AI will be everywhere at IFA. On booths, product pages, press releases, demos, keynotes, packaging, retailer briefings and launch decks. Smart devices will be more adaptive. Appliances will be more predictive. TVs will optimise more automatically. Robots will navigate more intelligently. Wearables will interpret more signals. Speakers will become more conversational. Connected-home systems will promise more personalisation, automation and control.

The risk is that every brand starts to sound the same.

“AI-powered.” “Smarter.” “Personalised.” “Adaptive.” “Intelligent.” “Next-generation.” “Autonomous.” These claims may be technically true, but they are not automatically commercially useful. Customers do not buy AI because it is AI. Retailers do not activate AI because it sounds advanced. Sales teams cannot defend AI unless they can explain what improves. A feature claim becomes valuable only when it translates into an outcome people understand, trust and are willing to choose.

That is why every AI claim needs a value test.

The first problem is that AI has become an easy label. It can be attached to recommendation engines, sensors, image processing, voice interfaces, energy optimisation, predictive maintenance, automation, navigation, personalisation, diagnostics, security detection, content discovery, cooking modes, cleaning paths and health insights.

Some of these uses are meaningful. Some are incremental. Some are difficult to prove. Some are not new at all, but have been renamed in the language of AI. From a brand perspective, the temptation is obvious: if AI creates attention, put AI in the story.

But attention is not the same as commercial traction. A weak AI claim may create curiosity at IFA and still fail at retail, in product pages, in sales conversations or in customer reviews. The market will ask: what does it do, why does it matter, can I see it, can I trust it, and is it worth paying for?

If the answer is unclear, the claim will not survive the value test.

I help consumer-tech, appliance and connected-device leaders translate AI from product language into commercial value. The work is not only about naming AI features. It is about clarifying what AI improves, which customer outcome it supports, what proof makes it credible, how retail can explain it, and whether it strengthens the reason to choose, pay more or stay longer.

AI claims will be abundant at IFA, but only some will be commercially useful. A strong AI claim must pass five tests: it must improve a relevant customer outcome, make that improvement visible or measurable, provide credible proof, be simple enough for retail and sales to explain, and strengthen the reason to choose, pay more, buy again or stay in the ecosystem. Weak AI claims describe the mechanism. Strong AI claims translate intelligence into value. The leadership question is not “do we have AI in the product?” It is “does AI create a clearer, more credible and more defensible reason to choose our brand?”

The AI Value Test is a simple filter for separating feature noise from commercial relevance. It is not a technical audit. It is a commercial test. It asks whether the AI claim can travel from product team to marketing, from marketing to retail, from retail to customer, and from customer experience back into trust, satisfaction and future demand.

A claim that passes the test becomes useful. It can support positioning, pricing, retail activation, product-page hierarchy, sales enablement and service design. A claim that fails the test may still describe a real function, but it should not be overpromoted.

The test has five questions.

The first test is outcome clarity. What becomes better for the customer because AI is inside the device or service?

Not what the algorithm does. Not how advanced the technology is. Not how the engineering team describes it. The customer outcome.

Does the device save time? Reduce effort? Improve performance? Lower energy use? Avoid mistakes? Personalise the experience? Increase comfort? Improve safety? Reduce waste? Predict problems? Make setup easier? Improve quality? Remove friction? Help the customer feel more in control?

Too many AI claims start with the mechanism: detection, optimisation, recommendation, automation, learning. The stronger claim starts with the result: fewer false alerts, better cleaning coverage, lower energy consumption, more relevant content, fewer manual settings, better maintenance timing, more consistent cooking, faster setup, fewer service issues.

The value test begins with a simple sentence: AI helps the customer achieve X better than before.

If that sentence is difficult to write, the claim is not ready.

The second test is evidence. A customer may not need to understand how AI works, but they need to see or feel that something improves. Retailers and sales teams also need proof. Otherwise AI remains a promise.

