How AI Is Transforming the Role of Mobile Applications

Mobile apps are entering a new era. As AI assistants and agentic systems reshape how people interact with digital services, enterprises face a vital decision: evolve their apps to work with AI, or risk losing user engagement and relevance.

Today’s users expect intelligent, context-aware, and frictionless interactions. Instead of tapping through menus, they issue natural requests to assistants, “Book my trip,” “Summarize this report,” “Pay this invoice”, and expect instant results. This shift from screen-based navigation to intent-based interaction is redefining what a “mobile experience” means.

At Krasamo, we help enterprises modernize their apps for this new reality, embedding on-device AI, connecting with cloud-based models, and integrating with emerging agentic AI protocols that link apps to autonomous agents, commerce networks, and real-world services. Learn more about our mobile app development expertise and how we build intelligent, AI-ready solutions.

The New Reality: From Apps to Intelligent Experiences

For the past decade, mobile apps have been the primary interface between brands and customers. But as AI becomes integrated into operating systems and cloud platforms, users are no longer limited to individual apps, they interact through assistants that orchestrate multiple services simultaneously.

This doesn’t mean apps are going away. It means they must become AI-augmented, capable of reasoning, personalizing, and participating in agentic workflows.

Apps are evolving in three key directions:

1. Embedded Intelligence: Apps now include AI capabilities that run directly on devices, enabling privacy-first, real-time interactions.
2. Cloud-Augmented Services: Apps connect to powerful AI models hosted in the cloud to generate, analyze, or summarize complex data.
3. Agentic Integration: Apps expose secure interfaces and protocols that allow external AI agents to interact, transact, and automate actions.

To see how these capabilities are transforming digital shopping and payments, explore our companion article, Agentic Commerce: The Evolution of Ecommerce.

This shift marks the beginning of a new era, where apps no longer operate in isolation but connect intelligently with AI systems and data sources.

How Mobile Apps Connect with AI

APIs: The Connection Between Apps and AI Models

This API layer enables developers to integrate generative features, like summarization, translation, and image description, directly into apps while maintaining security, observability, and production readiness.

Modern apps integrate AI through two main pathways, on-device and cloud, and increasingly through hybrid architectures that combine both.

Embedded (On-Device) AI

An embedded AI mobile app processes data locally using on-device models rather than relying on cloud servers. This “edge AI” approach enables:

• Real-time performance: No network latency or round-trips to the cloud.
• Enhanced privacy: Personal data stays on the device.
• Offline functionality: AI-powered features continue to work without connectivity.
• Lower bandwidth and cost: Local inference reduces network usage.

Examples include:

• Camera intelligence: real-time object detection and scene optimization.
• Predictive text and voice recognition: localized models improve speed and personalization.
• Health and security features: local biometric analysis for authentication and fraud detection.

These capabilities are powered by increasingly capable small language models (SLMs), compact, efficient variants of LLMs that can run on-device using mobile NPUs or GPUs. SLMs (such as Gemma) enable capabilities such as contextual reasoning, summarization, and natural language understanding directly within the app, allowing more intelligent and adaptive interactions without constant cloud dependency.

Running advanced generative models fully on-device is still limited to smaller models or high-end devices. These capabilities are supported by frameworks like LiteRT (Lite Runtime, formerly TensorFlow Lite), Apple’s Core ML, and Google’s AI Edge SDK, which allow enterprises to deploy compact, efficient models directly inside their apps.

Cloud AI Integration

For more complex or data-intensive tasks, like summarizing long reports, generating marketing copy, or analyzing customer data, apps connect to cloud-based AI models through secure APIs.

This connection allows mobile apps to:

• Access large-scale generative models capable of multimodal understanding (text, image, and voice).
• Deliver personalized recommendations, creative outputs, or semantic search results.
• Stay current with continuous model improvements without device updates.

Frameworks such as Firebase AI Logic, Vertex AI, and AI SDKs simplify this integration, providing ready-made interfaces for secure, scalable deployment.

Intelligent mobile apps can also connect with enterprise back-office automations, bridging user interactions with AI-driven operational workflows. To learn how AI is transforming these internal systems, read Implementing Intelligent Automation: AI in Back-Office Operations.

RAG in Mobile Apps

Mobile apps can use Retrieval-Augmented Generation (RAG) to ground AI responses in a user’s private or enterprise content, delivering more relevant, accurate, and context-aware results. RAG pipelines combine information retrieval with language generation, allowing models to reference external data instead of relying solely on pretraining.

Recent advances now make on-device RAG possible, bringing context to conversations without requiring model fine-tuning and, in many cases, without needing cloud access. For example, an app can reference a user’s stored documents, notes, or photos and provide summaries or answers directly on-device.

