When quartz technology arrived in the late 1960s, it didn’t just make watches more accurate; it fundamentally changed what people were willing to pay for. Today, AI is doing something similar to consulting and applied research: routine analysis and report writing are becoming cheap, fast and widely accessible, forcing us to rethink where real value in knowledge work actually lies. This blog uses the quartz crisis in watchmaking as a lens to explore how organizations can reposition their services in an AI-shaped world.

In the 1970s, the Swiss watch industry went through its own “end of the world” moment: the quartz crisis. For centuries, mechanical watchmakers had competed on precision. Then Seiko launched the Quartz Astron in 1969, the first commercial quartz wristwatch, which was far more accurate, cheaper, and easier to mass produce than mechanical movements. Timekeeping suddenly became a commodity. The impact was brutal. Between 1970 and the late 1980s, employment in the Swiss watch sector fell from around 90,000 to roughly 30,000, and the number of watchmakers dropped from about 1,600 to 600. Many brands disappeared. But a few players, like Rolex, Patek Philippe, Audemars Piguet, Vacheron Constantin and others survived by changing the basis of competition.

They stopped trying to beat quartz on accuracy and instead focused on what quartz could not offer: heritage, craftsmanship, emotional resonance, and cultural status. Mechanical watches evolved from simple tools into symbolic objects; pieces of engineering art, identity, and continuity that people proudly wear, collect, and pass on. Today, AI is playing a similar role in consulting and applied research.

AI as the Quartz of Consulting and Applied Research

Large language models, code assistants and data analyzers have made many classic consulting and research tasks cheap, fast and widely accessible. Drafting reports, compiling literature reviews, generating first pass market analyses, or building baseline models are now tasks that AI can support or partially automate at scale. For customers and stakeholders, this changes the economics. A “good enough” overview or slide deck is no longer exclusive to big consulting firms or specialized research institutes, which can be approximated by individuals with the right prompts and tools. As in the quartz crisis, the core utility function is being commoditized.

If consulting and applied research continue to compete mostly on speed of production, volume of deliverables or standard analytical quality, they risk being squeezed into a low margin, high competition layer of AI augmented services. The question becomes: what is the equivalent of the modern mechanical luxury watch in applied research and consulting?

Shifting from Commodity Outputs to Human Complications

The watch industry’s answer was to double down on what machines could not easily copy: fine mechanical craft, narrative depth, and emotional meaning. For consulting and applied research, the analogue is to focus on higher order value instead of commodity outputs:

  • Problem framing, not just problem solving: Defining the right question, scope and constraints in complex contexts with conflicting stakeholder interests and incomplete data.
  • Judgment and trade offs: Balancing technical feasibility, organizational realities, ethics, regulation and long term risk, rather than only optimizing metrics in a slide deck.
  • Implementation and change: Designing governance, training, and adoption pathways so that recommendations work in practice, not just in PowerPoint.

    • AI can generate excellent content, but it does not own or have responsibility for consequences, relationships or organizational memory. Just as quartz can keep perfect time but does not embody lineage or craft, AI can assemble compelling reports but cannot substitute for trust, accountability and lived experience.

    The strategic pivot is therefore to move from “we produce analysis” to “we design and steward decision systems” in which AI is embedded as a component, not treated as the enemy.

    AI as the New Movement Inside the Watch

    Mechanical brands did not survive by pretending quartz did not exist; they allowed quartz to dominate everyday timekeeping while positioning mechanical watches as something different and higher order, focused on niche luxury and emotional value. Similarly, the most resilient consulting and research practices will be those that treat AI as the new movement inside their services. This means using AI for exhaustive data crunching, scenario generation and rapid iteration in the early phases of projects, designing workflows in which AI handles volume and pattern recognition while human experts concentrate on synthesis, stakeholder alignment and risk management, and building “complications” around AI custom methods, industry specific playbooks and proprietary validation layers that ensure quality, robustness and transparency. In watch language: let AI keep the time, while you build the complex parts of the movement, where those nuanced decisions and integrations that matter specifically to a given company or ecosystem.

    After quartz, brand and trust became central to mechanical watches: people buy a Rolex or Patek not only for the mechanics, but for what the brand stands for. In the AI era, consulting and research are moving in the same direction. When “basic analysis” is widely accessible, customers and stakeholders start valuing proven impact in difficult environments, not just beautifully formatted reports. They tend to appreciate clear rules for how models are used, how bias and hallucinations are managed, and how data is protected. It is important to have continuous support through multiple transformation cycles, not one off projects. Trust becomes a key differentiator: in a world of abundant AI outputs, who do you call when decisions are high stakes and messy? The answer is rarely “another model”, most probably it is people and organizations that have repeatedly shown sound judgment and integrity.

    Practical Implications: How to Overcome the Crisis

    For individuals in consulting and applied research, advanced AI literacy, covering prompts, tool selection and workflow design should be treated as basic infrastructure, on par with statistics or Excel. At the same time, it is crucial to invest heavily in meta skills such as problem structuring, storytelling, stakeholder communication and ethical reasoning, because these capabilities turn raw AI outputs into responsible, context aware decisions. Ultimately, the goal is to position oneself as an orchestrator of intelligence rather than a mere producer of slides: someone who can combine AI, domain knowledge and organizational context into sound, accountable choices.

    For companies and institutions, the priority is to rebuild the portfolio around AI augmented premium experiences, strategy workshops, immersive decision labs and continuous advisory services that integrate human expertise with machine capabilities. Organizations should focus on proprietary processes, platforms and communities instead of relying solely on proprietary documents, thereby creating value that generic tools cannot easily copy. In parallel, they need robust AI governance and transparency to maintain trust in an increasingly stringent regulatory environment, particularly within the EU. The “crisis” scenario is straightforward: doubling down on commoditized deliverables while margins and differentiation shrink; the “quartz crisis” scenario offers a more promising path by redefining value around human strengths that AI amplifies rather than replaces.

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    Abdulrahman Abdelrazek

    Autorenprofil - LinkedIn



    Kategorien: Arbeitswelten (New Work, Connected Work), Digitale Transformation, Künstliche Intelligenz
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