Insider Brief
- IBM’s The Enterprise in 2030 study finds quantum computing is expected to reshape industry by the end of the decade, but most enterprises are not preparing for its adoption, creating a strategic gap that could leave even AI-advanced organizations exposed.
- While 59% of surveyed executives believe quantum-enabled AI will transform their industry by 2030, only 27% expect their organizations to be using quantum computing, a disconnect IBM describes as a strategic miscalculation rather than a timing issue.
- The report positions quantum as a complementary technology that will integrate gradually into hybrid classical-AI workflows, with early enterprise use cases already emerging in areas such as drug discovery and financial optimization.
IBM expects quantum computing to reshape industry by 2030, yet most enterprises are not preparing to use it — a gap that the company’s experts warn could leave even AI-advanced organizations exposed to the next major computing shift.
That warning sits at the center of The Enterprise in 2030, a study from the IBM Institute for Business Value, which surveyed more than 2,000 senior executives across 33 geographies and 23 industries in late 2025. The report’s central finding is not that quantum computing is imminent, but that it is inevitable — and that corporate strategy is lagging that reality.
According to the study, 59% of executives believe quantum-enabled artificial intelligence (AI) will transform their industry by the end of the decade. Yet only 27% expect their organizations to be using quantum computing in any capacity by that time. IBM characterizes the gap as a strategic miscalculation rather than a technology timing issue, arguing that companies focused narrowly on AI risk missing a deeper shift in how computation itself evolves.
“Quantum will never stand alone,” said Dr. Thomas Eckl, chief expert at Bosch, in the report. Classical computing, AI and quantum must work together in connected workflows.”
The study positions quantum computing not as a replacement for today’s systems, but as a complementary tool designed to handle classes of problems that strain even the most advanced classical computers — particularly optimization, simulation and probabilistic analysis. Those capabilities, IBM argues, will increasingly matter as AI systems grow more complex and data-hungry.
Readiness Gap — Strategic Consequences
IBM’s researchers frame quantum computing as the fifth and most underappreciated of five forces shaping the enterprise by 2030, following competitive pressure, productivity reinvestment, tailored AI models and the limits of automation. Unlike earlier waves of enterprise technology, the report suggests quantum’s impact will be indirect at first, most likely appearing inside hybrid workflows rather than as standalone systems.
However, the survey data show a disconnect with many executives treating quantum as a distant research topic rather than a planning priority. While nearly six in 10 respondents expect quantum-enabled AI to reshape their industry, fewer than three in 10 expect to deploy quantum tools themselves.
IBM describes that mismatch as unusual compared with prior technology transitions. In areas such as cloud computing and cybersecurity, adoption expectations and readiness tended to move together. With quantum, belief has outpaced preparation, according to the report.
The risk is not that companies will miss an early advantage, but that they will fail to adapt when quantum capabilities begin affecting markets indirectly — through supply chains, financial systems, drug discovery pipelines and national infrastructure.
“Quantum has the potential to accelerate computing — and to unlock use cases beyond the abilities of even today’s most powerful high-performance computers,” the study states.
IBM does not predict a single moment when quantum computing “arrives.” Instead, it describes a gradual integration process similar to how GPUs and specialized AI chips entered enterprise systems. IBM sees quantum arriving first as accelerators for narrow tasks, then as core components of broader platforms.
From Lab to Enterprise
To counter skepticism that quantum computing remains purely theoretical, the report covers several real-world deployments already underway inside large organizations.
One of the most detailed examples comes from Moderna, which has been applying quantum algorithms to problems in mRNA design. The challenge, according to the study, lies in predicting how strands of messenger RNA fold, a problem with an astronomically large number of possible configurations.
“Our goal is to improve human health,” said Alexey Galda, associate scientific director of quantum algorithms and applications at Moderna. “We believe it’s critical to explore every available tool — including quantum computing — to scale our progress today, rather than wait for the technology to fully mature.”
IBM reports that Moderna has used quantum systems involving up to 80 qubits to model mRNA secondary structures, surpassing prior limits in the field. The company also applied its approach to larger optimization problems using as many as 156 qubits and 950 non-local gates, with results matching those of commercial classical solvers.
The IBM team reports that the significance is not that quantum outperformed classical methods, but that it performed comparably on problems previously considered impractical for quantum systems. That parity suggests quantum computing is beginning to move from academic experimentation toward targeted enterprise workloads.
The report also cites early quantum experimentation in financial services, including a quantum-enabled algorithmic trading demonstration conducted by HSBC. In finance, where optimization and risk assessment are central, IBM suggests quantum tools may be adopted earlier than in sectors focused primarily on transactional processing.
To be clear, these examples are presented as signals rather than proof. IBM repeats throughout the report that quantum computing will not replace classical systems, nor will it deliver broad productivity gains on its own. Its value lies in addressing narrow bottlenecks that compound across complex systems.
AI Strategy not Enough
IBM calls particular attention in the report to organizations that view AI investment as sufficient preparation for the next decade. While AI dominates executive agendas — with 79% of respondents saying it will significantly contribute to revenue by 2030 — IBM argues that AI’s growth will increase demand for new forms of computation rather than reduce it.
As AI models become larger and more specialized, they generate optimization problems that classical systems struggle to solve efficiently. Quantum computing, IBM suggests, offers a way to handle those challenges without relying solely on brute-force scaling.
“By 2030, insight will be everywhere,” said Chad Gates, managing director at Pronto Software, in the report. “Interfaces will be radically different and AI will act as the business intelligence system, decision engine and a participant in operations.”
IBM’s analysis suggests that quantum computing’s most immediate role will be as an accelerator for AI-driven workflows. In other words, quantum will improve training, optimization and simulation rather than replacing existing infrastructure. That framing positions quantum readiness as part of AI governance and architecture decisions being made today.
The report also links quantum preparedness to cybersecurity, particularly as post-quantum cryptography standards move closer to adoption. While cybersecurity does not rank as a top executive priority for 2030 in the survey, IBM describes it as a “table stakes” capability that must evolve alongside emerging technologies.
In that context, quantum computing becomes less about competitive advantage and more about systemic resilience.
Plan for Inevitability, not Immediacy
IBM stops short of urging companies to deploy quantum hardware immediately. Instead, it argues that enterprises should treat quantum the way they once treated cloud computing before mass adoption. It should begin building skills, partnerships and governance frameworks sooner, rather than later.
The study calls for organizations to experiment with hybrid workflows that combine classical computing, AI and quantum tools, even if those quantum components remain small. It also urges leaders to monitor quantum developments as part of strategic risk management rather than innovation scouting.
This framing reflects the report’s broader thesis: that the enterprise of 2030 will be defined less by any single technology than by how organizations integrate multiple systems into adaptable architectures.
Competitive advantage in 2030 will come from innovation rather than resource optimization, a statement in the report that most — 64% — of the experts who were polled .
IBM’s researchers conclude that quantum computing fits that pattern. According to IBM, its impact will likely emerge unevenly, first benefiting organizations that have already mapped where advanced computation could unlock value.
According to the report, those that wait for clarity may find that the shift arrives embedded in markets and supply chains rather than announced in product launches.
