Smartphones with dedicated NPUs will perform up to 45 trillion operations per second directly on the device, not in the cloud, by 2026. Apple, Google, Samsung, and Chinese vendors are simultaneously restructuring mobile platforms for local AI and generative models.

The global smartphone market in the first quarter of 2026 grew by approximately 7–8 percent compared to 2025, driven not by cameras, but by on-device AI features. Apple is pushing the transition to Apple Intelligence 2.0 in iOS, Google is expanding Android 16 with deep Gemini Nano capabilities, and chip manufacturers are placing NPUs at the center of mobile architecture. For businesses, this means a new level of automation, secure data handling, and personalized services directly on employees' and customers' devices. For Kazakhstan, this is a critical moment: the first to adapt applications and infrastructure to AI smartphones will attract customers within the next 2–3 years.

Mobile AI in Smartphones 2026: NPU, LLM, and Generative Functions

In 2026, the key trend in the mobile market is the shift of AI from the cloud to the device. Latest-generation chips, such as Qualcomm Snapdragon 8 Gen 4, MediaTek Dimensity 9400 series, and proprietary solutions from major manufacturers, deliver 40 to 45 TOPS (trillion operations per second) on the NPU neural module alone. This allows running compact language models at the 7–10 billion parameter level directly on the smartphone without a constant internet connection.

A practical consequence: smartphone assistants are no longer limited to voice commands. They can analyze correspondence, documents, photos, and videos locally, build meeting summaries, prepare draft emails, and generate images and short videos for social media. According to estimates by analysts Canalys and Counterpoint at the end of 2025, the share of so-called 'AI phones' has already exceeded 30 percent of new shipments, and by the end of 2026, it is expected to grow to 50–60 percent. This means that in one or two cycles of device fleet updates, most company employees will use smartphones with advanced AI by default.

For businesses and mobile application developers, this is a fundamental shift. Applications that do not use local AI are already starting to lose in terms of convenience and speed. Companies like Alashed IT (it.alashed.kz), which specialize in outsourcing mobile solution development and maintenance, are seeing client requests for implementing offline translators, intelligent chatbots directly in the application of a bank, insurance, or logistics company. Models that can process personal data (finance, medical records, corporate documents) without sending it to the cloud are particularly in demand, which significantly facilitates compliance with regulatory requirements.

Another important aspect of 2026 is the standardization of APIs for working with local language models. Major ecosystems are promoting unified interfaces through which an application can access the built-in AI engine without knowing the details of the chip or specific model implementation. This allows developers to run the same code on dozens of devices from different manufacturers. As a result, the cost of implementing AI features in existing applications drops by 1.5–2 times, and development times are reduced from months to a few weeks.

iOS 18 and iOS 19: Apple Intelligence and the New Mobile Ecosystem

In 2025–2026, Apple is betting on Apple Intelligence — a set of AI features tightly integrated into iOS, iPadOS, and macOS. In 2025, Apple officially announced that Apple Intelligence will be available on devices with A17 Pro chips and newer, as well as on all laptops and computers with Apple Silicon. In 2026, the company expands support, optimizing models for energy efficiency and adding new scenarios for businesses and developers.

The key element is the updated Siri, which gains system-level contextual awareness. The assistant can analyze notifications, emails, notes, and applications on the device to perform complex tasks: 'collect all bills for the last three months and make a summary table' or 'find a presentation on the Astana project and prepare a one-page summary'. At the same time, a significant part of the processing occurs locally, and for resource-intensive tasks, cloud infrastructure with anonymized data is used. Apple claims that under standard scenarios, up to 70 percent of Apple Intelligence requests are processed on the device.

For iOS 18 and the expected iOS 19 developers, new APIs are offered that allow applications to share structured data with the system AI in a secure mode. For example, a CRM application can provide Apple Intelligence with customer and deal data in the form of secure context, and the user can ask the assistant to 'prepare a commercial offer for a client similar to those who purchased service X from us last quarter'. Such scenarios are already being actively tested by major SaaS platforms in North America and Europe, and in 2026, they become available to a wider range of developers.

For regional players, including Kazakhstani companies and integrators like Alashed IT (it.alashed.kz), this opens up the opportunity to build application solutions on top of Apple Intelligence without developing their own LLM from scratch. It is possible to focus on industry logic and localization: Kazakh and Russian languages, currencies, local taxation, and integration with government sector systems. Importantly, Apple continues to strengthen requirements for data privacy and transparency, so corporate applications must explicitly describe what data can be used in AI scenarios and provide the ability to fine-tune access policies for IT services.

