Published September 2, 2026 in Artificial Intelligence • 12 min read • 
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At the start of 2026, Logitech CEO Hanneke Faber faced a pivotal strategic decision. She believed that emerging AI agents, capable of controlling screens, cursors, and keystrokes through software alone, presented an opportunity rather than a threat for the world’s largest maker of computer mice. If hardware evolved into the sensory layer of AI, becoming the eyes, ears, and hands through which humans interact with intelligent systems, devices would no longer simply execute commands; they would understand context, interpret intent, and help bridge the physical and digital worlds.
Yet Faber was also wary of the hype. In interviews, she dismissed “AI gadgets” as “solutions looking for a problem that doesn’t exist.” The challenge was not whether AI would transform computing but where to place the bet. Pursuing this vision would require Logitech to move beyond products into platforms, partnerships, and entirely new forms of value creation – and to build a culture and operating model capable of integrating a fast-moving web of technologies.
Logitech’s dilemma is one many leaders now face. As AI reshapes industries, the critical question is no longer what the technology can do but where a company should apply it. The companies that thrive in the AI era will not necessarily be those that adopt the most AI. They will be those that position themselves where value is migrating and develop the capabilities to move there.
In a previous article, two of us (Cyril and Julian, together with Julian’s colleague Christopher J Wright) argued that the success of AI strategies depends on two critical dimensions:
Value-chain control: Who owns the customer relationship and captures the value?
Organizations with high value-chain control can test, refine, and scale innovations quickly because they exert greater influence over product design, manufacturing, distribution, and customer relationships. Samsung, for example, can roll out AI-powered display or camera improvements across its entire product portfolio because it controls everything from chip fabrication to global retail. Those with low value-chain control can only use AI to make their products better within a narrow scope, with limited impact beyond their offerings.
Technological breadth: How many technologies and partners must be orchestrated to deliver value?
In high-breadth industries – modern connected cars, for instance, which combine AI sensors for assisted parking and lane-keeping with map providers, telecommunications companies, and cloud partners – competitive advantage depends on integrating a constantly evolving set of technologies.
In low-breadth industries – food processing or basic logistics, for example – companies operate with fewer, more stable technologies and use AI to refine existing processes rather than redefine the landscape.
The two dimensions point to four distinct approaches companies can adopt to realize AI’s potential:
Focused differentiation: win through superior products.
Vertical integration: control more of the customer journey.
Collaborative ecosystem: create value through partnerships.
Platform leadership: orchestrate interactions between many participants.
To understand your current position – and whether it is sustainable – complete the diagnostic tool.
Only genuine transitions – changing what a company integrates or what it controls – reposition it where value is heading.
Before asking when to move, leaders should ask whether their AI initiatives amount to movement at all. Compare two products. The hearing aid has travelled an extraordinary journey across the matrix. Early devices were simple analog amplifiers that made every sound louder. Digital models could then be programmed to each wearer’s specific hearing loss. Next came AI-assisted aids that connect to the smartphone and adapt automatically to different sound environments. Today the category is evolving into an AI hearing platform: devices that also monitor health, translate foreign languages in real time, and keep learning through the cloud. Each step expanded both the range of technologies integrated and the share of the customer relationship captured.
Now consider the AI-enabled kettle. AI adds a measure of convenience, but there is no ecosystem expansion, no new service, no new value chain. The product’s position on the matrix has not changed – and neither have the company’s prospects. The first is a journey; the second is a feature. Only genuine transitions – changing what a company integrates or what it controls – reposition it where value is heading.
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Companies are not free to choose their position indefinitely. Sooner or later, they are forced to move, whether by the emergence of new technologies, shifting customer expectations, or new competitors. There are four critical signs that your position is becoming untenable:
If your position is untenable, consider the following strategic moves, to be used individually or in concert.
Moving along the technology axis requires companies to integrate an increasingly diverse set of technologies, often across fast-evolving, interconnected ecosystems they neither own nor control.
Logitech’s personal workspace products, which include computer mice, keyboards, and webcams, provide a good example. The company is a hardware champion – 1.5 billion users touch its devices on a daily or weekly basis – but AI has traditionally enabled incremental product enhancements and new functionality, putting its peripherals largely in the focused differentiation quadrant. To move, Logitech has prioritized creating value through strategic partnerships that bring external AI capabilities into its products. An early attempt was the Logitech Voice M380 mouse, launched in China in 2021 in collaboration with Baidu Brain, the AI arm of the Chinese search giant. Equipped with a dedicated dictation button, the device uses Baidu’s speech recognition and machine translation technologies to transcribe and translate spoken language directly at the cursor.
