Consumer-tech brands are adding AI to devices at speed. Smart TVs, appliances, wearables, phones, speakers, cameras, robots, mobility devices and connected-home products are all becoming more intelligent, adaptive, conversational, predictive or personalised. The claim is easy to understand at headline level: the product is smarter. It learns. It recommends. It automates. It adapts. It saves time. It improves the experience.

But AI inside the device should not be treated only as a feature layer. For many categories, it may become a business-model question.

If the intelligence improves after purchase, learns from usage, supports services, reduces friction, enables remote updates, predicts maintenance, personalises outcomes or creates ongoing value, then the commercial model around the device must change. Pricing, support, lifecycle engagement, retail explanation, service tiers, data trust, portfolio roles and monetisation logic all become part of the AI decision.

The strategic question is not only: does this product have AI?

The better question is: what does AI allow us to sell, support, learn or monetise after the product is sold?

The first wave of AI in consumer devices is often communicated through features. Better picture optimisation. Smarter cleaning routes. Predictive maintenance. Energy optimisation. Voice control. Personal recommendations. Adaptive cooking. Health insights. Automated routines. Security detection. Usage-based settings. Personalised sound. Context-aware assistance.

These are valuable. They can improve the product, create differentiation and justify premium positioning. But feature-level AI has a limit. If every brand claims smarter, adaptive, predictive or personalised, the market soon stops seeing intelligence as distinctive. AI becomes another specification, another badge, another line on the product page.

That is where many brands will struggle. They will add intelligence to the product without redesigning the commercial logic around it.

AI as a feature asks: what does the device do better?

AI as a business model asks: what changes because the device is now intelligent, connected and capable of improving over time?

I help consumer-tech, appliance and connected-device leaders translate AI from product claims into commercial strategy. The work is not only about naming AI features. It is about clarifying what AI changes in value proposition, portfolio role, pricing logic, service model, retail activation, customer trust and lifecycle monetisation.

Using outside-in scans, portfolio clarity work, GTM crash tests and AI-augmented commercial workflows, I help leaders identify where AI creates real business leverage, where it is only a badge, and what must change in the GTM system to turn intelligence into commercial momentum.

fredericmartin.eu

AI inside the device is not only a product decision. It is a business-model decision. When intelligence creates ongoing value after the sale, brands must rethink the surrounding commercial architecture: pricing, service tiers, support, updates, data trust, lifecycle engagement, retail explanation, portfolio roles and monetisation. The device may still be sold as hardware, but the value increasingly sits in what the device can learn, improve, recommend, automate, diagnose or connect over time. The winning brands will not simply add AI features. They will design clearer AI-enabled value models: what remains transactional, what becomes service-based, what strengthens loyalty, what improves margin, what creates data-enabled support, and what gives customers a better reason to stay.

In traditional consumer tech, the device has often been treated as the main unit of value. The product is designed, manufactured, launched, distributed, sold, supported and eventually replaced. The business model is largely shaped by hardware margin, channel economics, promotional cycles, accessories, warranty, service costs and replacement demand.

Connected devices already changed this logic. Software updates, apps, ecosystems, cloud services and connected experiences extended the relationship after purchase. AI pushes the shift further because the device can become more adaptive, more contextual and more dependent on ongoing intelligence.

That matters commercially. A device that learns, updates, predicts, recommends or personalises may create value long after the initial sale. If the value continues, the business model should not remain fully trapped in the launch transaction.

This does not mean every AI device should become a subscription. That would be a lazy conclusion. But it does mean leaders should ask whether the value architecture has changed. What is included in the device price? What should remain free? What could become a premium service? What improves retention? What reduces service cost? What strengthens the ecosystem? What creates recurring engagement? What data is needed? What trust promise is required?

The device may still be the object the customer buys. But the business may increasingly depend on what happens after the device enters the home.

Product specifications describe what a device can do at the moment of purchase. AI changes the conversation because part of the value may unfold through usage.

A cleaning robot may improve routing after learning the home. A washing machine may optimise cycles based on load, fabric or energy usage. A TV may adapt picture and content recommendations over time. A wearable may become more valuable as it understands patterns. A coffee machine may personalise routines. A smart thermostat may improve comfort and efficiency through behaviour. A security camera may reduce false alerts through better recognition.

The strategic issue is not only whether the AI works. It is whether the customer understands the value over time.