Some improvements are visible. A cleaning robot maps better. A camera reduces false alerts. A TV adjusts picture quality. A washing machine recommends a better cycle. A coffee machine remembers preferences. A wearable detects a pattern. A speaker responds more naturally.

Other improvements need measurement. Less energy. Fewer support calls. Faster setup. Better battery life. More accurate detection. Lower waste. Higher reliability. Reduced maintenance. Improved conversion. Stronger retention.

The commercial issue is not whether the AI is technically impressive. It is whether the improvement can be demonstrated, explained or evidenced in a way the market believes.

If the benefit is invisible, vague or hard to experience, the brand needs stronger proof. Without proof, the claim becomes decoration.

The third test is credibility. AI claims are becoming easier to doubt because the language is becoming overused. Customers have heard too many “smart” promises. Retailers have seen too many feature badges. Journalists have learned to ask what the AI actually does. Regulators and consumer advocates increasingly care about transparency, data use and unsupported claims.

Credible proof can take many forms: demonstrations, before-and-after comparisons, usage data, certification, controlled tests, customer reviews, expert validation, service metrics, energy results, reliability data or transparent explanations of how the feature works.

But proof must match the claim. A premium AI claim needs premium evidence. A trust-sensitive AI claim needs transparency. A performance claim needs performance proof. A personalisation claim needs clear customer value and data confidence. An energy claim needs measurable savings. A predictive-maintenance claim needs evidence that prediction reduces problems.

The weak pattern is: big AI claim, thin proof.

The strong pattern is: specific AI claim, visible outcome, credible evidence.

At IFA, this matters because brands are not only competing for attention. They are competing for belief.

The fourth test is explainability at the point of decision. If the retailer cannot explain the AI feature, the claim loses power. If the sales adviser needs three minutes of technical language to make the benefit clear, the claim will not scale. If the product page hides the value inside specifications, customers will compare price instead.

Retail does not need algorithmic detail. It needs a simple selling story.

What is the customer problem? What does the device do differently? What improves? Why is it better than the standard version? Why should the customer care? When will they notice the difference? What proof supports it? What should they choose if they do not need the AI version?

This is where many AI products fail commercially. The brand prepares a product story, but not a retail story. The claim sounds advanced in a launch deck but becomes weak in a store, on a marketplace page or in a distributor conversation.

An AI claim that cannot be explained simply should not be scaled broadly until the explanation is fixed.

The final test is commercial consequence. A good AI claim should do more than sound modern. It should strengthen the reason to choose.

Does AI justify a step-up? Does it make the premium model easier to defend? Does it make the product more distinctive? Does it improve trust? Does it create ongoing value after purchase? Does it support a service model? Does it reduce risk? Does it strengthen the ecosystem? Does it improve retention? Does it create a clearer product role in the portfolio?

If AI does not change the buying reason, it may still be useful, but it should not dominate the GTM story.

This is especially important as AI spreads across product ranges. If every product says AI, the word stops differentiating. The brand must explain why this AI matters, in this product, for this customer, in this situation, at this price.

The strongest AI claims do not simply say the product is smarter. They make the choice clearer.

Weak AI claims tend to describe the presence of intelligence without clarifying value. “AI-powered experience.” “Smart optimisation.” “Adaptive performance.” “Personalised recommendations.” “Intelligent automation.” “Next-generation device.” These phrases may be useful shorthand, but they are not enough.

Strong AI claims connect mechanism to outcome. “Reduces false alerts.” “Cuts setup time.” “Optimises energy use.” “Adapts cleaning to room conditions.” “Predicts maintenance before failure.” “Improves picture based on room light.” “Learns preferences to reduce manual settings.” “Recommends the right cycle to protect fabric.” “Detects usage patterns that help avoid waste.”

The difference is not only language. It is commercial discipline.

A weak claim asks the customer to trust the technology. A strong claim shows why the technology matters.

Not every category needs the same AI story. In some devices, AI is about convenience. In others, performance, reliability, safety, energy efficiency, personalisation, service or ecosystem value.

