This gap wasn't created by new technologies. It has always existed. What they did is make it more visible, and more costly to ignore.

For a long time, working with an OEM was relatively straightforward. You had a physical product, clear specifications, a manufacturer who could execute. The rules were known. So were the roles.

New product categories changed those rules. An AI wearable, a connected device, a smart medical instrument: these aren't products with interchangeable components you order and assemble. They're systems where hardware, software, experience, and architecture are interdependent from the very first decision. And that's where it gets complicated. Every choice triggers another. It's not an assembly line. It's a living system where every variable is connected to the others. Defining that system before going into manufacturing, making sure industrial constraints, design choices, and technical requirements align before molds are made and compromises are set in stone, is exactly the work an OEM doesn't do.

Translating, in this context, means converting. Taking an intent, often vague, never complete, and making it legible for someone who doesn’t speak the same language. The work isn’t to simplify. It’s to lose nothing in the passage from one world to the other.

What we do isn’t subcontracting, and it isn’t consulting. It’s taking ownership of the riskiest moment in product development: when everything is still open, decisions carry the most weight, and nobody in the organization has all the answers yet.

Concretely: defining the system architecture before choices get locked in .

• Component selection and packing (choosing the right electronic components and optimizing how they fit within the available space)

• DFA (Design for Assembly = designing the product so it can be assembled efficiently and without error at production scale)

• DFM (Design for Manufacturing = adapting the design to match the actual capabilities and constraints of the manufacturing process)

• EMC (Electromagnetic Compatibility = ensuring the device neither emits interference nor is affected by it, a mandatory certification requirement in most markets)

• PCB design (laying out the printed circuit board that connects and powers all electronic components of the product)

Prototyping to validate, not to impress.

Killing what doesn't hold early, before it gets expensive.

Delivering documentation any OEM or ODM can read without interpretation.

Design and engineering move together from day one, not in sequence. Decisions about form have consequences on architecture. Constraints on architecture have consequences on form. Separating the two is precisely what produces the compromises everyone regrets at the end of the process.

What we bring is a structured process that runs design and engineering in parallel. We explore where a technology creates real product and human value. We help you define what the product should be, and what it shouldn’t, aligning product vision, system architecture, desirability and feasibility. We design form, interaction, electronics, and software together, using design to test assumptions rather than decorate ideas. We build functional prototypes to validate product decisions in real conditions: technology integration, user interaction, materials, key manufacturing constraints. We prepare products for manufacturing without becoming a manufacturer. This phase ensures partner-ready documentation, manufacturing readiness, and a smooth handover to trusted industrial partners. We define manufacturing principles, assemblies, tolerances, electronics architecture, and supplier pathways so that any OEM or ODM partner can take over without friction.

Solid pre-industrial documentation, validated prototypes, a clear path to production.

There's one last thing OEMs don't do, and that nobody does for you if you don't anticipate it :

A manufacturer can reproduce a form at scale.
It can't create the reason someone will want that product in their life.

That reason lives in decisions that seem secondary: the way the object sits in the hand, the signal it sends before it's even switched on, the apparent simplicity of an interaction that took months of work to disappear.

Desirability isn't added value. It's a prerequisite.

A product that doesn't earn its place doesn't survive.

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Sometimes it happens slowly. A few delays here, a bit of friction there.
Sometimes it hits all at once, when what used to feel natural now feels heavy, complicated, or strangely disconnected from what your company actually stands for.

For us at Futurewave, that moment came when we looked at how our teams collaborated.

We are a multidisciplinary studio by design: strategy, design, engineering and tech experts under one roof, solving innovative hardware and product development challenges.

In theory, that diversity should have been our biggest strength.

In practice? Our structure kept getting in the way.

Projects flowed from one discipline to another depending on the stage. Strategy to design. Design to engineering. Engineering to tech.

It felt like a relay race where the baton kept getting dropped.

Information gaps. Communication loops. Unclear ownership. Mismatched expectations.

And one question became unavoidable:

If we are an innovation studio, challenge the status quo for our clients every day, why were we keeping the status quo for ourselves?

That question led to one of the biggest changes in our company’s history:
we reorganized Futurewave into small, empowered, multidisciplinary squads.

Here’s why we did it, how we approached it and what we’ve learned so far.

The limits of competency-based teams

On paper, our old model made perfect sense.

Designers in one team.
Engineers in another.
Innovation strategists and tech experts in their own groups.

It looked clean. Organized. Logical.

In real life, it felt more like friction.

These disciplines don’t just have different skills. They have different mental models, ways of working, ways of communicating and ways of making decisions.

When a project moved from one team to the next, it was like shifting gears without using the clutch. You could make it work, but it wasn’t smooth.

We kept seeing the same patterns:

• Teams optimizing for their discipline instead of the shared outcome

• Missing context and info between project stages

• Bottlenecks created by handovers

• Unclear ownership (who actually decides?)

• Reactive instead of proactive communication

• A sense that everyone was responsible, which meant no one truly owned the whole project

None of this happened because people lacked skill or motivation. The structure simply did not reflect the reality of our work anymore.

We needed something that matched the complexity, interdisciplinarity and speed of the projects we were taking on.

So we started searching for a better way.

Why we chose a squad model

One insight kept coming back:

Our structure did not reflect our identity.

Futurewave’s strength is the tight integration of strategy, design, engineering and technology. But when disciplines sit in separate teams, you don’t get collaboration, you get silos.

We wanted more.

More collaboration.

More shared ownership.

More early alignment.

More fluid decision-making.

More learning between disciplines.

More true multidisciplinarity.