If the value develops after purchase, the GTM story cannot stop at launch features. The brand needs a lifecycle value story: what gets better, why it matters, how customers will notice it, what proof supports it and what expectations should be set.

This is especially important for premium positioning. Customers may accept a higher price if the device creates ongoing value. But the brand must make that value tangible, not abstract.

AI can make a feature better. It can also create the basis for a service.

The difference matters. A feature is usually sold with the product. A service requires design choices around access, support, pricing, updates, customer communication, data, reliability and long-term value. It also creates new expectations. If the device promises intelligence, the customer expects the intelligence to work, improve and remain trustworthy.

Service architecture asks: what support surrounds the AI-enabled device?

Does the customer receive personalised recommendations? Remote diagnostics? Predictive maintenance? Software upgrades? Performance optimisation? Energy reports? Security alerts? Consumable replenishment? Coaching? Device health monitoring? Premium assistance? Extended care? Integration with other devices? For B2B or professional segments, could aggregated insights help operators manage fleets, usage, maintenance or performance?

Not all of this should be monetised directly. Some service value protects the core product experience. Some reduces cost. Some increases loyalty. Some supports premium tiers. Some creates ecosystem stickiness. Some becomes part of the brand promise.

AI inside the device creates a service question even when the company still sells hardware.

Consumer-tech economics have often depended on sell-in, sell-out, margin, promotion, channel mix and replacement cycles. AI-enabled devices add a lifecycle layer. The company can potentially earn, save or retain value after the initial transaction.

The sources of value may differ by category. There may be subscription tiers, premium software features, consumables, extended warranty, predictive maintenance, energy optimisation services, cloud storage, security monitoring, personalisation, data-enabled support, remote diagnostics, upgrade paths or ecosystem bundles. There may also be indirect economics: lower service costs, fewer returns, better customer satisfaction, higher repeat purchase, improved recommendation rates and stronger ecosystem attachment.

The commercial danger is to jump too quickly to monetisation. Customers do not pay for AI because it is AI. They pay when the ongoing value is clear, useful, trusted and worth the price.

The leadership question is: where does AI create recurring value strong enough to change the economics?

If the answer is weak, AI should remain part of the product experience. If the answer is strong, the company may need a different pricing, service and retention model.

A traditional launch concentrates effort before and around the release. Product assets, press materials, retail packs, campaigns, sales enablement and promotions are prepared to create initial attention. But an AI-enabled device may need a stronger post-purchase relationship.

The customer may need onboarding. The AI may need explanation. Settings may need configuration. Updates may change functionality. Insights may require interpretation. Recommendations may need trust. Service prompts may need timing. Data permissions may need clarity. New capabilities may arrive later. The experience may improve only if the customer stays engaged.

This changes GTM. The launch is no longer the whole story. The relationship becomes part of the model.

For consumer-tech leaders, this creates a new question: how do we design the first 90 days after purchase?

That period may determine whether AI feels valuable or invisible, helpful or intrusive, premium or confusing. It may also determine whether customers accept future services, trust recommendations, activate features and remain in the ecosystem.

AI inside the device requires not only product marketing. It requires relationship design.

Retailers and sales teams cannot sell algorithms. They need outcomes.

This is a major GTM challenge. Many AI features are hard to explain at the point of purchase. “AI-powered” is not enough. Customers want to know what improves. Retailers want to know what to demonstrate. Sales advisers need simple comparison logic. Product pages need proof. Reviews need to confirm the claim.

The better retail story is not: this device has AI.

It is: this device saves time, reduces effort, improves performance, lowers energy use, prevents problems, adapts to your habits, makes better recommendations or helps you get a better result.

The proof must be practical. Before and after. Fewer steps. Better results. Lower waste. Faster setup. More accurate detection. Less manual adjustment. More relevant recommendations. Better maintenance. Clearer alerts. Stronger personalisation.

AI becomes commercially meaningful only when it is translated into the customer’s outcome and the retailer’s selling story.

Not every product should carry the same AI logic. This is where portfolio thinking becomes critical.

Some products may use AI as a premium differentiator. Some may use it as a convenience layer. Some may use it to strengthen ecosystem value. Some may use it to reduce service cost. Some may remain intentionally simple because simplicity is the selling point. Some may act as AI-enabled hero products that lift the category story. Others may not justify the added complexity, cost or trust burden.

The portfolio question is: which products deserve intelligence, and why?