Modern SDKs such as the AI Edge RAG library support this capability on Android, enabling developers to customize how data is chunked, stored, and retrieved from local vector databases. Similar architectures are emerging across iOS and cross-platform frameworks, combining local RAG components with cloud-based reasoning when deeper context or model capacity is needed.

Although this is still an emerging capability with early-stage adoption, it is already enabling a new generation of context-grounded, privacy-preserving mobile AI experiences, where insights are generated from the user’s own data, not from external sources.

The Hybrid Model

Most enterprise apps will adopt a hybrid AI architecture: on-device AI for privacy-sensitive, real-time features, and cloud AI for deeper reasoning and large-scale computation.

For example, a financial app might use on-device AI to detect anomalies in spending patterns in real time while relying on a cloud-based model to analyze long-term trends and provide predictive insights.

This hybrid approach balances speed, privacy, and intelligence, ensuring that mobile apps remain both performant and capable of evolving alongside AI advancements.

For a deeper look at platform-specific tools, frameworks, and SDKs for building AI Android apps, visit the official Android AI developer site.

Making Apps Agent-Ready: The Next Step in AI Integration

The next frontier in mobile evolution is agentic AI, a paradigm where AI systems not only respond but also act autonomously across enterprise ecosystems.

To prepare apps for this new environment, developers are adopting emerging AI communication protocols that allow mobile apps and agents to collaborate securely and intelligently.

To enable apps to interact intelligently with AI agents, developers rely on agent development frameworks that manage how agents are created, orchestrated, and connected to enterprise systems. The mobile app (the frontend) communicates with these backend frameworks through secure APIs, exchanging user input and responses in real time.

Frameworks such as Google’s Agent Development Kit (ADK), LangChain, and CrewAI provide the infrastructure for multi-agent coordination and cloud-based reasoning, allowing mobile apps to remain lightweight interfaces while complex logic runs in the backend.

These frameworks provide the plumbing for an app (especially on the backend) to manage one or more AI agents, connect them to data/tools, and support nascent protocols.

Let’s break down the major protocols shaping this ecosystem:

1. MCP (Model Context Protocol)

The Model Context Protocol (MCP), introduced by Anthropic, is becoming a leading standard for enabling AI agents to access tools, data, and services. Although MCP is not yet widely deployed in production mobile apps, it is being tested in enterprise contexts.

In a mobile app workflow:

• The mobile app serves as the MCP host, where the user interacts with the AI.
• The MCP server connects to backend systems (APIs, CRMs, or databases) and exposes them as tools to the AI agent.
• When the user issues a request (“Summarize the latest sales reports”), the AI agent uses the MCP server to securely fetch the required data and return a summarized result inside the app.

MCP provides a unified and secure bridge between AI agents and enterprise systems, critical for apps handling real-world business operations.

2. A2A (Agent-to-Agent Protocol)

The A2A (Agent-to-Agent) protocol, developed by Google and now part of the Linux Foundation, enables AI agents to collaborate, delegating tasks, sharing context across domains, and interoperating across different frameworks. In mobile environments, this means:

• A user’s in-app agent can communicate with external agents (e.g., travel, logistics, finance).
• These agents coordinate actions to complete multi-step workflows.

For example, a travel app might use A2A for the following sequence:

“Book a flight to Miami for under $800.”
  → The user’s app agent communicates with a travel agent to find options.
  → A budget agent validates costs.
  → Both coordinate results and return a unified itinerary to the user.

A2A is still early in its lifecycle. Today, it functions primarily as an open protocol specification with a reference implementation, and current adoption is centered on experiments and pilot projects, not mainstream consumer apps. Public demos from Google and other partners demonstrate the potential for cross-domain, multi-agent collaboration, but large-scale cross-app communication remains emerging.

Still, this is the ideal moment to explore pilot initiatives. Even in its early form, A2A provides the cooperative foundation that will eventually allow traditional mobile applications to participate in interconnected ecosystems of specialized AI agents.

If you’d like, I can make this more concise, more enterprise-friendly, more technical, or more marketing-oriented, depending on how it will be used in your “AI in mobile apps” article.

3. ACP (Agentic Commerce Protocol)

The Agentic Commerce Protocol (ACP), co-developed by Stripe and OpenAI, enables agents to discover, build carts, and initiate purchases on behalf of users. ACP is actively being rolled out by early partnerships and not widely implemented in mobile; it is a cutting edge AI commerce feature.

In a mobile app:

• The user’s AI agent uses ACP to interact with merchant agents that expose product catalogs and checkout APIs.
• The agent builds a cart, presents it for approval, and initiates a seamless checkout.
• Payments remain secure through tokenization and controlled access to payment credentials.

This protocol opens the door for AI-driven ecommerce, where your mobile app becomes a smart purchasing assistant.

4. AP2 (Agent Payments Protocol)

The Agent Payments Protocol (AP2), launched by Google, extends the agentic ecosystem into secure, verifiable transactions. It introduces cryptographic mandates, signed by the user, that serve as proof of authorization when agents initiate payments.

