From smartphones that rewrite messages to computers that remember what appeared on screen, artificial intelligence is becoming a standard selling point. The harder question is whether these features solve real problems or simply give manufacturers another reason to sell us new devices.
By Pentacept Reporters
Artificial intelligence is no longer confined to chatbots, research laboratories and workplace software. It is moving into the devices people carry, wear and use at home every day.
Smartphones can remove unwanted objects from photographs, summarise recordings, translate conversations and suggest replies. Computers can search for files using ordinary language, improve video calls and generate images. Watches interpret health and fitness data, while headphones adjust sound according to the listener’s environment.
Technology companies describe this as the beginning of a more personal and intelligent generation of devices. For consumers, however, the arrival of another heavily promoted feature raises a familiar question: do we genuinely need AI in our gadgets?
The short answer is that some AI features are already useful. Others remain unreliable, unnecessary or poorly explained. The value depends less on whether a device carries an AI label and more on whether its intelligence solves a recurring problem without creating new concerns about privacy, accuracy, battery life and cost.
AI was already inside our devices
Artificial intelligence in consumer electronics is not entirely new.
Phone cameras have used machine learning for years to recognise scenes, improve low-light photographs and distinguish a person from the background in portrait mode. Facial recognition, voice assistants, predictive text, spam filtering and noise cancellation also rely on forms of AI.
What has changed is the arrival of generative AI. Unlike earlier systems designed to perform a narrow task, generative models can create, rewrite, summarise and interpret different forms of content.
This development has made AI more visible to consumers. Instead of quietly improving a photograph in the background, the technology can now draft an email, summarise a telephone call, alter an image or answer a question about information stored on a device.
Apple, Google, Samsung and Microsoft have all built AI functions into their product ecosystems. Their approaches differ, but they share a common ambition: to make the device an assistant that understands what a user is doing and helps complete the next task.
Microsoft’s Copilot+ computers, for example, use a specialised neural processing unit, commonly known as an NPU, to handle certain AI tasks locally. Microsoft says qualifying machines require an NPU capable of more than 40 trillion operations per second. Features include live captions, improved search, image tools and Windows Studio Effects for video calls. Microsoft’s Copilot+ PC guidance explains the hardware requirements and supported functions.
Google’s Android AICore allows compatible devices to run its Gemini Nano model locally. According to Google’s documentation, supported features can include text summarisation and suggested replies without sending the relevant information to Google’s cloud servers.
Samsung also allows users of supported Galaxy devices to restrict some AI processing to the device. The company notes, however, that feature availability depends on the model, software version and selected processing settings. Samsung’s support guidance provides an option to disable cloud-based AI processing.
Apple takes a mixed approach. Simpler requests may be processed directly on a compatible device, while more demanding requests can be sent to its Private Cloud Compute system. Apple says information sent through that system is used only to complete the request and is not stored or made accessible to the company. These remain the manufacturer’s stated safeguards, although Apple has also made parts of the system available for independent security inspection. Apple’s privacy documentation explains how the process is designed to work.
Where AI can make a real difference
The most useful AI features tend to perform small, repetitive tasks rather than dramatic ones.
Live transcription can help a journalist review an interview, allow a student to revisit a lecture or help a worker capture important points from a meeting. Translation tools can reduce language barriers during travel, business conversations and communication between families in different countries.
For African and diaspora users, reliable on-device translation could become particularly valuable. It may support communication in areas with limited or expensive internet access, although the usefulness of these tools will depend on how well manufacturers support African languages and accents.
AI-assisted photography is another practical area. Modern phones can reduce blur, improve lighting and help users locate images without scrolling through thousands of files. For people who create content or run small businesses from their phones, these improvements can save time and reduce the need for additional editing software.
Accessibility may offer an even stronger justification. Real-time captions can support people with hearing difficulties. Image descriptions can help users with visual impairments understand what appears on a screen. Voice control can make devices easier to use for people with limited mobility.
These applications demonstrate what consumer AI does best. It becomes valuable when it works quietly, saves time and makes technology easier to access.
When intelligence becomes a marketing label
The difficulty is that the term “AI” is now attached to almost everything.
Televisions, toothbrushes, refrigerators, ovens, vacuum cleaners and mattresses are increasingly promoted as intelligent. In some cases, the technology may adjust settings or identify patterns that improve the user’s experience. In others, the label describes a basic automated feature that does not require the kind of intelligence suggested by the marketing.
Consumers should therefore ask what the AI function actually does.