And we needed a structure that could scale sustainably.

That is where the squad model came in.

We were inspired by Spotify’s model, but we did not copy-paste it. We adapted it to a service-company reality. Our clients and projects operate differently from a product-led tech company, so our version needed to be flexible.

The squad model fit for several reasons.

1. It matches how we actually work, together

Modern projects are no longer linear. They require designers and engineers to collaborate from day one, not pass work over a wall. The squad structure embraces this.

2. It makes room for real empowerment

Squads own their mission. They decide how to organize, how to deliver, how to coordinate. Leadership provides direction and context, squads choose how to execute.

3. It reflects our belief in innovation

We encourage clients to challenge assumptions, explore new solutions, and evolve their products. We needed to apply that mindset internally. Staying static would have contradicted our DNA.

4. It helps us scale

A squad is a self-contained unit.

When we want to grow, we can create another squad as long as the mission is clear and the team is empowered.

This opens the door to something very powerful:

internal entrepreneurship.

People inside Futurewave can one day spot an opportunity, a new region, a new offer, a new type of client, and build a squad around that mission. It is a way for people to grow, explore, and take ownership far beyond their job description.

The squad model was more than an operational change for us. It was a way to align structure with identity, ambition, and culture.

What a squad looks like at Futurewave

A squad is simple in definition:

A small, stable, multidisciplinary team that can handle a project from A to Z.

Each squad includes:

• Strategy

• Design

• Engineering

• Tech

• Project management

But what matters more is the mindset.

A squad has a mission.
Not just tasks. Not just clients. A purpose.

A squad is empowered.
The company leadership sets the strategic frame, the squad decides how to organize.

A squad owns outcomes.
No more “this belongs to design” or “engineering will decide later”.

The squad decides, together.

A squad is also a learning space.
People can take on responsibilities beyond their craft, operations, coordination, rituals, internal tooling, and more.

That autonomy is what turns a squad from a working group into a mini-company inside the company.

Our biggest fear: motivation

The biggest risk wasn’t structural. It was human.

This change cannot work unless people are motivated by it.

You can’t announce empowerment.
You can’t impose autonomy.
You can’t force ownership.

We worried about what might happen if people did not believe in the model, or if motivation did not spread naturally. We wondered whether some would feel confused or resistant, or whether the initial excitement would fade before the structure had time to settle. And of course, there was the fear that the culture would not follow the structure quickly enough.

The squad model is not something leadership can implement alone.

It only works if everyone is onboard.

That fear shaped how we designed the rollout.

Our change strategy: start with early adopters

We decided early on that we would not push this model onto the whole company in a top-down way. Instead, we took a more human, and in hindsight, much more effective approach.

We focused on the early adopters.

These were the people who naturally resonated with the idea, who saw the vision intuitively and immediately leaned in. They were excited, aligned, curious, and eager.

We doubled down on them.

Not to create favor or hierarchy, but to build momentum.

We invested more time with them. We explained the model in depth, tested early assumptions together, involved them in defining the new rituals and spoke openly about our own fears and motivations.

In short, we built trust first with those who were naturally aligned.

Why?

Because late adopters often base their motivation on what they see around them. If the first people to adopt the new model are excited, committed, informed, and engaged, that energy spreads naturally.

And it did.

Motivating the motivated first helped others feel curious instead of skeptical, included instead of forced. It created a positive chain reaction.

The hardest part: jumping before we felt ready

If you have ever redesigned a process, a workflow, or an entire company structure, you know this feeling:

You can overthinking it to death.

We spent months refining the framework. We rewrote processes, designed rituals, defined roles, improved the manifesto, mapped responsibilities and drafted playbooks.

But at some point, all the thinking becomes a comfort zone. You can always find one more thing to refine, one more document to finish, one more discussion to have.

The uncomfortable truth is:

Transformation only begins when you switch from planning to doing.

We weren’t 100% ready.
Probably not even 90%.

But real change rarely starts from completeness, it starts from courage.

So we jumped.

We flipped the switch.

We trusted that we would adjust in motion.

And yes, things did not go exactly as expected.

They never do.

But the model became real, and that was the only way forward.

How we rolled it out

We did not attempt a huge company-wide transformation in a single step. That rarely works, and it usually burns people out.

Instead, we focused on the big levers first.

Step 1: switch from competency teams to multidisciplinary squads

The biggest structural shift, and the most impactful.

Step 2: align the physical space

We changed the desk layout so squads sat together.

Small detail, huge impact.

Step 3: clarify missions and responsibilities

Every squad defined their own purpose within Futurewave’s frame. It is their focus area, a domain to own.

After that, we entered phase two, continuous improvement.

We met regularly with squads.

We listened, adjusted and clarified.

We added rituals when gaps appeared.

We redistributed responsibilities as people grew.

We improved processes as real-world friction surfaced.

Transformation became a journey, not a single moment.

Early results: the signals that told us we were on the right path

The first months after the switch were both telling and encouraging. Even if it is still early, we have already seen clear signs that confirm we made the right choice.

People are more motivated. Collaboration feels more natural and communication has improved significantly. Team members are taking on internal responsibilities beyond their core role, something that more rarely happened alone before. The organization feels clearer and the impact of multidisciplinarity is showing up in everyday work. Perhaps most importantly, autonomy has sparked a genuine increase in engagement.

These early signals did not appear because everything was perfect. They appeared because the collective energy shifted.

