The luxury industry sits on a goldmine of customer data spanning decades. Yet even as AI promises to unlock unprecedented insights, many brands remain trapped in pilot purgatory – enthusiastic about the technology’s potential but struggling to move beyond experimentation. How can this change?
The party’s over. After years of post-pandemic exuberance, the luxury watch industry is waking up to a harsher reality. Tariffs squeezing margins, gold prices at record levels, and a strong Swiss Franc are all hindering Swiss exports. Inflation has dampened aspirational buyers’ enthusiasm. Geopolitical tensions disrupt everything from supply chains to market access. For an industry accustomed to measured, incremental growth over decades-long product cycles, the current environment presents an uncomfortable truth: expansion can no longer be taken for granted.
So when the tide turns, where do you find growth? The answer, increasingly, lies in looking inward – scrutinising operations, optimising strategies, making smarter bets. And that requires data.
Here’s the paradox: watchmaking has never had more information at its fingertips. Two decades of e-commerce transactions. Social media engagement across platforms. Secondary market pricing that updates in real-time. Authorised dealer sell-through rates. Boutique foot traffic patterns. Digital campaign performance metrics. The list expands with every new consumer touchpoint.
“We’ve got an incredible amount of transactional data for our customers over the course of the last 20 years,” observed Fran Millar, CEO of Rapha, in a recent interview on The Luxury Society Podcast. “Consolidating it all and then empowering our teams and our people to utilise AI tools to mine that data – I think that’s the biggest opportunity for us that we’re going to go after first.”
Millar’s observation captures both the promise and the challenge. In theory, this data wealth should enable manufactures to formulate sophisticated strategies, respond faster to market shifts, and allocate resources with surgical precision. AI amplifies this potential exponentially – machine learning algorithms can process datasets at speeds impossible for human analysts, identifying patterns and anomalies that would otherwise remain hidden.
The urgency is palpable. According to recent findings in The State of AI in Luxury, over 71% of luxury executives agree that AI adoption cannot be delayed, with 43% expecting full integration within the next 6-12 months. The message is clear: hesitation is not an option.
However, over 42% are still in the “Experimenting” phase. The experimentation bottleneck is real, and three fundamental challenges explain why.
Walk into any manufacture’s IT department and you’ll find a familiar story. Customer data lives in the CRM system. E-commerce behaviour sits on a different platform. Social media analytics exist in yet another environment. Point-of-sale data from authorised dealers? That’s scattered across retail partners’ systems. Secondary market pricing? External platforms that don’t integrate with anything else.
For watchmakers operating across dozens of markets, this fragmentation multiplies exponentially. A Geneva-based manufacture might have excellent visibility into boutique performance in Europe but limited insight into dealer inventory in Asia. They might track Instagram engagement religiously but struggle to connect that buzz to actual purchase behaviour. They might monitor Chrono24 prices – a critical leading indicator – yet find it nearly impossible to feed those signals into demand planning systems.
The regulatory landscape compounds the problem. GDPR requirements in Europe, data localisation mandates in China, varying privacy laws across jurisdictions – brands are often legally prevented from building the unified datasets that effective AI requires. Customer information must remain on servers in specific countries, creating barriers that are difficult, sometimes impossible, to bridge.
The irony is crushing: the industry sits on decades of rich transactional data yet struggles to consolidate it effectively. And without solving this foundational infrastructure challenge, even the most sophisticated AI tools remain underutilised – like installing a Formula One engine in a car with square wheels.
Suppose you solve fragmentation. Congratulations – now you’re drowning.
Modern watchmaking executives face an avalanche of metrics. Dashboards overflow with KPIs. Reports stack up with granular breakdowns across regions, channels, product categories. Every meeting brings new analyses, fresh charts, more numbers to digest. The abundance, rather than clarifying decisions, creates paralysis.
This phenomenon helps explain the 42% of luxury brands trapped in experimentation. They’re running tests, gathering more data, conducting more analyses – but not necessarily making better decisions. The fundamental problem isn’t insufficient information; it’s insufficient clarity and lack of clear processes.
What executives actually need is intelligent filtration. In watchmaking, this might mean obsessive monitoring of secondary market prices against search demand for key references – grey market values often indicate retail demand months ahead. It might mean tracking specific social media engagement patterns that correlate with boutique traffic. It might mean identifying the handful of metrics that serve as genuine leading indicators rather than lagging confirmations.
What the industry doesn’t need is another dashboard. What it needs is continuous, intelligent monitoring – systems that track thousands of signals simultaneously, learn which combinations predict meaningful outcomes, and algorithmically flag insights when they become relevant. When secondary market prices for your sports collection suddenly spike, you should know immediately, complete with context about what’s driving the change. When social media sentiment shifts, marketing teams should receive alerts before patterns solidify. When dealer inventory deviates from norms, commercial teams should be prompted to investigate.
This is where AI excels. Machine learning can serve as an intelligent filter, surfacing insights that warrant executive attention while letting noise fade into the background. For an industry built on deep client intimacy and long purchase cycles – where brands must analyse nuanced signals from waitlist requests, auction results, and collector forums to map a client’s journey from first inquiry to grail purchase – AI offers the only scalable solution.
Even perfect data infrastructure and crystal-clear insights aren’t enough. Here’s the scenario: your AI system successfully identifies a critical pattern. Secondary market prices for your sports line are declining. Social media engagement has plateaued. Dealers report slower sell-through. The algorithm has done its job, cutting through noise to surface something that matters.
Now what?
This is the final mile problem – the chasm between knowing and doing. The insight is valuable, but it doesn’t automatically generate a response. Should you adjust production? Shift marketing spend? Offer dealer incentives? Launch a new colourway? The answer requires contextual knowledge, strategic judgment, and coordination across multiple functions – things algorithms don’t provide.
Brands understand AI can produce outputs, but haven’t translated that capability into strategic, customer-facing applications that drive business outcomes.
The next frontier lies in AI-generated action plans. Imagine a system that doesn’t just identify the declining performance pattern but also generates: an executive briefing contextualising the trend; a preliminary report for product teams exploring responses based on historical patterns; draft communications for commercial teams discussing dealer strategies; scenario analyses showing likely impacts of different interventions.
These outputs wouldn’t replace human judgment – strategic decisions still demand the nuanced understanding only experienced leaders provide. But they would dramatically accelerate the path from insight to action, ensuring intelligence translates into timely responses rather than getting lost in organisational inertia or the next experimental pilot.
The promise of AI in watchmaking isn’t about replacing master watchmakers with algorithms or automating away creativity. It’s about building infrastructure that allows brands to leverage data they’re already collecting – connecting fragmented streams, filtering signals from noise, and translating insights into strategic action.
As growth becomes harder to secure, the manufactures that master this trifecta will claim decisive advantages. They’ll respond faster to market shifts, allocate resources more efficiently, and make strategic bets grounded in evidence rather than instinct. They’ll transform overwhelming complexity into competitive edge rather than paralysis.
The question facing watchmaking isn’t whether to embrace AI. The question is whether brands are building foundational capabilities to make AI genuinely transformative rather than just another unused analytics tool or, worse, another pilot trapped in the experimentation bottleneck currently defining 42% of the sector.
Those who solve this won’t merely survive current challenges. They’ll emerge stronger, more agile, better positioned for whatever comes next. The path demands moving beyond experimentation into systematic implementation: consolidating fragmented architectures, investing in cross-functional AI fluency, focusing on proven use cases where AI delivers clear business outcomes.
The data is there. The technology exists. What separates winners from experimenters is superior processes and execution.
The bottleneck isn’t technical. It’s organisational. And the clock is ticking.