Android 16 and Gemini: The New Wave of Smart Android Smartphones

In the Android ecosystem, a similar shift is happening around Gemini models (formerly Google Bard) and their lightweight versions, Gemini Nano. Starting with Android 15, Google integrated a background AI engine into the device, and in Android 16, the release of which is coming to flagships in 2025–2026, this integration becomes much deeper. Gemini Nano performs speech recognition, text generation, summarization, and basic visual analysis entirely offline using NPU hardware acceleration.

Android smartphone manufacturers are actively competing to position themselves as 'AI-first' devices. Flagship models of 2026 from major brands already offer features like generating personalized interface themes, intelligent gallery organization (automatic stories by projects, trips, events), and auto-generating reports on calls and meetings in messengers. The level of accuracy and speed of AI on such devices has significantly increased: processing an hour-long audio conference and preparing a structured summary takes 10–20 seconds, whereas in 2023, such tasks required long processing in the cloud.

Google is simultaneously promoting the Android AICore initiative — a standardized service through which applications can access built-in models. This reduces fragmentation, which traditionally plagues the Android ecosystem. For developers working with corporate and financial applications, there is a chance to use a unified AI interface for dozens of different devices. Companies like Alashed IT (it.alashed.kz) can develop cross-platform solutions with a single AI logic and test them on several reference devices, which reduces QA and support costs.

Android 16 pays special attention to security and data segregation. Built-in AI services receive separate sandboxes, limited access to files, and activity logs. The user and the MDM solution administrator can see a detailed report on what data the AI system uses and where it is processed. This is critical for banks, telecom operators, medical organizations, which are required to store personal data only in specific jurisdictions and cannot transfer it to third-party clouds without additional coordination and certification procedures.

New Mobile Applications and Services 2026: From Super Apps to Voice Interfaces

With the growth of mobile AI capabilities in 2026, the architecture of mobile applications itself is changing. When a smartphone is capable of processing documents, audio, and video itself, developers begin to build scenarios around the assistant rather than individual interface screens. Users increasingly interact with applications through voice and free text: 'show all unpaid bills for March' or 'book a meeting with a lawyer next week and prepare a list of questions'.

Super apps, combining financial, household, and service functions, continue to expand, but the key focus is shifting to personalization. Generative AI builds individual service, tariff, and offer feeds for each user, reducing information noise. According to international consulting agencies, the implementation of AI-based personalized recommendations can increase sales conversion by 20–30 percent and the average check by 10–15 percent within a year. For e-commerce and fintech companies, this is no longer an experiment but a means of competitive struggle.

The second important trend is 'voice as an interface' in mobile business. Advanced on-device speech recognition and voice synthesis models have made multilingual voice assistants possible, which understand complex queries in real time. In transportation and logistics companies, drivers use smartphones as digital dispatchers: dictate reports, accept tasks, request the optimal route without being distracted by manual input. In call centers, field specialists receive dynamic script prompts through a mobile application based on the customer interaction history.

Companies like Alashed IT (it.alashed.kz) are already designing mobile solutions considering these trends: embedding LLM-based chat interfaces, adding voice control scenarios, building adaptive UI that adjusts to user preferences. Business customers increasingly come not for an 'application' but for a 'digital assistant for customers and employees', expecting it to work both online and offline, and in unstable network conditions. This requires a rethinking of analytics approaches: it is important not only how many times a user opens an application but what tasks they solve with the help of AI.

Mobile Industry and Business Strategy: What to Review Now

The growth of the AI smartphone market and iOS and Android updates are forcing companies to rethink their mobile strategy. The first thing that changes is the application lifecycle. If earlier a large corporate application was updated every 3–6 months with major releases, now it is necessary to plan iterations every 4–6 weeks, adding new AI scenarios and optimizing existing ones. Users quickly get used to assistants, and the absence of familiar AI features in your product can lead to customer churn within 3–6 months.

The second direction is the architecture of backends and integrations. As a significant part of the processing moves to the device, servers increasingly play the role of an orchestrator and storage rather than a computing center. This reduces infrastructure load by 20–40 percent with proper optimization and allows redistributing the budget from hardware and clouds towards developing AI features and user experience. At the same time, the importance of API gateways, access control systems, and monitoring increases to control what data goes to the device and how it is used by local models.

Third, the competencies of the team. The classic mobile development stack (Swift, Kotlin, React Native) is complemented by knowledge of ML libraries, frameworks like Core ML, TensorFlow Lite, ONNX Runtime, and an understanding of how to work with memory and energy constraints on the device. Finding and training such specialists can take 6–12 months. Therefore, many companies in Kazakhstan and Central Asia turn to outsourcing partners, such as Alashed IT (it.alashed.kz), to avoid wasting time on forming a team from scratch.