What we’re still learning

We are proud of the progress, but we know the work is not finished. The squad model works best when it evolves continuously. Today, we are still refining which rituals help squads the most, how to balance autonomy with consistency and how chapters and guilds should mature. We are learning how to support internal entrepreneurship, how to launch new squads the right way, how to maintain quality across disciplines and what leadership looks like in a structure built on empowerment.

This model was never meant to be implemented once. It is meant to be shaped over time.

Conclusion: the real transformation was cultural

Switching to squads wasn’t just structural.

It was cultural.

It aligned structure with identity.
It reflected our values: curiosity, collaboration, exploration.
It unlocked our multidisciplinary potential.
It made scaling more natural.
It restored clarity and ownership.

And most importantly:

It made us operate the way we always wanted to operate: innovative, connected and human.

We’re still learning.
Still refining.
Still improving.

But we’re building the kind of company we always imagined:

A place where autonomy and collaboration coexist.
Where people grow beyond their titles.
Where innovation isn’t just what we deliver, it’s how we work.

Thanks for reading! This post is public so feel free to share it.

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This article unpacks five principles from the ELO concept worth carrying into future projects, plus one idea we think is missing.

5 Ideas Worth Keeping for Innovation

Five principles emerge from the concept that are worth keeping for any team working on next-generation vehicles. Here’s an overview, and more details below.

Design from moments, not features

ELO frames the car as a tool that adapts to different moments across a day or week.

• Work means a rotating driver seat and fold-out desk turning the front zone into a mobile workstation.

• Play means extractable seating that becomes outdoor furniture, with the cabin behaving like a basecamp for cycling, paddling, or picnics.

• Rest means two inflatable dropstitch mattresses stored in the trunk, plus a projector and integrated screen for a sleep-and-chill setup.

This mode logic drives the interior architecture. Extractable seats, flat surfaces, and clear storage zones make reconfiguration fast and intuitive. The cabin behaves like a kit rather than a fixed layout. Product teams need scenario maps alongside feature roadmaps: designing from use cases backward rather than stacking specifications forward.

Treat parked time as prime time

ELO treats stationary time as valuable. Desk mode, cinema mode, and basecamp mode all assume the car is used while stopped. Lighting, ergonomics, storage staging, and interior calm become strategic concerns rather than afterthoughts.

Access reinforces this flexibility. Opposing doors and a pillar-free side opening create a wide portal into the cabin (around 1.9 m), changing how passengers enter, how gear gets loaded, and how the space feels when parked. The radical access architecture is driven by real loading needs and inclusivity, not styling.

ELO uses EV packaging freedom to maximize volume in a short length: wheels pushed outward and a cabin-forward posture support a monovolume-style interior that aims for urban manageability without sacrificing capacity. The space works as well at rest as it does in motion.

Turn the battery into infrastructure

The utility layer goes beyond propulsion. V2L powers tools, cooking gear, and e-bikes, while integrated compressed air inflates paddles, tires, and mattresses. Together, they shift the EV battery from a drivetrain component to infrastructure for daily life.

This points to what might be called Vehicle-to-Activity utility. Power and compressed air turn the vehicle into a support system for hobbies and professional tasks. OEM differentiation can come from utilities that make activities easier, and accessory ecosystems become more legitimate as a result. Utilities beyond charging (air, power, lighting, mounting systems) may become standardized features rather than novelties.

Let materials tell the truth

Surfaces are designed for hard use: exterior protection in recyclable expanded polypropylene, interior felts derived from recycled scraps, and finishes that resist water rather than fingerprints. Sustainability here means durability, replaceability, and materials that look like what they are.

Replaceable protective parts and visibly rugged materials move away from fragile “luxury” finishes. Sustainability becomes tangible through product decisions rather than marketing claims. Material honesty and repairability become brand signals. Modular interiors start to behave like product architecture, with components following their own lifecycle of replacement and upgrades.

The interface follows the same logic. ELO reduces dashboard complexity by projecting information onto a transparent film near the windshield, with compact joystick controls on the steering wheel. Projection and reduced screen dominance point to calmer, cheaper, less distracting HMI.

Bring partners inside the product

The partnerships read as functional contributions rather than branding exercises. Decathlon brings outdoor-use logic and activity-led design cues. Goodyear brings smart tire concepts with visible wear and pressure feedback. Citroën brings EV platform thinking and the willingness to treat the cabin as a living space.

Decathlon and Goodyear are not decoration; their contributions shape user value directly. Retail, sports, materials, and consumer goods become serious sources of automotive innovation. Smart components communicating status visually (not only through apps) represent a shift in how suppliers can contribute to user experience.

What’s missing: design for the mess

A cabin built for outdoor life will encounter sand, mud, wet gear, and dogs. Rugged materials are a start, but true adventure-readiness requires thinking through the cleaning cycle. Sloped floors with drainage channels, removable panels for deep cleaning, an air blowing system to clear debris from seams, sealed compartments to protect electronics from splashes, a dedicated dirty zone near the tailgate. If the vehicle is truly a basecamp, it needs to handle the return from adventure as gracefully as the departure.

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Hardware commoditization

What was once ŌURA’s domain is now a crowded race, the technical barrier to entry has collapsed. Our manufacturing partners in China confirm what the market is showing: smart rings have become commodity hardware. Factories are mass-producing rings with nearly identical sensor configurations (eg. PPG for heart rate, temperature sensors, accelerometers, Bluetooth chips).

This commoditization creates a fundamental challenge for market leaders. When the hardware becomes undifferentiated, competition shifts to software, ecosystems, and brand positioning. Samsung entered with the Galaxy Ring. Ultrahuman is aggressively pushing a no-subscription model. RingConn offers detailed metrics at lower prices. Even experimental players like Lumia are testing whether earrings can deliver similar biometric value without the downsides of wearing a ring during sports.

