Logitech's CEO: AI Gadgets Are Solving Problems No One Has
Logitech's chief executive, Hanneke Faber, is skeptical of standalone AI devices. In a recent interview, she said the category hasn't proven a clear use case while phones and PCs absorb the same capabilities with less friction.
Her stance follows a year of AI-first gadget launches that struggled to win mainstream adoption. Meanwhile, Logitech is channeling AI into familiar peripherals and returning revenue to pre-pandemic levels.
Why this matters for your strategy
Products like the Humane AI Pin and Rabbit R1 promised to replace parts of the smartphone experience. They ran into slow performance, thin feature sets, and subscriptions that felt like a toll, not value.
The bigger signal: as phones and PCs gain on-device models and tighter assistant integrations, a dedicated device has to beat incumbents on speed, convenience, and price. Most haven't.
Logitech's AI play: augment, don't overbuild
Instead of creating a new device class, Logitech is adding AI where it removes friction in existing workflows. Webcams use subject-aware framing and noise filtering. The new MX Master 4 ships with shortcuts tied into ChatGPT and Microsoft Copilot.
Faber framed this as discipline. The company ships roughly three dozen products a year on a multi-cycle roadmap, so every feature has to earn its place with a clear benefit.
Supply chain and pricing: steady hands, fewer surprises
Logitech raised prices earlier this year following tariff changes. With the supply chain now stabilized across China and five additional countries, further increases aren't expected.
Diversification has restored consistency across 13 product categories, many near pandemic-era volumes. Growth in China has accelerated, driven by a locally led product strategy after prior market share losses.
The market isn't done testing hardware
Large firms are still exploring AI-first devices. OpenAI's acquisition of a startup co-founded by Jony Ive has sparked talk of a consumer product built around generative models.
Whether that avoids the pitfalls of earlier launches is unclear. Faber's read is simple: creating demand for a standalone assistant is hard when multifunction devices already do the job.
What leaders should do next
- Prioritize AI features that remove steps in existing workflows over net-new hardware.
- Pilot assistant shortcuts inside tools your teams already use (e.g., ChatGPT/Copilot) and measure cycle-time savings.
- Set hard gates for any hardware bet: specific use case, measurable lift, durable distribution, and a support model users will accept.
- Price with restraint. Avoid subscription stacking unless there's clear, recurring value. Watch tariff exposure and build multi-country supply options.
- Localize for China with in-country leadership and product decisions close to the customer.
- Lock a two-to-three-cycle roadmap. Kill features that can't prove adoption or margin impact.
If you're upskilling teams to execute practical AI inside current products and operations, explore role-based programs at Complete AI Training.
The takeaway: Don't chase novelty. Build AI where your customers already live, prove the lift, and keep your supply chain and pricing boring-in the best way.
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