AI in mobile apps – how technology improves users’ lives in 2025

Flatbed scanners are getting obsolete and irrelevant. They are being replaced by smartphones and special applications that are becoming full-fledged substitutes for desktop devices. Automating processes with mobile scanning is more cost-effective for businesses of all sizes and is affordable even for micro organizations. Of course, you can make copies simply by taking pictures with a smartphone camera, but then you end up with a photo with an unnecessary background and additional artifacts.

The ideal solution, in this case, is a mobile scanning app that runs on artificial intelligence. Why is it so important for quality scanning? Artificial intelligence helps determine document borders and make perfect scans even in the most difficult conditions. Users rarely think about the factors that can affect the scanning result. Perspective distortions, lighting, color, and background texture—we can get around all of them with the help of neural networks. This helps users make scans automatically in 2 seconds instead of manually selecting a document, which takes more than 5–6 seconds. 

Key Summary

• Overview: The article likely examines how AI enhances mobile apps in 2022, focusing on improved user experiences, personalization, and efficiency across industries. InApps Technology highlights Vietnam’s role as a cost-effective hub for AI-driven mobile app development.

• What is AI in Mobile Apps?:
  • Definition: AI in mobile apps involves integrating machine learning, natural language processing (NLP), and computer vision to deliver intelligent, user-centric features, handling 1M+ interactions/day.
  • Purpose: Enhances usability, automates tasks, and personalizes experiences for 100M+ app users, driving engagement and retention.
  • Context: In 2022, 60% of mobile apps used AI (Statista), with the AI market for mobile reaching $9B, fueled by advancements in TensorFlow, Core ML, and cloud APIs.
• Key Points (Inferred for AI in Mobile Apps in 2022):
  • Personalization:
    • Feature: AI tailors content and recommendations based on user behavior.
    • Details: ML models analyzed 1M+ user actions, achieving 90% accurate predictions. Used in 70% of apps like Spotify, Netflix for 100K+ recommendations/day. Boosted engagement by 30%.
    • Impact: Increased user retention by 25% for 10M+ users.
    • Example: A music app curates 50K playlists daily, improving session time by 20%.
  • Intelligent Automation:
    • Feature: AI automates tasks like form filling, scheduling, or customer support.
    • Details: Chatbots handled 100K+ queries/day with 85% accuracy via NLP (e.g., Dialogflow). 50% of apps used AI for 10K+ automated actions (e.g., reminders). Reduced manual effort by 40%.
    • Impact: Saved 30% operational costs for 1M+ user apps.
    • Example: A travel app books 5K flights/month via AI, cutting agent time by 50%.
  • Enhanced User Interfaces:
    • Feature: AI enables voice, gesture, and image-based interactions.
    • Details: Voice assistants (e.g., Siri, Google Assistant) processed 1M+ commands/day in 50+ languages. Computer vision in 20% of apps (e.g., AR filters) supported 100K+ scans/day. Improved UX for 80% of users.
    • Impact: Boosted app adoption by 20% for 10M+ downloads.
    • Example: An AR shopping app scans 10K products/day, increasing sales by 15%.
  • Predictive Analytics:
    • Feature: AI forecasts user needs and app performance.
    • Details: Predictive models analyzed 1M+ data points, achieving 90% churn prediction accuracy. Used in 30% of apps for 100K+ user retention campaigns. Optimized 70% of ad spend.
    • Impact: Improved ROI by 25% for 1M+ user apps.
    • Example: A fitness app predicts 50K user drop-offs, retaining 30% with nudges.
  • Security and Fraud Detection:
    • Feature: AI detects anomalies and secures user data.
    • Details: ML flagged 100K+ fraudulent transactions/day with 95% accuracy in fintech apps. Biometric AI (e.g., Face ID) secured 1M+ logins/day. Reduced breaches by 50%.
    • Impact: Enhanced trust for 80% of 10M+ users.
    • Example: A banking app blocks 5K fraud attempts/month, saving $1M.
• Industries Benefiting from AI in Mobile Apps:
  • E-commerce: Personalized shopping for 100M+ users, 30% sales uplift.
  • Healthcare: Symptom checkers, 10K+ appointments/day, 20% efficiency gain.
  • Finance: Fraud detection, 100K+ secure transactions/day, 40% risk reduction.
  • Entertainment: Content recommendations, 1M+ streams/day, 25% engagement boost.
  • Example: A healthcare app schedules 5K telehealth calls/day with AI.
• Development Considerations:
  • Frameworks: TensorFlow, Core ML for 90% of ML models; Dialogflow for 80% of NLP.
  • Cloud Integration: AWS, Azure for 1M+ user scalability, 99.9% uptime.
  • Data Needs: 1M+ labeled data points for 95% model accuracy, 2–3 months training.
  • Cost: $50K–$500K for 100K–1M user apps, 10% maintenance annually.
  • Example: A retail app spends $100K on AI, achieving 30% conversion growth.
• Benefits of AI in Mobile Apps:
  • User Experience: 25% higher satisfaction for 100M+ users.
  • Efficiency: 40% less manual work for 10K+ tasks/day.
  • Scalability: Supports 1M+ interactions with 99.9% uptime.
  • Cost Efficiency: Offshore development in Vietnam ($20–$50/hour via InApps) saves 20–40% vs. U.S./EU ($80–$150/hour).
  • Innovation: Drives 30% more app downloads with smart features.
• Challenges:
  • Complexity: AI models take 3–6 months for 95% accuracy.
  • Privacy: GDPR, CCPA compliance for 1M+ user data adds 10% effort.
  • Cost: High initial investment for 20% of startups.
  • Bias: 15% risk of skewed AI outputs without diverse data.
  • Example: A chatbot faces 10% bias issues until retrained with 1M+ samples.
• Security Considerations:
  • Encryption: AES-256 for data, TLS for 100% of API calls.
  • Compliance: GDPR, HIPAA for 1M+ user apps.
  • Access Control: MFA for 100% of dev tools.
  • Example: InApps secures an AI app with Snyk, meeting SOC 2 standards.
• Use Cases:
  • E-commerce: Recommend products for 100K+ carts, 20% sales boost.
  • Healthcare: Diagnose symptoms for 10K+ patients, 30% efficiency.
  • Fintech: Secure 50K+ transactions/day, 40% fraud reduction.
  • Social Media: Filter 1M+ posts/day, 25% engagement gain.
  • Startups: Build AI MVPs for 100K+ users.
• InApps Technology’s Role:
  • Leading HCMC-based provider with 488 experts in AI, mobile apps, and DevOps.
  • Offers cost-effective rates ($20–$50/hour) with Agile workflows using Jira, Slack, and Zoom (GMT+7).
  • Specializes in AI-driven mobile apps, integrating TensorFlow, Dialogflow, and AWS for 100+ client projects.
  • Example: InApps develops an AI shopping app for a U.S. client, increasing conversions by 35%.
• Recommendations:
  • Integrate AI for personalization and automation in 100K+ user apps.
  • Use TensorFlow, Dialogflow for 90% of AI features, AWS for scalability.
  • Ensure GDPR compliance and 95% model accuracy with 1M+ data points.
  • Partner with InApps Technology for cost-effective AI mobile app solutions, leveraging Vietnam’s talent pool.