ŌURA still leads, but its premium pricing strategy now requires constant justification.

ŌURA leading the race

ŌURA’s response has been aggressive. First in expansion, with the company more than doubling its unit sales and active subscribers this year. To fuel this growth, Oura secured a massive $900 million funding round in October 2025. Second, in legal battles around patent infringements2.

These numbers demonstrate that despite increasing competition, ŌURA maintains market leadership, but new entrants are taking bites from different angles.

Strategically, ŌURA now focuses on women in their early twenties. This demographic focus makes sense: a group more willing to invest in preventive health tracking, women-specific features, comfortable with subscription models, and likely to value discrete wearables over bulky smartwatches. But it also highlights the growth challenge: ŌURA needs to continuously find and convert new user segments to sustain its trajectory.

Filling the gaps

ŌURA released features that respond to criticisms we raised in our original test: actionable coaching, not just metrics. Most notably, ŌURA Advisor, an AI-powered health assistant that addresses the “now what?” problem. Instead of just stating “you slept poorly,” it contextualizes: “You’ve had three nights of poor sleep this week, which typically happens when your bedtime shifts. Try going to bed 30 minutes earlier tonight.” Users can customize interaction frequency and tone, avoiding notification spam.

The second addition is Health Panels, a blood testing service via Quest Diagnostics. For $99, members can order comprehensive lab tests covering 50 biomarkers that integrate directly into the ŌURA app. This bridges consumer wearables and clinical healthcare, acknowledging that ring sensors can only measure certain things. By partnering rather than building proprietary hardware, ŌURA creates genuine ecosystem value.

However, accessibility questions emerge. At $400+ for the ring, $6/month subscription, and $99 per blood panel, ŌURA’s complete proposition is expensive. The people who might benefit most from early health intervention are often those least able to afford it.

These additions justify the subscription cost better than Gen 4 did at launch. But the question remains: how many users will sustain this engagement beyond initial novelty?

Competition heats up

Competition is reshaping around four key dimensions:

Ecosystem building: Rings can’t measure everything. Winners will partner strategically rather than cramming more sensors into one device. ŌURA’s Quest partnership and Ultrahuman’s multi-device approach (ring, CGM, blood testing) demonstrate this. No single form factor serves all use cases.

Price justification: As sensors commoditize, price pressure intensifies. The market is splitting between premium positioning justified by software versus affordable hardware for basic tracking. ŌURA must prove their AI coaching, blood testing integration, and brand warrant the premium.

Feature focus: ŌURA’s demographic targeting shows smart strategic thinking. Rather than being everything to everyone, nail specific use cases. Period tracking, fertility insights, and style options create differentiation that matters more than marginal sensor improvements.

Form factor experimentation: The market hasn’t converged on rings as the only answer. Lumia’s earring demonstrates that similar biometric tracking can happen in different form factors that avoid ring-related limitations (eg. gym, bouldering).

AI hype filtering: we see a lot of gimmick products flooding the market betting on various AI-boosted value propositions, however they’ll need to demonstrate real value and long term usage to stay relevant (eg. Sandbar). See our recent AI wearables analysis on the topic.

Conclusion

Six months later, the smart ring market is maturing, technology keeps improving, but key questions ahead:

• Will users sustain engagement once the novelty fades, or will smart rings join the graveyard of abandoned fitness trackers?

• As platforms collect more intimate health data across devices and lab tests, who actually controls and benefits from this information when companies get acquired or fail?

• Can premium players like ŌURA maintain differentiation when hardware commoditizes, or will “good enough” become the market standard?

Industry is optimizing for more tracking, more metrics, more AI insights. But nobody’s proven whether more data actually leads to healthier lives or just more expensive dependencies. The winning product won’t be the one with the most features. It’ll be the one that helps people build lasting health habits and then gets out of the way. Thanks for reading and let us know what you think!

At Futurewave, we try to anticipate what the next generation of products will look like. For this, we start from how consumer products are currently built. From our experience, there are originally three core foundations behind today’s consumer tech product development: design, manufacturing, and electronics & firmware.

• Design is about making the product lovable, enhancing the user experience, and finding the correct form factor and materials to create an emotional bond between the product and the user. When that job is done well, some products become part of your daily life without you even noticing. They become some kind of extension of your soul.

• Manufacturing comes into play once the products have been designed and need to be made manufacturable. Electromechanical engineers start from the CADs made by designers in Fusion and dive into the engineering in Solidworks. Here, everything needs to be defined, from the most complex spring mechanism to the smallest bolt and calibration factors. Products come to life at this stage through 3D printing and constant back-and-forth with specialized suppliers and OEMs for future industrialization.

• Electronics & firmware act as the brains of tech products. Products need to be programmed to act, which means creating custom-made PCBs with the right choice of components and the best matching microcontroller, finding the right balance between availability, costs, and performance. This is also the step where products become interactive with their environment. By connecting the product’s firmware to a back-end architecture, data can be transferred online via WiFi or LTE-M to interact with the products. Alternatively, recent advances in Bluetooth technology, especially BLE, allow products to exchange data seamlessly.

This might already sound quite state-of-the-art. It’s the wonderful intersection between IoT and product design, mixing function and emotion, making the world a more beautiful place by using technology in the right way.

Nevertheless, we like to think ahead at Futurewave. What if there was a novel spike for hardware consumer tech products? Something that would bridge the gap between the product and human emotions, where that extension of your soul we mentioned earlier actually comes to life. We strongly believe that we’re at the edge of adding a new foundation to product development: Intelligence.

