then that it is expanding its footprint all over the industry in 2026, when AI is shaking up every business on planet earth.
In this article we have listed three different user acquisition topics significantly affected by AI this year. We have excluded the use of generative AI for creatives’ optimization, already covered by previous articles in our blog.
1. Agentic AI for user acquisition
3. ASO vs generative AI in user acquisition
Agentic AI means an artificial intelligence system that performs specific tasks without or with very limited human supervision. As mentioned before, AI has already been extensively used in order to optimize user acquisition campaigns. The main difference here is the decision-making part of it. Indeed, agentic AI plans and executes actions based on its own computations and analysis, without human guidance.
In terms of user acquisition campaigns, this means agentic AI, given a specific goal, can allocate budget to multiple channels, test & rotate different ads, and update bidding and targeting strategies. By measuring signals across endless touchpoints, agentic AI is able to identify patterns and create scores in order to make data-driven decisions.
The first and most important thing is to clearly define the goal the system is trying to achieve. Then, clearer measurement, smarter decision making and more accurate feedback from the agentic AI will do the rest. This will shift time and resources from the manual and daily adjustments to long-term strategic planning and definitions.
While AI has always been part of the campaign optimization process, it has now improved and extended its capabilities. AI informs the UA platform bidding system by observing and analyzing multiple metrics and signals. Based on such data, AI assesses whether or not impressions and clicks should be bought and their predicted value in terms of KPIs like CPI, CPA, ROAS, retention rate, etc. And the same logic applies not only to single impressions and clicks, but to clusters of them, segmented with signals like operating system, device type, etc.
This way, AI doesn’t make decisions based on costs only, but compares the cost to the value it brings. And this is especially true considering the full funnel perspective that AI can take into account. By tracking users’ behaviour way past the install, the optimization towards ROI further improves. App openings, purchases, subscriptions & refunds, engagement and endless similar metrics are included in AI measurement and optimization.
By combining such capabilities with creatives’ optimization, agentic AI makes user acquisition campaigns smarter and more profitable. Indeed, if all the correct post-install signals are measured, optimization towards lower costs will be replaced by optimization towards higher revenue and profits.
Finally, as it is happening to SEO and websites, AI is affecting how people find apps in the stores. While mobile ads and ASO remain still relevant, more and more users are redirected to app stores directly from AI assistants like Gemini, ChatGPT and Claude. With a shorter attention span, users are passively scrolling through their feed, making in-app ads less effective than before.
This dramatically alters the discovery phase of new apps by people. What initially originated from google search and app stores, now starts with generative AI and chat assistants. Thus, ASO, like SEO, faces increasing challenges in its effectiveness, as well as attribution does. Indeed, apps need to track the activity that comes from AI tools and to correctly identify a potential new user’s journey which moves through different touchpoints (web, app, AI).
However, this doesn’t mean the end for ASO, like it isn’t for SEO. Apps need to update their descriptions following a new approach. The goal should be to make the apps relevant to AI assistants, in order to be recommended to users and generate download intent. In addition, by updating the attribution process, together with MMPs, it is possible to effectively track the audiences’ overlap between web/AI and apps.
Conclusion
After the first six months of 2026, it’s pretty clear that AI will keep re-shaping the user acquisition industry. It brings both challenges and solutions, all of which need to be carefully analyzed and assessed. The 3 main topics addressed in this article are:
- Agentic AI in campaign management
- AI for UA campaign optimization
- How AI affects ASO
While there are multiple additional layers of mobile user acquisition affected by AI, we focused on these three because of their immediate effect, which is already visible to any app marketers in the industry. If you want to be up to speed about the latest trends of AI and user acquisition, subscribe to our newsletter and get a free report about the state of mobile apps in 2026 here!
