AI on smartphones & tablets
What is possible today, what CIOs need to know – and why modern device fleets are becoming a strategic prerequisite
Artificial intelligence (AI) is no longer an isolated tool that has to be launched deliberately. It is now an integral part of modern smartphones and tablets – embedded in operating systems, standard applications and assistant functions.
For CIOs, this fundamentally changes the starting point:
AI is no longer being introduced – it is already here. And it is increasingly used in a mobile, context‑aware and performance‑dependent way.
The crucial question is therefore not whether AI should be used, but how employees can be enabled to use these new possibilities productively without losing security, control and digital sovereignty.
AI goes mobile – a quiet but profound development
In recent years, AI has rapidly evolved from specialised applications into everyday functions. Studies by McKinsey show that generative AI is already used in more than 60% of companies – often initially informally and in a decentralised manner.
The entry point is increasingly the mobile device. Translations, text suggestions, automatic summaries or image analyses are used on the go – quickly, situationally and often without a conscious decision “for AI”.
Mobile devices are thus the place where AI becomes tangibly real in everyday working life.
Which AI technology is already standard today – and what is being added
A large proportion of AI usage today does not take place via separately installed tools, but via integrated functions. Modern operating systems come with AI capabilities embedded in mail, notes, camera or search functions. This AI is “silent” – it works in the background without employees perceiving it as a separate technology.
Alongside this, there are still consciously used, separately installed AI applications. These are usually cloud‑based, work with their own user accounts and enable particularly powerful but also more sensitive use cases.
For companies, this distinction is crucial:
not every use of AI is visible – but every use is effective.
On device or cloud? Why device capability suddenly matters
A key trend described, among others, by Gartner is the increasing shift of AI functions directly onto the endpoint device.
The reason is simple:
on‑device AI enables faster responses, reduces dependency on cloud services and can – if implemented correctly – offer advantages in terms of data protection. At the same time, cloud AI remains relevant wherever very high computing power or rapid model updates are required.
In practice, a hybrid model is emerging. What matters, however, is this:
the more modern the device, the more AI functions can be used locally and securely.
Context: which devices are already reaching their limits
Realistically, a clear trend is already apparent today:
- current AI functions require powerful processors, dedicated AI units and long‑term OS support.
- Devices that only receive limited updates or lack specialised AI hardware will continue to work, but will no longer offer the full range of functions.
As a rule of thumb:
- in the Apple ecosystem, AI functions deliver their full value primarily on newer generations (particularly evident from the iPhone 16 generation onwards).
- For Android devices, a similar picture emerges: AI capability depends heavily on processor architecture, vendor support and OS version, not just on the device’s age.
The point is not that “everything old is obsolete”.
The point is that AI capability is increasingly becoming a defining characteristic of modern endpoint devices.
Digital sovereignty does not arise from abstinence, but from enablement
Digital sovereignty does not mean excluding technologies, but understanding, contextualising and using them securely.
Employees who can use AI functions sensibly
- work faster
- in a more structured way
- and often with higher quality.
Deloitte studies show that the technological currency of work equipment has a direct impact on productivity and innovative capacity. Outdated devices not only slow down technical possibilities – they also slow down competence development.
Opportunities and risks are closely intertwined
AI assistants can provide enormous added value: support with texts, summaries, research or preparation. At the same time, risks arise when corporate information is inadvertently entered into external AI services or when private AI accounts are used.
ENISA explicitly points out that uncontrolled AI use – especially on mobile devices – is an increasing risk for data protection and information security.
The risk does not lie in AI itself, but in a lack of transparency and missing guardrails.
Control instead of prohibition – what this means in practice
A blanket ban on AI is hardly enforceable in a mobile reality – and often counterproductive. Successful IT organisations instead pursue a differentiated approach:
They define clear guardrails for which AI scenarios are useful and permitted, create transparency about usage and raise employee awareness of risks.
Control means:
- deliberate enablement strategies
- clear communication instead of grey areas
- and modern, powerful endpoint devices as a secure foundation.
Digital sovereignty emerges where employees know what they may do, what they should not do – and why.
Conclusion: AI as a competence amplifier – with the right foundation
Artificial intelligence is one of the greatest boosters of competence in recent years, especially on mobile devices. It must not, however, be used at the expense of security, data protection or control.
Modern device fleets are a central prerequisite for this. They enable up‑to‑date AI functions, modern security mechanisms and long‑term digital sovereignty.
Device‑as‑a‑Service (DaaS) supports exactly this approach: companies do not tie up capital, remain able to upgrade and ensure that employees work with contemporary, powerful endpoint devices – in step with technological progress.
Not every innovation has to be used immediately.
But it should be possible at any time.
Sources & Studies
McKinsey – The State of AI, Gartner – AI in Devices, Gartner – Edge AI, Gartner – Hybrid Ai & Edge Processing, Deloitte – Digital Workplace, Deloitte – Productivity, McKinsey – Generative AI, McKinsey – Productivity, ENISA – AI Threat Landscape, BSI – Mobile Endgeräte, BSI – Stand der Technologie, Apple – Software Support Lifecycle, Google – Update- & Security-Zyklen, Android – Update- & Security-Zyklen
Editorial note
The studies and sources mentioned are used to classify current technological developments. The specific relevance always depends on device type, operating system version and the individual situation of the company.