The Fourth Foundation: Intelligence

According to the Cambridge Dictionary, intelligence means “the ability to learn, understand, and make judgments or have opinions that are based on reason.”1 As such, equipping products with intelligence would allow them to, for example, learn new tasks, understand context, and proactively take successive actions based on reasoning. These behaviors, however (almost) unseen in consumer tech products, are inspired by the robotics world, where AI has always been part of the embedded systems.

Intelligence in consumer tech products is on the start line of becoming a commodity in the coming years. With the rapid advances in AI research, from GenAI to reinforcement learning and beyond, embedding intelligence into products might soon become a norm. This intelligence will manifest far beyond simple chatbot interfaces, extending into rich multimodal interactions that seamlessly blend voice, gesture, visual cues, and environmental awareness.

Intelligence creates a bidirectional understanding that transforms the entire product experience.

1. From one direction, it allows the user to better understand the product: what it thinks, which state it’s in, what it can do. This creates opportunities for more sustainable product use. Instead of throwing products away when a single feature stops working, the product will be able to indicate what is malfunctioning, why this is happening, and how to solve it. This aligns with our Design For Repairability DNA at Futurewave and supports the recent EU directive on repair of goods2.

2. From the other direction, intelligence allows the product to better understand the user through more personalized, adaptive interactions that grow with the user. You can create an emotional bond with your intelligent product, which makes it evolve from isolation to companionship. These interactions can take many forms: vocal, gestural, visual, touch, smell, and more, enabled by the rich mix of sensing technologies available today. We believe the challenge lies in achieving human-like interactions without anthropomorphism. Apple’s ELEGNT study3 explores this interesting topic around expressive, functional movement.

The true power of intelligent products lies in their ability to move beyond rigid, predefined user flows toward generative, adaptive experiences. Rather than navigating through complex menu systems, users will interact with products through intent-based communication – simply expressing what they want to achieve and trusting the product to understand and deliver. This “Do What I Mean”4 approach transforms the relationship between user and product, making technology feel more intuitive and less mechanical.

The Promise and the Peril

This paradigm opens up fascinating possibilities for products that are intentionally unfinished by design – evolving organically with user needs and behaviors over time. Rather than launching with a fixed feature set, intelligent products can continuously adapt, learn, and expand their capabilities based on real-world usage patterns and emerging user requirements.

However, this shift demands that users remain firmly in control. Intelligent products must provide clear, accessible ways for users to adjust, override, or refine AI-generated outputs. The goal is to be proactive without becoming intrusive – anticipating needs while respecting boundaries and preferences.

With these opportunities come significant responsibilities.

• Cognition might shift away from humans to products, especially for younger generations. Outsourcing some tasks to devices and machines makes sense, but not at the expense of human cognition, which is our biggest asset to differentiate ourselves from other living species on Earth. If we are surrounded by intelligent products in our daily lives, we might start relying on them too much, until reaching a point of non return.

• User need is equally important. Making products think by embedding AI should only be done if there is a genuine user need that delivers real, long-term value rather than mere novelty. This might either be a current need, responding to a market in demand, or an anticipated need. The latter would mean that people maybe don’t realize yet that integrating intelligence into some products might actually change living habits for the better (we hope).

• Trust and safety cannot be overlooked. As products become more autonomous and capable of independent decision-making, establishing robust guardrails becomes essential. Users need to trust that their intelligent products will behave predictably and safely, while having clear visibility into how decisions are made and maintained control over critical functions.

Making Intelligence Real

The transition from software-based AI applications to physical, embedded intelligent products requires hands-on experimentation rather than theoretical planning alone. The complexities of integrating advanced language models and AI capabilities into hardware demand iterative prototyping and real-world testing.

At the core, the Human-Product Interaction (HPI) can be augmented by intelligence, either coming from the Cloud or from the Edge. The Cloud allows for more computing power and access to more resources and applications, but requires internet connection and might become very expensive, depending on the number of AI tokens used. On the other hand, having the intelligence run on the Edge (on the device itself) requires less resources, is (almost) free to use after the hardware has been purchased, but is limited in computing power for very small processing units. The Nvidia Jetson5 family is often considered one of the strongest options in the context of Edge AI compute, but its volume is not discreet.

When leveraging Intelligence in the Cloud, the product connects to a comprehensive backend infrastructure that handles request and response orchestration. This cloud pathway links the product to databases and storage systems, AI compute servers for processing-intensive tasks, networks that enable communication with other devices, and application servers that manage the overall system logic.

Alternatively, Intelligence at the Edge runs directly on the device itself. Through the product’s operating system, the firmware can execute inference tasks using local AI models and access local storage resources like flash memory and RAM. This enables immediate processing without requiring internet connectivity.

The optimal approach often lies in a hybrid model that combines both pathways. Lightweight tasks can be processed locally on the device for immediate response, while more compute-intensive operations are delegated to the cloud when connectivity and budget allow. This flexible architecture enables intelligent products to deliver responsive, personalized experiences while efficiently managing computational resources and maintaining functionality even when connectivity is limited.

Conclusion

The future of consumer tech products isn’t just about better hardware or sleeker designs. It’s about adding intelligence as a fourth foundation alongside design, manufacturing, and electronics & firmware. This intelligence creates a two-way conversation between users and products, where both sides develop deeper understanding and more meaningful interactions.

The technology is ready. We can deploy intelligence through cloud computing for heavy processing or directly on devices for immediate responses, or blend both approaches for optimal performance. The real challenge lies in implementation: building products that are genuinely helpful without becoming intrusive, that enhance human capabilities without replacing them, and that evolve with users while keeping them in control.