Today the company works with a broad portfolio of partners, from platform leaders like Microsoft, Google, and Apple to NVIDIA, Zoom, Amazon, and JD.com, among many more, to deliver software capabilities directly to users. The depth of collaboration varies: at one level, Logitech simply exposes new operating-system features to its users as they appear; at another, it is looking into AI user experiences that can be attached to the mouse cursor, similar to Google’s Magic Pointer on Chromebooks, which lets users point at content on the screen and request immediate AI assistance. At the deepest level, it engages with its partners in joint design work on how the underlying user experience should evolve. The aim is a win-win in which both sides reimagine the user experience together. Rather than attempting to build a proprietary AI stack, Logitech is aligning its AI strategy with the realities of its business model, capabilities, and position within the value chain.
The payoff is greater relevance within a rapidly expanding digital ecosystem and access to innovation at scale. The trade-off is increased coordination complexity, greater dependence on platform providers, and less control over key layers of the technology stack. For leaders, the challenge is not simply deciding how much to invest in AI but determining where AI can create the most value given the organization’s strategic position and capabilities.
Shifting up or down the value chain allows firms to capture more of the journey from idea to end user. The reward is greater control over customer experience, data, and margins. The cost is increased exposure, operationally and strategically.
Signify, the former Philips Lighting, shows what crossing this axis looks like. It began as a focused differentiator selling premium smart bulbs, then launched Interact, its connected-lighting software platform, to build direct relationships with enterprise and city customers – deliberately acquiring more of the path from idea to market, along with the data and service revenues that come with it.
Logitech’s teamwork solutions for enterprise meeting rooms illustrate how hard the same decision can be. The business currently sits in the collaborative ecosystem quadrant. Products include AI-powered meeting room cameras that run its CollabOS software and make remote attendees feel like they are seated in the room. But these products reach customers only via the virtual meeting platforms: they must be certified for Microsoft Teams, Zoom, and Google Meet, and they depend on those platforms’ road maps and rules.
The risk is that these platform owners could enter the hardware layer directly, disrupting Logitech in much the same way that smartphones disrupted GPS device makers. Yet for Logitech to move into platform leadership would require enormous amounts of capital deployed against entrenched incumbents. The company previously tried to compete head-on with Cisco and Polycom through its 2009 acquisition of videoconferencing company LifeSize Communications (now Lifesize) for roughly $400m. However, it eventually spun off the business.
This experience shaped Logitech’s current strategy: build conference-room hardware in-house, keep earning – through continuous hardware innovation – the right to remain a valued partner to the major platforms, and avoid betting the company on owning one.
No position should be permanent. Often the most dangerous choice is inertia.
Once a leader in standalone navigation devices, TomTom evolved into a data-enabled ecosystem with real-time updates and partnerships. But as navigation became embedded within smartphones and automotive systems, its core functionality was absorbed by larger platforms. Without securing a defensible position, it was sidelined.
Logitech sees the advent of AI agents as a similar shift – one that will automate parts of the digital space regardless of what any company decides. Asked whether the move toward agentic and ecosystem strategies is really a choice, Nicolas is categorical: it is a necessity, more akin to the laws of physics or evolution. When the rules of the game change, a company that keeps playing the old game brilliantly will still lose.
This has framed Logitech’s thinking around a shift from precision to intention. Historically, its tools have given users fine-grained control, the way a musician’s fingers control an instrument. Wide adoption of agentic AI means its products will need to shift to conducting – conveying intent to a system that executes it – whether the piece is simple and well-defined, like moving an image or a piece of text, or more complex and open-ended, like briefing a project to an outside team.
Companies such as Logitech may thus evolve from device manufacturers into interface orchestrators, helping users interact seamlessly with AI systems across platforms and environments. Whether any individual company can successfully occupy that position is uncertain, but the broader point stands: as AI reshapes how humans engage with technology, firms must rethink their products as well as the role they play in the ecosystem.
The success of any strategic shift depends on a handful of organizational levers that determine whether a company can adapt – or remains trapped by its past.
