A decade ago, machine learning seemed like an intriguing but distant technology. Today, it runs quietly through nearly every industry, from finance to fashion. Deloitte’s latest foresight report suggests quantum computing may follow a similar trajectory, though perhaps at a faster pace. The analysis places the next five years as a transition period: from isolated proofs-of-concept to experiments with commercial intent across sectors such as finance, chemicals, life sciences, mobility, energy, and logistics.
The hardware is progressing at a pace that was once considered unlikely. Deloitte identifies a technical inflection point, between 200 and 1,000 reliable logical qubits, as the moment quantum becomes not just experimental but commercially useful. From roughly 50 logical qubits today, the race to scale is accelerating, with players such as Google, Amazon, Microsoft, and PsiQuantum projecting breakthroughs within five to seven years.
McKinsey’s Quantum Technology Monitor reinforces this trajectory, estimating the global market for quantum technology could approach $100 billion by 2035, with computing alone accounting for as much as $72 billion. That figure is not purely speculative: in 2024, quantum companies generated an estimated $650 to $750 million in revenue, a number expected to exceed $1 billion in 2025. Governments are also moving assertively. Japan, for example, has committed $7.4 billion in new quantum investments this year.
Deloitte outlines four scenarios shaped by two uncertainties: how soon scalable systems arrive, and how developed the surrounding talent and operating ecosystems become.
· Surprise: Quantum arrives earlier than expected, but the workforce and operating environment lag, creating talent bottlenecks.
· Quandary: Progress is slow, talent ecosystems remain immature, and organizations that neglected experimentation lose a decade of learning.
· Explosion: Both hardware and talent mature early, leading to rapid adoption and periods of overexuberance.
· Leap: Talent and ecosystems are strong, but hardware lags; early movers continue to benefit from partnerships and incremental innovations.
The scenarios do not attempt to predict a single outcome. Instead, they illustrate how timing, talent availability, and ecosystem maturity could combine to shape competitive realities.
The workforce challenge is already visible. Analysts estimate that 250,000 quantum jobs will be needed by 2030. Yet global job postings grew just 4.4% over the past year and even declined month-over-month in early 2025. Retraining classical computing specialists has proven costly, while dedicated academic programs remain narrow. In Deloitte’s 2024 survey, one-quarter of US executives reported investing in quantum, a threefold increase over the prior year, yet most cited talent as their single greatest barrier to scaling.
hat is at stake for leadership is not technical fluency but strategic foresight. Key questions now surfacing at board level include: where quantum could alter industry economics; how partnerships position organizations for access to scarce talent and intellectual property; and what investments in infrastructure or encryption may be required to manage risk over the long term.
As Idalia Friedson, former chief strategy officer at Strangeworks, noted: “A lot of people think there’ll be a moment when quantum just turns on once the hardware is ready. But it’s not that simple. Like flipping a light switch, it only works if the wiring behind the scenes is already in place.”
Across Deloitte and McKinsey, the signal is consistent: the next five years are likely to bring measurable progress, in hardware reliability, revenue generation, and public-private investment, without uniform adoption across the economy. For leaders in finance, life sciences, energy, mobility, and beyond, the most relevant insights today concern pace, reliability, ecosystem access, and security readiness. What emerges is not a countdown to a single breakthrough, but an evolving landscape where foresight, collaboration, and readiness may prove as consequential as the hardware itself.
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