For a TV, the value may be better picture, sound, content discovery or interface simplicity. For an appliance, it may be energy, care, diagnosis, routine optimisation or reliability. For a robot, it may be navigation, autonomy, obstacle avoidance, cleaning quality or reduced intervention. For a wearable, it may be insight, coaching, pattern recognition or health-related interpretation. For a smart speaker, it may be natural interaction, service integration or household orchestration. For mobility, it may be safety, maintenance, route intelligence or battery management.

The mistake is to use the same AI language across categories. “Smart” is not a strategy. Each category needs a value translation.

The question is: what does intelligence mean in this category, and what outcome does it improve enough to matter?

AI claims also affect portfolio clarity. If AI appears in one product, it may support premium positioning. If it appears across the range, it may become a brand promise. If it appears inconsistently, it may confuse the customer. If it appears only in advanced models, it must justify the step-up. If simpler products remain non-AI, the brand must explain why simplicity remains a valid choice.

The portfolio question is: what role does AI play?

Is AI the hero of the range, the premium proof, the service layer, the ecosystem connector, the energy story, the personalisation layer, the support system or simply a feature improvement? If the role is unclear, the portfolio becomes harder to navigate.

This is where many brands will face a post-IFA challenge. They may launch several AI-enabled products, but the market may not understand which ones matter most or why.

AI should make the portfolio sharper, not busier.

The value test is incomplete without trust. AI-enabled devices often depend on data: usage patterns, preferences, environment, images, voice, location, diagnostics, performance logs or household behaviour. If customers do not understand what is collected, why it matters and how it improves their experience, the value can become fragile.

Trust is not a legal footnote. It is part of the commercial story.

A strong AI claim should be paired with a clear trust promise: what data is used, what benefit it enables, what control the customer has, what happens locally or in the cloud, what is stored, what can be turned off, and how the experience changes if the customer opts out.

This does not mean every product page needs a technical policy explanation. But it does mean AI claims should not be separated from transparency. When intelligence becomes personal, trust becomes part of the value proposition.

AI will be everywhere at IFA. That is exactly why the word will become less useful on its own.

The brands that stand out will not simply be those that say AI most often. They will be those that make AI understandable, provable and commercially relevant. They will show what improves, who benefits, how the improvement is evidenced, how retail can explain it, and why it creates a stronger reason to choose.

That is the value test.

AI claims that pass it can support premium pricing, portfolio clarity, retail activation, service models and customer trust. AI claims that fail it will become noise: impressive in launch language, weak in buying decisions.

The leadership question is not “how visible is AI in our product story?”

The better question is: which AI claims create value the market can understand, believe and choose?

Before IFA, take every AI claim in your launch, booth, product page, sales deck and retailer pack. Test it against five questions.

What customer outcome improves? Is the improvement visible or measurable? Is the proof credible? Can retail and sales explain it simply? Does it strengthen the reason to choose?

If the answer is weak, do not just polish the claim. Fix the value translation, strengthen the proof or reduce the prominence of the claim.

Short CTA: before putting AI at the centre of the story, make sure the claim survives the value test.

From The Strategic Brief
AI Inside the Device: From Feature to Business Model
AI Transparency Is Becoming a GTM Issue
Where AI Actually Creates GTM Leverage
Your IFA Launch Is Not Ready Until the Retail Story Is Ready
Too Many Products, Too Little Choice: The IFA Portfolio Clarity Index
Stop Managing Products. Start Managing Choice.
What IFA Reveals About the Future of Consumer Tech Markets
The Commercial Architecture of a Business Designed to Win

External reading
Harvard Business Review, The Elements of Value
Harvard Business Review, Customer Value Propositions in Business Markets
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
Harvard Business Review, The Age of Continuous Connection
April Dunford, Obviously Awesome
Byron Sharp, How Brands Grow
David Aaker, Brand Portfolio Strategy