A washing machine that adjusts water consumption according to the weight of a load may provide a measurable benefit. A kitchen appliance that requires an account, an application and access to personal information before offering a simple recommendation may create more inconvenience than value.
The AI label can also be used to encourage unnecessary upgrades. Some newer features require more memory and specialist processors, which means they may not run on older devices. A perfectly functional phone or computer can suddenly appear outdated because it does not support the latest AI tools.
That does not automatically make a new device a sensible purchase. If a consumer will rarely use the advertised functions, paying a premium for AI hardware offers little practical return.
Privacy cannot be an afterthought
The more a device knows about its owner, the more useful it may become. That is also what makes it sensitive.
An intelligent phone may analyse messages, photographs, contacts and location data. A computer assistant could potentially process documents, browsing activity and information displayed on screen. A wearable device may collect health, sleep and movement data.
The first privacy question is therefore whether the processing happens on the device or in the cloud.
Local processing generally offers advantages. Information does not need to leave the gadget, features may work without an internet connection and responses can arrive more quickly. It is not a complete guarantee of security, but it reduces some of the risks associated with transmitting personal data to external servers.
Cloud processing can run larger and more capable models, but it requires users to understand what information is being sent, how long it is kept, who can access it and whether it may be used to improve the service.
The UK Information Commissioner’s Office states that AI systems using personal data remain subject to data-protection requirements, including transparency, security and data minimisation. Its guidance on AI and data protection makes clear that organisations should collect only the information required for a defined purpose and explain how that information is used.
Consumers should not have to search through complicated menus to discover whether an AI feature is analysing personal data. Privacy options should be clear, understandable and available before the feature is activated.
AI can still get things wrong
An AI assistant may sound confident even when its answer is incorrect.
This is especially important when gadgets summarise conversations, interpret health information or provide guidance that could influence a serious decision. A summary may leave out context. A transcription tool may misunderstand a name or accent. An image editor may alter details that were present in the original photograph.
The United States National Institute of Standards and Technology identifies confidently presented false information as one of the risks associated with generative AI. Its Generative AI Risk Management Profile recommends evaluating and managing such risks rather than treating AI output as automatically reliable.
Users should view AI-generated summaries, recommendations and answers as assistance, not unquestionable fact. Important information should still be checked against the original recording, document or trusted source.
For journalists, legal professionals, healthcare workers and others handling sensitive information, this distinction is essential. Convenience must not replace verification.
Battery life and environmental cost
Running AI directly on a gadget requires processing power. This can affect battery consumption, heat and overall performance, particularly when a device is handling a large model or generating lengthy content.
On-device AI research has identified energy management and limited computing resources as continuing technical challenges. A 2025 survey of on-device AI models found that privacy and offline operation offer clear benefits, but hardware limitations and energy efficiency remain important concerns.
There is also a broader environmental question. If AI features encourage people to replace working phones and computers more frequently, the result could be more electronic waste and greater demand for energy-intensive manufacturing.
A useful AI function should improve the life of a device, not shorten its relevance by making capable hardware feel prematurely obsolete.
Five questions to ask before buying an AI gadget
Consumers do not need to reject AI, but they should look beyond the label.
Before paying for an AI-enabled device, ask:
- What practical problem does the AI feature solve?
A specific benefit is more valuable than a long list of impressive demonstrations. - Will I use it regularly?
A function used once during the first week does not justify a large price difference. - Does it work on the device or require the cloud?
This affects privacy, speed and whether the feature works without an internet connection. - Can the feature be switched off?
Users should be able to control cloud processing, data collection and unwanted assistants. - Will it remain free?
Consumers should check whether advertised features require a subscription now or could move behind a paid service later.
So, do we need AI in our gadgets?
We do not need AI in every device, and we certainly do not need it simply because a manufacturer has placed the term on a box.
AI becomes worthwhile when it removes a genuine difficulty, improves accessibility, saves time or allows a device to perform an existing function more effectively. Translation, transcription, intelligent search, call-noise reduction and certain photography tools already show practical value.
The case becomes weaker when AI adds complexity, collects unnecessary information or serves mainly as a reason to increase the price of a familiar product.
The best intelligent gadgets may eventually be those that stop announcing their intelligence. Their features will work reliably in the background, respect the user’s choices and make everyday tasks noticeably easier.
Until then, consumers should judge AI devices by the same standard applied to every other piece of technology: not by what the marketing promises, but by what the product genuinely improves.