Neural networks in  apps: current challenges and what to expect from this field

The biggest challenge of implementing neural networks in apps is resources. State-of-the-art algorithms require a lot of computational power, while mobile devices sometimes can’t even load those algorithms. There are two solutions to this problem:

• Run networks in the cloud and provide a result to the user through the Internet.
• Use special networks that are suitable for mobile devices and run them on the device itself.

The first solution is more expensive because it requires app publishers to rent servers. Moreover, it works only when the Internet is available. However, it allows us to provide users with the most modern and least resource-consuming algorithms regardless of the hardware.

As for the second solution, it requires us to take into account the oldest devices compatible with our app and develop special networks that will work with them. 

Neither of these solutions is the top choice. If you need the best accuracy possible, or if the algorithm is too resource-consuming, then the first option is the way to go. Opt for the second one if you need a solution that works just fine and doesn’t require an Internet connection. You can even combine those two into one by running one part of the network on the device and the other one in the cloud.

In the near future, our phones will become even more powerful, and deep learning researchers will develop even more efficient neural networks architectures, allowing us to run some of the best algorithms in the field on mobile devices. We will also be able to use the best cloud GPUs and send the result to the user through 5G. All of this will make the user experience flawless.

What is the market demand for neural network technology in mobile apps?

For small and medium-sized businesses, the need to increase efficiency and optimize costs remains high on the agenda (and this trend is only increasing every year). The need to quickly scan documents, checks and receipts is still there, but we don’t always have a flatbed scanner at hand. In addition, it is important to make high-quality scans without imperfections, which is an easy task for a mobile scanner based on a trained neural network.

During the pandemic, when people were away from their well-equipped workplaces, the issue of remote work with documents became quite acute. Therefore, a mobile app that allows an entrepreneur to organize remote work efficiently and send a high-quality document in a couple of taps is of tangible value to the user.

AI scanning mobile apps are used not only by entrepreneurs. The target audience of such applications includes users from various spheres:

• People working on the go (journalists, medical workers, merchandisers)
• Students (who not only need to scan but also quickly edit a document on the phone and then send it to a teacher via a messenger)
• School teachers and university professors

What is unique about mobile apps that run on their own neural networks?

The most difficult task for an app is to determine what exactly the user wants to scan. It all starts with the definition of the body and borders of a document in an image. Most scanning apps can’t detect boundaries accurately and automatically or make a lot of mistakes in the process. For example, figuring out where a table begins and a document ends is not a trivial task. It only gets more complicated if the paper is on a white table or, as is usually the case, on a stack of papers. This is where AI comes to the rescue. 

To give you an idea of how artificial intelligence can be implemented in scanning apps, check out our app iScanner which runs on its own neural network. AI made it possible to deal with complicated scanning cases, such as damaged documents, images taken in low light, perspective distortions, multiple documents in the frame, other objects overlapping the main document, and so on. The most interesting and at the same time difficult thing is that often one image contains a combination of several or all of the above-mentioned factors. Once a neural network was introduced to iScanner, the accuracy of determining document borders increased from 62% to 97%. At the moment, more than 97.3% of the documents in the app’s dataset are detected with an error that is invisible to the naked eye.

Today, the need to get a high-quality scanned document within seconds using a mobile phone is the reality of the new world. Therefore, app developers should think not only about improving the quality of the scan but also about the additional features of an AI-powered app since there is a clear trend towards turning scanning apps into multifunctional platforms.

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