When a product team searches for ‘how to add AI’, they’re often in the same position as you are now. They have a connected product (IoT product), or an idea for one, and they want to understand whether they should add AI to it.

The answer is usually yes. The more important questions are when and how to bring AI into the design. That decision shapes every hardware choice that follows.

This guide explains what AIoT (or AI IoT) actually means, where it adds genuine value and what a well-structured development process looks like from the start. It will help you decide whether AI belongs in your product and what processes you need to put in place before committing to a build.

---

What does AIoT actually mean?

AIoT stands for artificial intelligence of things. It’s an established term for a specific kind of product architecture rather than a marketing label. Your phone is an AIoT device: the facial recognition to unlock your phone works through AI. When Google Maps reroutes you around a traffic jam, that’s cloud AI acting on data from millions of connected phones. 

You’re using AIoT every day without even thinking about it.

A Nest or Hive thermostat can help illustrate the relevance of connected product development. A standard thermostat lets you set a temperature, but a connected one lets you control it remotely. An AIoT thermostat learns your schedule, adapts to the weather forecast and acts accordingly. 

Connection without intelligence is remote control. But AIoT makes a product genuinely useful.

The distinction is worth stating clearly:

• IoT means a device can communicate. It sends and receives data across a network.
• AI means a system can analyse and adapt. It draws patterns from data and uses them to make decisions or predictions.
• AIoT brings both together in a device that collects and communicates data and uses AI to act on that data intelligently.

It sounds simple. But in practice, building a product with those three capabilities working as one coherent system is a design challenge.

Most functionality that is marketed as AI in product development is far narrower than the term implies. Understanding what AIoT actually requires at the hardware and architecture level is the starting point for making good decisions about it.

Why does AIoT need to be designed in, not added later?

Working out when to incorporate AI into the design is the most consequential decision in an AIoT project.

AI-capable hardware has different requirements from a standard connected device. For example, it needs more processing power, different memory architecture, specific power management considerations and, in many applications, specialised processors designed for machine learning inference. 

Those requirements affect the printed circuit board (PCB) layout, component selection, thermal performance and enclosure design. They shape the system architecture before a single line of firmware can be written.

If the design incorporates AIoT from the outset, then the hardware, power budget, data pipeline and firmware all work together as a system. The AIoT will deliver what it promises: a product that senses, communicates and responds intelligently. Each capability reinforces the others.

If AI is treated as a later addition, the constraints compound: the device could lack sufficient processing headroom, the data pipeline may not be structured correctly, and the firmware may require significant rework. The result is a slower, more expensive development and a product that performs way below its potential.

The specification stage is more involved for an AIoT product than for a standard connected device. But the extra work in the beginning leads to a much cleaner path to manufacture.

Where does AIoT add real value?

The AIoT market has grown rapidly for good reason. Grand View Research valued the global market at $171.4 billion in 2024 and projects it will reach $896.8 billion by 2030. The compound annual growth rate of 31.7% reflects genuine utility across manufacturing, health care, logistics and infrastructure.

But market size doesn’t tell you whether AI belongs in your product. The relevant question is simpler: does your product collect data that, if properly analysed, could make the product more useful, reliable or efficient?

Logistics and transport are among the most obvious AIoT applications.

IoT can collect and transmit data in real time, including:

• Vehicle locations
• Load status
• Route conditions
• Fuel consumption

AI can then handle the analysis and adaptation, including:

• Routing decisions
• Maintenance scheduling
• Demand forecasting
• Resource allocation

Neither discipline fully replaces the other. The combination of IoT and AI makes modern fleet intelligence possible. It only works because the data architecture and the analytical layer were designed together.

Predictive maintenance in industrial settings is another strong use case. Sensors on machinery collect vibration, temperature and acoustic data continuously. AI models trained on that data identify patterns that precede equipment failure, often days or weeks before any visible signs. The AIoT product can anticipate faults as well as monitor data.

Medtech is one of the most compelling sectors for incorporating AIoT. As well as collecting readings, an AIoT-enabled glucose monitor can identify trends, flag anomalies and adapt its behaviour based on what it learns about an individual user over time.

The common thread across all three examples is this: IoT provides the data. AI provides the understanding. Together, they enable a product to do something that neither could do alone.

But you still need to consider whether AI is necessary in your product at all. 

A well-designed connected product with solid firmware and a clear data architecture can be highly capable without AI. Only add AI if it can solve a problem that you can’t resolve without it. The cost and complexity are significant enough that the application needs to justify the investment.

On the device, in the cloud or both?

This is one of the most important technical decisions when designing an AIoT product, and there is no universal answer.

Cloud AI processes data on remote servers. It benefits from large data sets, scalable compute and centralised model management. Model updates can be deployed without touching the device. 

The trade-offs are network latency, connectivity dependency, ongoing infrastructure cost and data privacy considerations.

Edge AI runs the model on the device itself, at the point of use. It responds in real time without network dependency, operates in offline or intermittent-connectivity environments and keeps sensitive data on the device. 

The constraints are hardware resource limits, power consumption and the complexity of deploying model updates across devices in the field.