At Futurewave, we see intelligence not as a flashy add-on, but as the natural next step in product evolution. The products that will define the next decade won’t just be smart, they’ll be truly intelligent companions that understand context, anticipate needs, and grow alongside the people who use them.

The question isn’t whether intelligent products will emerge, it’s whether we’ll design them thoughtfully. The work starts now, with prototypes, experiments, and real-world testing. Because the best way to understand the future of intelligent products is to build them.

The AI wearables market is experiencing a gold rush moment with pins, pendants, glasses, and even shoes flooding the market. But beneath the hype lies a fundamental question: are these products solving real problems, or are they innovations in search of users? We’re clearly in Utterback’s “Fluid phase” with wild experimentation and no dominant design yet. But there’s a path forward: the winners will be those that solve specific problems well, respect privacy boundaries, and fit naturally into daily life rather than trying to do everything.

Testing the hype

The tech industry is excited about a new category: AI-backed wearable hardware devices promising to revolutionize how we interact with technology. Pins that record everything you say, glasses that translate in real-time, pendants that summarize your meetings. The promises are bold, the designs are varied, and the marketing is everywhere.

At Futurewave, we like to crash test new products. When the Rabbit R1 emerged as one of the pioneers in this space, generating significant buzz and positioned as a breakthrough AI companion device, we thought it deserved a proper evaluation. The verdict? It looks nice, it’s fun to test, but eventually it feels more like a novelty than a necessity.

But this test opened a much broader discussion. The Rabbit R1 isn’t an outlier, it’s representative of an entire wave of AI wearables entering the market, each claiming to be the device that will finally free us from our smartphones, boost our productivity, or augment our capabilities in meaningful ways.

So far, most of these devices struggle to demonstrate clear advantages over existing solutions and that’s the real problem this market needs to solve.

Who’s Building What?

Every company is betting on a different vision of what AI hardware should be. Just look at the explosion of different designs: pins, pendants, glasses, rings, handheld devices, even shoes. This thanks to miniaturization of size (hardware, sensors, chips), increase in computing power and democratization of AI models. It opened opportunities to integrate high tech in tiny objects, plus more freedom in design and aesthetics beyond functionality.

We’ve mapped out players in a matrix to compare them based on their use case (specific vs. generic) and philosophy (tech vs. lifestyle) :

• “Quiet optimizers”: they solve ONE specific problem well (health, fitness, workplace), have a clear value proposition, are discrete and require minimal behavior change.

• “Tech pioneers”: they lead with technology and attempt to be everything for everyone. This area seems to fail by building a solution and then searching for problems it can solve.

• “Specialist tools”: they lead with technology and solve a specific problem, however they need to demonstrate real value for people to accept them.

• “Fashion generalists”: they hide features inside something people already desire for non-tech reasons, softening the barrier to adoption but questioning end-user benefit.

  

This fragmentation reveals the core challenge: without a clear understanding of what users actually need, companies are trying different approaches across various form factors and use cases. Academic research validates these concerns, approximately 30% of wearable device owners abandon their devices within six months, with abandonment rates climbing as high as 50% within just two weeks for some devices1. The reason? Most AI wearable companies are betting on the market rather than understanding users to their core. It’s not a technical problem, it’s a behavioral one.

What’s going to emerge?

Referring to Utterback’s innovation model2, it’s clear: we are in the “Fluid phase” of AI wearables, meaning the market is characterized by:

• Radical product innovation: Companies experimenting with wildly different approaches

• No dominant design: No clear standard has emerged

• High failure rate: Multiple examples of failures or mixed success

• Market confusion: Consumers don’t know what to buy or why they need it

So what’s next? Is this market going to evolve past the fluid phase into an established product category or is it a temporary trend?

Taking example from smartphones, manufacturers initially introduced a variety of designs (flip phones, candy bar styles, sliders, etc.), operating systems (Symbian, Windows Mobile, Palm OS), input methods (physical keyboards, stylus, touchscreens), and hardware innovations.

Then the iPhone emerged, with its touchscreen, minimalist hardware, and App Store, as the solution that best matched user needs and became the blueprint that most competitors followed, setting the dominant design and the entry in the transitional phase.

Conclusion

AI wearables will likely reach the “Transitional phase” through the following:

1. Core user needs satisfaction: be good at one specific thing, so users understand the benefit, instead of being poorly-defined “do-everything” product. Form factor and lifestyle integration matter more than raw AI capabilities.

2. Elegantly blend in daily life: the more you go for discrete high technology and sensor miniaturization, the easier user accept to wear it and the better the comfort and data fidelity.

3. Avoid latency and ensure privacy by moving compute from cloud to local nodes (eg. mobile phones, private trustworthy servers), or by moving compute from cloud to edge (long shot for wearables). Always-on recording and ambient listening face major social acceptance barriers. Regulatory frameworks (GDPR, FDA, AI Act) and user expectations demand privacy-by-design approaches.

4. Think in terms of ecosystem: envision the wearable as a node in a network, working together with others for a better user experience, not as a stand-alone.

The successful products won’t be the most technologically impressive, they’ll be the ones that understand human behavior, respect privacy boundaries, and fit naturally into existing routines.

Otherwise… these ideas won’t go further and we’ll find better use of artificial intelligence in products than dedicated companions.

As we watch this market evolve, let us know what you think. What AI tool is actually missing? What would make you wear technology on your body every day?