Moving from one quadrant to another is rarely a question of technology alone. The success of any strategic shift depends on a handful of organizational levers that determine whether a company can adapt – or remains trapped by its past. We have identified five. Each can act as fuel that accelerates the journey, or as a brake that stalls it.
| Lever | When it acts as fuel | When it acts as a brake |
|---|---|---|
| Leadership | Funds ahead of proof | Stalls at pilot budgets |
| Culture | Fails fast, then learns | Pilots run for visibility, not validation |
| Capital | Backs long feedback loops | Rewards quarterly optics |
| Talent | Bridges tech and business domains | Caps how far you can go |
| Brand | Lowers adoption friction | Breeds lock-in anxiety |
Leadership. The most important lever. As fuel, leadership funds the move ahead of proof; as a brake, it stalls at pilot budgets. Strategic transformation requires sustained commitment from the top: transformation is rarely linear, and the temptation to abandon a course midway is strong. The key leadership challenge is greenlighting AI investments before returns are certain. Because AI-driven shifts often emerge gradually, leaders must place bets before competitors do, and before the financial case is fully proven.
Culture. As fuel, a strong innovation culture fails fast and learns; as a brake, it runs pilots for visibility rather than validation. Innovation, by its nature, involves surprise – new ideas, new ways of working, challenges to long-standing assumptions – and organizations vary widely in how much resistance they put up against surprising ideas. Smaller, more agile companies often adapt more easily, while former state-owned enterprises can face particularly steep barriers because stability has been embedded in their DNA for decades. Even at Logitech, bridging the gap between core operational teams and visionary innovation units involves balancing short-term financial targets with long-term strategic bets. Success depends on aligning decision-making capacity and resources to match the speed of larger, fast-moving ecosystem partners.
Capital. As fuel, capital backs the long feedback loops that transformation requires; as a brake, it flows to whatever flatters quarterly optics. Building new capabilities, acquiring new technologies, and entering new markets all demand investment with distant, uncertain returns – yet reward systems and sunk costs often pull spending back toward the optimized core. Consider cigarette manufacturing, where production lines have been tuned over decades for maximum speed and efficiency: even modest changes carry significant costs, creating powerful incentives to maintain the status quo.
Talent. As fuel, talent bridges technology and domain expertise; as a brake, its absence caps how far you can go. This lever is less critical than the three above, because the right skills and capabilities can be accessed through strategic partnerships when needed to move across the axes.
Brand. As fuel, a strong brand lowers adoption friction and opens doors; as a brake, it breeds lock-in anxiety and anchors customer perceptions in ways that make repositioning difficult. Consumers buy a BMW not simply for transportation but for what the brand represents – and a company moving into a new competitive space may find that its brand equity does not travel with it. For Logitech, its positioning as a trusted partner operating across platforms has enabled its webcams to be used at US immigration as well as at the border between Hong Kong and Shenzhen.
While some challenges vary by industry and strategic position, certain levers are universal. Leadership, culture, and capital shape the ability of every organization to evolve. Together, the five levers determine whether a company can successfully reposition itself as markets, technologies, and sources of value continue to shift.
Too many organizations begin their AI journey by asking what the technology can do. The more important question is where they intend to compete as AI reshapes ecosystems, value chains, and customer expectations. Only then can they determine which technologies matter.
Hanneke Faber’s ambition is to move Logitech toward platform leadership, setting the industry standard for how humans interact with AI, much as USB became the standard for connecting devices to computers in the late 1990s. The goal is to build these capabilities now, so that Logitech can surf the wave of technological change rather than be swept aside by it.
This strategy carries real risks: greater dependence on software ecosystems Logitech does not control, the danger of cannibalizing existing revenue streams, and the possibility that new competitors emerge from entirely unexpected directions. Tesla and xAI’s announced Digital Optimus project is a case in point – a video-to-action agent designed to operate a virtual mouse and keyboard cheaply and at massive scale, illustrating how capabilities built for one domain can suddenly threaten another.
Implementing an AI strategy is ultimately an organizational challenge, not a technological one. Success requires leaders to align the five levers: leadership willing to commit before certainty; a culture capable of absorbing surprise and making bold, long-term bets; capital allocated toward emerging sources of value, even at the risk of disrupting existing businesses; talent – and the partnership structures that extend it – able to operate in new business models and ecosystems; and a brand that customers and partners will continue to trust as the ecosystem evolves.
AI is not a strategy. It is a tool that enables one. The organizations that create the most value will be those that first decide where they can win as value shifts, and then build the leadership, culture, capital, talent, and brand required to move there. That is why AI strategy is not a position but a journey – and why the levers that matter most are organizational, not technological.
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