	Edge AI	Cloud AI
Response time	Immediate: no network round trip	Dependent on connectivity
Connectivity need	Can operate offline	Requires reliable connection
Data privacy	Stays on device	Transmitted externally
Processing power	Constrained by device hardware	Almost unlimited
Ongoing cost	Low infrastructure cost	Bandwidth and compute costs
Local adaptation	Strong	Works from aggregate data
Model updates	Requires device-side deployment	Centralised and straightforward

Most well-designed AIoT products use both. Cloud AI handles broad pattern recognition, model training and analysis that requires large data sets. Edge AI handles real-time response, local adaptation and operation in environments with limited connectivity or excessive latency.

The choice between edge, cloud and hybrid should happen at the specification stage. It affects component selection, PCB specification, firmware architecture, power budget and unit cost.

A closer look: AI hearing aids

Hearing aids clearly illustrate the potential and requirements of AIoT architecture.

Traditional hearing aids amplify sound. Well-designed models amplify sound selectively. However, the selectivity is based on fixed rules, which means that its efficacy is limited. A concert hall, a restaurant and a busy street each require a different response. No fixed algorithm can anticipate every acoustic environment a wearer encounters.

Modern AI hearing aids, from manufacturers such as Phonak, Starkey and Signia, use deep neural networks trained on large audio data sets to classify sound environments in real time. The device identifies whether the wearer is in a conversation, a crowd, near machinery or listening to music and adjusts its behaviour accordingly. 

Some models learn from individual user preferences over time, adapting to the specific acoustic situations that the wearer most commonly encounters.

This is edge AI running on a device that sits in an ear canal. The processing happens in milliseconds, without any cloud connection, on hardware that must fit within extreme size and power constraints.

It’s not the AI alone that makes this possible. It’s the simultaneous design of the AI model, the audio sensing hardware, the processing architecture and the firmware from the start. 

The neural network’s requirements shaped the chip selection. The chip selection shaped the PCB. The power budget shaped both. None of those decisions could have been retrofitted.

A non-AI hearing aid amplifies, but an AIoT hearing aid listens, understands and responds. The difference is the architecture.

What does good AIoT specification look like?

The success of an AIoT project is determined at the specification state, and it is more involved than most product teams predict.

For a standard connected product, specification covers what the device does, how it communicates, what sensors it needs and what the firmware controls. For an AIoT product, it must also address the AI model. 

That means answering:

• What data does the model need to learn from?
• How will that data be collected, labelled and stored?
• Where will the model run: on the device, in the cloud or both?
• What processing capacity and memory does that require?
• How will the model be trained, tested and validated?
• How will it be updated in the field?
• What does success look like, and how will it be measured?

These questions require simultaneous input from electronics, firmware, data and product design teams. And this is where concurrent engineering can positively affect an AIoT project the most.

When electronics product design, firmware development and AI model work are aligned from the start, the hardware decisions reflect the actual needs of the model. 

Data requirements inform sensor selection before the PCB is designed. Power constraints shape the model architecture before it is trained. Testing needs influence the firmware before the prototype is built.

This alignment separates a well-executed AIoT product from one that delivers less than its specification promised.

What surprises teams the most at the start?

The scope of model development is the most consistent gap between expectation and reality in AIoT development.

A model that performs well in controlled conditions often behaves differently in the real world. 

This could be because the data collected during testing may not reflect the full range of conditions the product will encounter when in use. Edge cases that didn’t appear in training can produce unexpected results. The gap between a model that works in the lab and one that works reliably in the field requires significant iteration.

This doesn’t mean you should avoid AIoT, but you should make sure you plan for it properly. A feasibility study that tests the model against real-world data before hardware development begins is the most effective way to mitigate risk. It removes the largest unknown factor early while changes are still easy to implement.

There’s also the question of necessity. AI is a powerful tool, but it’s costly and complicated. A product that doesn’t actually need adaptive capability won’t improve if you add AI. AI is only worth the investment if there’s a specific problem that data-driven adaptation solves better than any simpler approach.

What to do before you start

Before approaching a development partner, follow these three steps to create a sound foundation for your AIoT project.

1. Run a feasibility study. Define what the AI model needs to do. Identify the data it requires. Build a model and test it. 

A tested, working model that performs reliably on real-world data gives the entire development project a defensible starting point. It also gives leadership the evidence needed to make clear investment decisions before hardware spend begins.

2. Map the data requirements. Determine what data the model needs, where it will come from, how it will be collected and whether that data is available in sufficient volume and quality. Data requirements must drive sensor selection, not the other way round.

3. Be honest about the commercial case. AIoT development costs more time and money than standard connected product development. The investment needs a clear commercial return. 

A product that commands a premium because it adapts intelligently, a hearing aid that learns, a maintenance system that prevents failure and a logistics platform that routes in real time all produce that return. A product where AI is a feature rather than a function is harder to justify.

Here are some questions you should answer before speaking to a development partner:

• What problem does the AI solve that simpler logic cannot?
• What data does the model need, and is it available?
• Will the AI run on the device, in the cloud, or both?
• What is the target unit cost and production volume?
• What compliance or certification requirements apply?
• Has the model been tested against real-world data?

A strong starting point does not need to be a complete specification. But it should include a clear problem, a tested model and an honest commercial case.

Ready to plan an AIoT product?

3Point1 brings electronics product design, firmware, compliance and manufacturing readiness together under one accountable team. We work across the full development process, from specification and feasibility through to a compliant, manufacturable product.

If you’re exploring whether AI belongs in your product, the most useful starting point is a conversation that covers the full picture: product goal, data requirements, hardware implications and the route from concept to manufacture.

Contact us to discuss your AIoT development project.