If AI is added too broadly, the range may become harder to understand. If it is added only to premium products, the brand must justify the step-up. If it is added inconsistently, the portfolio logic may weaken. If basic products remain non-AI, the brand needs to explain why that is a better choice for some customers.

AI should clarify product roles, not blur them.

The strongest portfolios will not be those where every device says “AI.” They will be those where intelligence has a clear commercial job.

AI-enabled devices often require data. Usage patterns, preferences, environments, images, voice inputs, health indicators, location, performance logs, device behaviour, service signals or household routines may all become part of the value system.

That makes trust part of the business model.

Customers will ask, explicitly or implicitly: what is collected, why is it needed, what improves because of it, who has access, how is it protected, can I control it, and what happens if I opt out? Retailers may also need answers. Regulators may require clarity. Product teams may need constraints. Marketing may need more disciplined claims.

Trust is not only a compliance issue. It is a commercial issue. If customers do not trust the intelligence, they will underuse it. If they underuse it, the value is weaker. If the value is weaker, monetisation becomes harder. If monetisation is harder, AI remains a cost and a claim rather than a business model.

The trust model must be designed with the value model. The more ongoing and personal the AI value becomes, the stronger the trust architecture must be.

Service is often seen as a cost centre in consumer tech. AI can change that. Devices that detect performance issues, predict maintenance needs, guide customers through fixes, recommend consumables, trigger updates or help support teams diagnose problems can reduce friction and improve the customer relationship.

This is not glamorous, but it can be commercially powerful.

A product that prevents problems may reduce returns. A device that explains itself may reduce support calls. Predictive maintenance may protect satisfaction. Better diagnostics may improve repair efficiency. Usage insights may inform product development. Service data may reveal where customers struggle, what features are ignored and which promises fail in reality.

AI inside the device can turn support from reactive cost into a learning system.

This matters because the business model is not only about revenue expansion. It is also about margin protection, lower friction, better retention and faster product improvement.

Leaders should not ask only whether AI improves the device. They should ask whether AI changes the business model around the device.

The test has seven questions.

Does AI create value after the sale? Does that value improve over time? Does the customer understand and trust it? Does it justify a premium, service tier or ecosystem relationship? Does it reduce support cost or increase retention? Does it change the role of the product in the portfolio? Does it require a different retail, pricing, service or data model?

If the answer is yes, AI is not just a feature. It is part of the business architecture.

If the answer is no, AI may still be useful, but it should be positioned honestly as a product improvement rather than inflated into a strategic business model.

AI inside the device is becoming one of the defining shifts in consumer tech. But the winning question will not be who adds the most AI claims. It will be who turns intelligence into clearer value, stronger relationships and better business economics.

A device can be smarter without creating a smarter business. That is the risk.

The stronger opportunity is to treat AI as a business-model design question. What value continues after purchase? What service can be built around it? What can be monetised? What should remain included? What improves loyalty? What reduces friction? What strengthens the portfolio? What helps retailers sell? What does the customer need to trust? What data enables the value? What operating capabilities does the company need?

AI inside the device changes the product.

But if leaders ask the right questions, it can also change the business.

Take one AI-enabled product in your portfolio and map the value beyond the sale. What does the device learn, improve, predict, personalise, recommend, automate or diagnose after purchase? Which of those benefits are visible to the customer? Which could support a service layer? Which reduce cost or friction? Which require data trust? Which justify a premium? Which strengthen the portfolio role?

Then separate the feature from the model.

If AI only improves a function, market it clearly. If AI creates ongoing value, design the business model around it.

Short CTA: before adding another AI badge to the device, decide what AI changes in value, service, trust and monetisation.

From The Strategic Brief
AI Transparency Is Becoming a GTM Issue
Where AI Actually Creates GTM Leverage
Stop Managing Products. Start Managing Choice.
Too Many Products, Too Little Choice: The IFA Portfolio Clarity Index
What IFA Reveals About the Future of Consumer Tech Markets
The Commercial Architecture of a Business Designed to Win
The CEO’s New Operating System

External reading
Harvard Business Review, Competing in the Age of AI
Harvard Business Review, The Elements of Value
Harvard Business Review, Know Your Customers’ “Jobs to Be Done”
Harvard Business Review, How Smart, Connected Products Are Transforming Competition
Harvard Business Review, How Smart, Connected Products Are Transforming Companies