Author: Jehan Coppé

Companies around the world are under pressure to deliver innovation that is fast, customer-focused, and market-ready. Yet many projects stall at the idea stage, struggle to align stakeholders, or fail to generate real business impact. Futurewave’s new Product Innovation practice is designed to change this. By combining deep industry expertise, multidisciplinary execution, and proprietary AI-driven tools, we help organizations transform opportunities into validated product concepts within weeks, not months. This allows corporate innovation leaders to remain dedicated to their ongoing initiatives, while Futurewave operates alongside them to de-risk and fast-track disruptive ideas.

Our mission

Futurewave accelerates breakthrough innovation through rapid validation and pragmatic creativity. Every engagement begins with a deep understanding of the client’s market, users, and competition. This ensures that our recommendations are not only visionary but also strategically grounded. We exist to bridge the gap between big ideas and tangible products, helping companies create measurable competitive advantage.

Addressing innovation challenges

Across industries, organizations face recurring obstacles: balancing daily operations with long-term exploration, navigating slow decision-making cycles, and investing in initiatives that fail to reach customers. Too often, innovation becomes detached from strategy, producing features that look good in presentations but fail to create business value. Futurewave’s Product Innovation practice was built to solve these challenges. We developed structured methodologies to separate signal from noise, validate opportunities before major commitments, and provide access to expertise that accelerates results. The outcome is innovation that is faster, clearer, and more impactful.

Our services

Futurewave’s Product Innovation practice offers a modular journey from insight to execution. While the steps build on each other as a natural progression, organizations can also engage in selected phases based on their immediate priorities. The Opportunity Radar reveals new directions in just three days by combining trend analysis with expert-led ideation enhanced by AI tools — leveraging technology to amplify human creativity while keeping human insight at the core. The Immersion phase delivers deep market intelligence in two weeks, creating a foundation for confident decisions. With Product Strategy, we help leaders resolve critical questions about launching, repositioning, or reimagining products. Through Product Innovation, we turn user needs and future scenarios into structured, validated concepts that balance ambition with feasibility. Finally, Experimental Prototyping brings ideas to life through tangible models and functional prototypes, enabling rapid iteration and refinement before large-scale investment.

Who we serve

The Product Innovation practice can fuel creativity and impact for global companies in any industry, with a proven track record in technology, healthcare, industrial design, mobility, and automotive. It serves any organization seeking to turn uncertainty into opportunity, stay ahead of competitors, and secure long-term relevance in fast-changing markets. Whether the goal is to open new markets, redefine a product category, or embed a culture of consistent innovation, Futurewave provides the expertise, clarity, and execution power to make it happen.

Conclusion

Innovation is no longer optional. It is essential for survival and growth. With the launch of its Innovation practice, Futurewave helps companies worldwide shorten time-to-market, de-risk investments, and turn ambitious ideas into validated solutions.

Authors: Alban Broze & Jehan Coppé

Learn more about product innovation

“We can see that physical speed, of movement, freezes you. You are inert, delateralized. And there’s a phenomenon of hypnopie. The faster you go, the further your gaze must project, and you lose lateralization. In other words, you are fascinated.”

Virilio uses a simple image: when you drive fast, you must look far. If you fix on the road just in front of your hood, you crash. Your vision must anticipate, project itself 200 meters, 500 meters ahead.

But.

If you look ONLY far, you miss the immediate dangers. The pedestrian who emerges, the pothole, the truck that brakes. Your peripheral vision must stay active, your attention must be able to switch instantly from distant to near.

As the great theorist of dromology, Paul Virilio was already observing decades ago phenomena that are still present, that have ultimately only amplified, and that today sometimes give the impression that “He who goes faster eats he who reflects.”

We are by definition an “innovation” studio, therefore, we sell acceleration. It’s our job. We imagine, design and develop the products dreamed by our clients. Our starting point often nests in the imaginary and our mission is to create a bridge toward the real in order to give form, to translate these dreams into a language now almost constantly understood through the “marketable” prism.

A client arrives with their dream; or unfortunately more often with a problem, we deliver a prototype in six weeks. Fast iteration, fail fast, optimized time-to-market. All the vocabulary of speed that traps us in a form of fascination Virilio spoke of, that can freeze us in a trajectory at any moment. Tunnel vision.

So how do we avoid crashing?

The question is not rhetorical. It structures our daily practice, and probably that of any studio that refuses to become a simple executor of preformatted briefs, condemned to being high-level service providers who decorate capitalist acceleration with pretty design thinking.

There exists a tension, named, theorized, between two ways of conceiving innovation. Two temporalities. Two types of gaze.

On one side, speculative design. On the other, pragmatic development.

These terms are not fixed, not yet stabilized in a clear theoretical corpus. We borrow them, twist them, adjust them to our workshop reality. What follows is not a doctrine, rather an attempt to name what’s at stake when we try to conceive without being hypnotized by delivery speed alone.

Because in an innovation studio’s practice, this fascination takes this precise form: the obsession with the deliverable. The prototype that pops. The concept that sells. The marketable product by deadline.

Speculative design then allows us to look far, anticipate mutations, question the brief’s evidences. Necessarily, this implies looking 3-5-10 years ahead, at minimum. Scanning trends, cultural mutations, weak signals.

Speculation allows us to tell our clients: “What you’re asking for is coherent with 2023. But 2026 will look like this. Do you want us to design for yesterday or for tomorrow?” The risk however is very real: staying in pure speculation. Magnificent concepts, intellectual provocations, but zero production. A speculative future that never materializes becomes academic, disconnected.