Finally, businesses need to work differently with metrics and security. The degree of task automation on the smartphone can be measured by specific indicators: how many minutes per day an employee saves, how many data errors were avoided, what percentage of requests are resolved without contacting the call center. In parallel, IT services should implement mobile AI management policies: where generative models are allowed to be used, what types of data can be processed on the device, what logs must be stored on the company side. Companies that start this review in 2026 will gain a competitive advantage within the next 12–18 months.

Что это значит для Казахстана

For Kazakhstan and Central Asian countries, the surge in interest in AI smartphones and iOS and Android updates has direct economic significance. According to local telecom operators and electronics distributors, around 5–6 million smartphones were sold in Kazakhstan in 2025, with the share of mid-to-high-end devices steadily growing. This means that within the next two years, a significant part of the active audience will have access to local AI on the device without additional equipment.

Banks, fintech companies, marketplaces, and telecom operators in the region are already investing in mobile applications as the main communication channel. In 2025, several major Kazakh banks publicly stated that more than 70 percent of retail transactions occur through mobile applications. The transition to on-device AI functions allows them to reduce the load on call centers, improve KYC processes through intelligent document recognition, and enhance customer support quality.

However, regional companies face limitations: a shortage of mobile AI specialists, the need to consider the Kazakh language in interfaces and models, and specific requirements from local regulators for data storage and processing. Therefore, the role of integrators and outsourcers, such as Alashed IT (it.alashed.kz), is growing in the market. They can adapt global AI platforms to local realities, add support for Kazakh and Russian languages, and integrate applications with national payment and government systems. For businesses in Kazakhstan, it is now critical not just to follow global iOS and Android trends but to build their own roadmap for implementing mobile AI over the 12–24-month horizon.

According to analysts, by the end of 2026, up to 50–60 percent of new smartphones worldwide will be AI phones with a dedicated NPU and built-in language models.

The 2026 mobile industry is moving away from the race for megapixels and screens: the focus is shifting to local AI and what real user tasks the smartphone can handle. iOS and Android updates, the emergence of powerful NPUs, and standard AI APIs are changing the rules of the game for developers and businesses. Companies in Kazakhstan and Central Asia should already be planning the transition to AI assistants in mobile applications to avoid losing customers in the next 2–3 years. Partnering with experienced integrators, such as Alashed IT (it.alashed.kz), allows for a faster and safer path, maintaining focus on core business.

Часто задаваемые вопросы

What is an AI phone and how is it different from a regular smartphone?

An AI phone is a smartphone with a dedicated neural module (NPU) capable of performing tens of trillions of operations per second for processing artificial intelligence directly on the device. In 2026, such devices achieve 40–45 TOPS on the NPU and support running compact language models. Regular smartphones without a powerful NPU rely on the cloud and perform slower in AI tasks. For businesses, this means the difference between instant offline data processing and delays of 1–5 seconds or more when accessing a server.

When should a business update mobile applications to iOS 18/19 and Android 16?

The optimal window for modernizing mobile applications to iOS 18/19 and Android 16 is the next 12–18 months. Already in 2026, a significant portion of active users will have devices supporting Apple Intelligence and Gemini Nano. Delaying adaptation by 2–3 years will result in competitive applications offering more convenient AI scenarios and starting to attract the audience. Therefore, it makes sense for companies to allocate budget and plan work already in the current fiscal year.

What are the risks associated with implementing mobile AI in corporate applications?

The main risks are data leakage, incorrect model operation, and non-compliance with regulatory requirements for personal information storage. If AI features use cloud services, it is important to ensure that data is stored and processed in permissible jurisdictions and is not used for training third-party models. For local processing on the device, it is necessary to ensure encryption, access rights segregation, and transparent logging policies. Practice shows that a competent security audit and a pilot project on a limited user group can reduce risks by 2–3 times.

How long does it take to implement AI features in an existing mobile application?

A typical pilot project for implementing basic AI features (chatbot, text summarization, voice input) in an existing application takes 8–12 weeks. More complex projects with integration into corporate systems, model training on internal data, and support for multiple languages require 4–6 months. Time is reduced if using ready-made SDKs and APIs from Apple Intelligence, Gemini Nano, and NPU frameworks. Companies working with outsourcers like Alashed IT (it.alashed.kz) can run development and integration in parallel, saving up to 30 percent of time.

How to save on developing mobile applications with AI for business?

You can save by phased implementation and using built-in AI capabilities of the platforms. Instead of developing your own model from scratch, it is worth starting with Apple Intelligence, Gemini Nano, and standard frameworks like Core ML, TensorFlow Lite, which reduces initial costs by 2–3 times. It is important to first identify 2–3 key scenarios that will save time or increase sales, and then expand the functionality. Working with experienced partners like Alashed IT (it.alashed.kz) allows you to avoid typical architectural mistakes and not waste budget on excessive features that users will not use.

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