AP2 is a newly introduced open standard that is still in the early stages of real-world adoption. Over time, we expect it to power mobile apps that can make verifiable purchases on behalf of users, based on cryptographically signed mandates. AP2 is presently in the prototype stage and we envision this supporting mobile apps to make purchases on behalf of its users.

Within a mobile app:

• The user gives a mandate: “Book the hotel and pay only if the total is under $800.”
• The AI agent acts on the mandate, executes the transaction securely, and generates an audit trail.
• The app displays confirmation and logs the transaction in the user’s account.

Together, ACP and AP2 enable mobile apps to handle end-to-end autonomous commerce safely and transparently.

To explore how agentic workflows are reshaping AI application development, see our related article, Application Development: Embracing AI Agents and Agentic Workflows.

Mobile AI Use Cases

As these architectures mature, enterprises are finding practical ways to apply AI within their mobile workflows. There are countless opportunities to embed AI into mobile applications, from automating everyday tasks to enabling entirely new forms of intelligence at the edge. The following examples highlight how AI features are enhancing field operations, logistics, and service management through mobile applications.

AI Voice-to-Form Inspections

Technicians can now dictate their field observations while AI automatically converts speech into structured inspection reports. Instead of typing long notes, a worker simply describes the site conditions, and the system interprets key details, such as materials, damages, and recommended actions, filling out the appropriate fields in real time. This voice-to-form automation eliminates tedious data entry, shortens reporting cycles, and improves accuracy. These capabilities are already being adopted in inspection, maintenance, and utilities environments, significantly increasing productivity and documentation quality.

Smart Scheduling and Dispatch Automation

AI-powered scheduling systems are helping organizations match the right technician to the right job based on skills, proximity, availability, and priority. When schedules change, the system automatically reassigns jobs and notifies affected customers, reducing downtime and missed service windows. Machine-learning models can also predict job durations and identify potential bottlenecks, allowing dispatchers to oversee rather than manually reschedule. This use of AI optimization is already delivering measurable efficiency gains in large field service organizations, with emerging systems adding semi-autonomous rescheduling capabilities.[1]

Parts Identification and Just-in-Time Procurement

Mobile AI is also streamlining how technicians identify and acquire parts. Using image recognition or natural-language queries, a technician can take a photo of a component or describe the issue, and the AI retrieves product matches from internal catalogs, checks inventory levels, and suggests compatible alternatives. When stock is low, the system can trigger a procurement request or route the order for approval. This process minimizes errors, accelerates repairs, and improves first-time fix rates. While fully autonomous ordering is still emerging, these AI-assisted workflows are already enhancing coordination between field teams and supply-chain systems.

Geospatial Awareness and Contextual Guidance
AI-enhanced mapping capabilities are improving how mobile teams visualize data and make decisions in the field. Modern applications integrate interactive maps that overlay asset locations, maintenance history, environmental conditions, and route efficiency in one view. Geospatial AI models can highlight high-priority areas, such as assets showing signs of failure or regions affected by severe weather, helping teams focus their attention where it matters most. Combined with intelligent routing and real-time updates, these systems are already reducing travel time, operational costs, and human error, with more advanced contextual filtering now entering early deployment.

Remote Visual Assessment and Inspection
AI is also enhancing remote diagnostics through mobile imagery and drone-captured visuals. Technicians or autonomous devices can capture high-resolution photos and videos, which AI models analyze to detect anomalies, label defects, and generate structured work orders. This capability is being used to inspect infrastructure, buildings, and equipment safely and efficiently, often reducing the need for on-site visits. As vision models and mobile integrations improve, remote assessment is evolving from an innovation project into a practical field-service solution.

Example: A Mobile Travel App in an Agentic Workflow

Here’s how these technologies interact in a real scenario:

1. User request: “Book me a weekend trip to Miami under $800.”
2. A2A: The app’s AI agent coordinates with travel, flight, and budget agents.
3. MCP: These agents use MCP servers to retrieve live data from booking systems and airline APIs.
4. ACP: The agent communicates with merchant agents to assemble a cart.
5. AP2: The agent finalizes the purchase securely using the user’s signed mandate.
6. App response: The mobile app displays confirmation and adds the itinerary to the user’s calendar.

The result? A fully automated, context-aware, AI-driven experience, from intent to execution, completed within the user’s mobile app.

While this example shows a travel app, similar agentic workflows can enhance logistics, healthcare, and field service applications, where mobile apps act as intelligent gateways to complex AI ecosystems.

Future-proof your mobile app strategy

At Krasamo, as a mobile app development company and AI developer, we encourage our clients to prepare for these emerging trends.

Talk with Krasamo about building AI-integrated mobile experiences that adapt to the era of assistants and agents.