Pragmatic development meanwhile allows us to look close, manage material constraints, deliver what can exist today and coincides with tangible realities. Pragmatic development is the engineer saying: “OK but with what components?” The sales person reminding: “The client has 80k budget, not 300k.” The project manager alerting: “If we add this layer, we exceed by 6 weeks.”

These constraints are obviously not always brakes, they’re also guardrails.

Without them, we’d produce splendid concepts that don’t materialize, that cost too much, that arrive too late, or that serve no one.

Pragmatism forces incarnation. It obliges speculation to descend from the sky of ideas to negotiate with matter: available electronic components, stable software frameworks, real budgets, contractual deadlines, the team’s skills.

However, contrary to speculative design, the risk here is killing all ambition. Finding ourselves delivering “what’s possible” rather than “what’s necessary.” Optimizing the existing instead of proposing the unprecedented. Becoming an elegant technical service provider but conceptually dead.

The balance is delicate: one must not choose between speculation and pragmatism. One must hold them together in tension.

Our strategic process follows this logic:

Assumed speculation → Material confrontation → Incarnated synthesis.

We build concepts that keep speculation’s ambition but accept pragmatism’s constraints.

• Should we give the client what they ask for (pragmatic) or what they need without knowing it yet (speculative)?

• Should we maximize the team’s pride in our concepts (speculative) or the serenity of delivery (pragmatic)?

• Should we respond to markets’ current demand (pragmatic) or anticipate emerging demand (speculative)?

• Should we optimize the user’s immediate comfort (pragmatic) or preserve their long-term autonomy and security (speculative)?

There is no stable answer. These balances are renegotiated project by project, week by week, sometimes hour by hour.

And perhaps that’s exactly it, the exit from hypnopie: accepting that there is no fixed trajectory. That each project imposes recalibrating the gaze. That speed is not the problem itself, but the inability to modulate, to slow down when necessary, to deviate when the crash becomes inevitable.

Virilio wasn’t proposing to renounce the car. He was proposing to understand what it does to our perception. To make visible the fascination that freezes us.

He said: ‘To invent the airplane is to invent the plane crash. To invent the ship is to invent the shipwreck.’

Each technology invents its specific accident and that’s the whole dilemma of the modern world.

And today, the accident has become the model.

The structures that dominate technological innovation reward exactly this fascination: move fast and break things, fail fast, disrupt or die. Speed is no longer a means, it has become the end. He who goes faster wins, independently of what he destroys on the sides.

It’s not a question of personalities. It’s a question of system that valorizes acceleration, disruption, rapid value extraction. It produces actors who optimize for speed because that’s what guarantees them survival and domination.

We refuse this logic.

Not because we would be more virtuous. But because we see the crashes around us. Projects that destroy their users’ attention to optimize engagement. Devices that capture data without real consent. Innovations that accelerate work to exhaustion.

We don’t claim to have a miracle solution. We don’t say “technology in service of humans” as if it were an evidence sufficient to proclaim.

We say: there exists a tension between speed and consciousness. And it is possible to work this tension rather than deny it. To deliver without being hypnotized.

It’s difficult. It’s unstable. It doesn’t resolve anything definitively.

But at least, it refuses the fascination. It keeps open the possibility to say no.

To slow down.

To deviate before the crash.

.

Building for Speed and Structure

At Futurewave, our design and engineering process moves fast, from sketch to prototype, from idea to PCB. That speed demands efficiency and precision at every step. Over time, we realized that traditional Altium component management tools slowed us down, trapped in GUIs and inconsistent data handling. To solve this, we built our own code-based, GitHub-hosted component library system, designed to scale effortlessly across projects. Today, we’re sharing it with the world.

From Chaos to Clarity

Every hardware engineer knows the frustration of chaotic libraries: duplicate footprints, lost datasheets, inconsistent naming, and the endless need to verify what should already be verified. In a studio like ours, where projects shift daily between consumer electronics, wearables, and IoT devices, that chaos multiplies. We needed something light, shareable, and automated. The result was a fully scriptable, JSON-driven structure that transforms library management into a clean, version-controlled process. Each component exists as a simple text file — transparent, readable, and traceable — eliminating the clutter of legacy tools.

A Proven and Scalable Foundation

Our library already contains over 250 verified components that we use daily: connectors, microcontrollers, sensors, and discrete parts. More than 5,000 devices have been built and shipped using this system, proving its reliability and production readiness. While it’s still growing, its foundation is strong. Simple enough for small teams, yet structured for complex product lines. It isn’t just a repository of parts; it’s a framework for efficient collaboration. Anyone can understand it, contribute to it, and adapt it to their own workflows without proprietary lock-ins or steep learning curves.

Why We Opened It

We believe good tools should be shared. Our Altium library system is not a reinvention, but it’s a refinement of what already works, stripped of unnecessary layers and dependencies. By releasing it as open source, we want to empower small teams and independent engineers to maintain professional-grade libraries without relying on costly PLM systems. It’s an invitation to the community to build better together, using transparent and standardized component definitions. For us, open sourcing is not just about sharing code; it’s about strengthening the ecosystem that enables innovation in hardware design.

What Comes Next

Futurewave continues to evolve this system with features such as automated validation tools, continuous integration for component updates, and easier imports from suppliers like LCSC. The goal is to make the entire process of creating, verifying, and sharing components seamless. As we improve it, we invite the community to contribute ideas, fixes, and components. Ensuring it remains fast, adaptable, and developer-centric. Futurewave’s philosophy has always been to create value, not just products. With this open-source library, that value extends beyond our studio, to every engineer who believes in building smarter, faster, and together.

Check out our open source Altium library

Author: Wouter Van Rompaey