For years, boards have held quantum at a familiar distance: eventually important, potentially transformative, and clearly disruptive but still “far enough away” to leave to research labs. That assumption is now much harder to defend.

The technology is moving toward fault-tolerant systems by 2028 to 2029, with IBM’s roadmap targeting 200 logical qubits in 2029. At that threshold, quantum machines will begin to outperform classical systems on some high‑complexity optimization and simulation problems. For hardware players, this will mark a major step toward fault‑tolerant quantum computing; for enterprises, it will be the point at which quantum can start to generate a computational competitive advantage.

Industries where differentiation is rooted in rapid simulation models or optimization of scenarios will be the first to realize this advantage, such as designing molecules and treatments (life sciences, healthcare), orchestrating global logistics networks, managing financial risk (banking and insurance), optimizing battery chemistry (chemicals), or modeling dynamic systems in aerospace, energy, manufacturing, and utilities. In these domains, smarter computation directly translates into faster innovation, lower costs, higher resilience, and improved sustainability.

But to realize the gains, companies need to be ready to master quantum computing (QC), adding it to other capabilities in their analytics arsenal along with machine learning and artificial intelligence. Move too slowly, and they risk slipping into a widening gap behind competitors that develop this advantage faster.

Early movers that embed quantum into their analytics will be able to reset cost bases, speed, and quality in ways that late adopters cannot easily match. Because building quantum capabilities takes three to four years, followers may find themselves structurally behind by the time the technology stabilizes.

CEOs and executives shouldn’t still be asking whether quantum will matter, but rather how to prepare an adaptive three‑year roadmap. Once the technology stabilizes, companies need to be ready to turn quantum potential into business advantage.

Why this second wave of analytics matters

Quantum computing is one of four major quantum technology domains, alongside sensors, cryptography, and communications. Unlike AI and classical analytics, it is not only about processing more data but also about searching through vast numbers of possible combinations to solve optimization problems and simulating complex systems that conventional tools cannot handle efficiently.

Across multiple hardware approaches (superconducting, trapped ion, photonic, neutral atom, topological, quantum dots), progress in error correction and device engineering suggests that systems with around 200 usable logical qubits could be available by 2028 to 2029. Industrialization is likely to accelerate through the 2030s: Quantum processing units (QPUs) will emerge as standard resources, hardware will miniaturize and specialize, and the software and algorithm stack will mature.

As systems with thousands of logical qubits become available (mid 2030s) and error correction scales, quantum computing is expected to move from experimental platforms to integration into enterprise architectures. It will sit alongside data platforms, AI models, and high‑performance computing (HPC), and in some cases may displace parts of today’s GPU‑based workflows. The broader technology stack will evolve rapidly, reshaping from algorithms and analytics to data architecture, deployment models, and integrated software ecosystems.

This doesn’t mean quantum will replace AI. Instead, it will extend the analytical arsenal, bringing a new capability precisely where classical approaches hit their limits. The most advanced organizations will be the first to treat quantum, AI, and HPC as a continuum of tools that can be activated to cover the full spectrum of business analytics performance.

But organizational change occurs more slowly than technology progress. Building the skills and use‑case portfolio, operating model, and infrastructure to exploit quantum at scale can take three to four years. Use cases take six to nine months to develop, from problem framing to mathematical modeling, algorithm tuning, data preparation (including accommodating new formats), computation, and impact assessment.

This creates a synchronization problem for executives, in which there are two clocks running:

• An enterprise-readiness clock that requires years to build capabilities and embed new ways of working
• A technology clock that is accelerating toward 100 logical qubits and beyond

By the time the technology stabilizes at the end of this decade, the gap between early movers and followers may already be locked in.

Quantum isn't another generative AI project

Quantum computing does not follow the same pattern as generative AI. There is no quick proof of concept that can be launched in a few weeks and scaled rapidly across the enterprise.

Our work with Le Lab Quantique (a non-profit organization whose goal is to structure and energize the quantum ecosystem in France and abroad) and leading enterprises shows that transforming quantum potential into business value is a multiyear journey. Each use requires deep collaboration among quantum specialists, data and analytics teams, and business owners, and success depends as much on organizational learning as on hardware access.

Three implications for CEOs and executives stand out:

• Longer learning cycles. Business applications in quantum require a long loop, not a two‑month experiment. Overinvesting too early risks frustration; moving too slowly risks letting competitors build a lead that will be hard to catch up.
• Complementarity with AI. Quantum pilots benchmark against advanced AI and HPC baselines. AI and “proxy quantum” simulations on classical hardware are already used to prototype quantum workflows and understand where quantum actually adds unique value, but enterprises stretch the limit where AI cannot run the computation (speed, compute, energy) and handover to quantum.
• Architecture uncertainty. Key hardware questions—such as how to interconnect QPUs without losing fidelity, who will invest (companies or countries), or how to build practical quantum memory—remain open. However, the direction of travel is clear: The industry is shifting from scientific experimentation to engineering scale‑up.

For executives, this means quantum should be treated as a strategic technology trend with a long build‑up period. It’s too early for massive capex bets but too late to ignore and wait until it is proven technology.

Let use cases lead, not technology

For most organizations, the right entry point is focused experimentation linked to concrete business problems. The first wave of value will come from use cases where quantum can augment existing analytics in optimization, simulation, and complex modeling.

Leading companies share a common approach, blending R&D and operations:

• Start from today’s uncrackable problems, not from quantum technology wish lists—for example, chronic bottlenecks in network routing, portfolio risk, production scheduling, or molecular simulation.
• Rigorously test whether advanced AI and HPC can already solve the problem. Quantum is used where classical methods struggle or are uneconomical at scale.
• Treat each use as a structured experiment, with clear business metrics and decision thresholds for scaling.
• Don’t confine quantum to innovation centers. Bring in operational leaders in manufacturing, logistics, engineering, supply chain, R&D, and commercial functions early as owner of the business problem to resolve.

Talent is a central constraint. Companies need a small core of specialized quantum experts and a broader pool of “quantum‑literate” leaders in data, IT, and operations who can identify relevant problems, interpret results, and integrate quantum into decision processes. This capability cannot be built overnight and requires targeted hiring, training, and partnerships.

Ultimately, the objective is to make quantum just another layer in the analytics stack—used where it makes a difference but invisible elsewhere.

A quantum maturity model

As with other technology initiatives, organizations can assess their readiness and capability using a maturity model (see Figure 1). This gives senior executives and their teams a common framework for tracking progress and aligning investments.

Figure 1

This quantum readiness maturity model considers 10 categories of action across 4 overlapping domains:

• Strategic steering. Define and govern the roadmap, monitor external technology and competitive signposts, synchronize with internal milestones, secure CEO and board commitment, decide on investment posture (early mover vs. fast follower), and use experiments to inform strategic choices.
• Early developments. Build quantum awareness, set up a “Q factory” to design and execute proofs of concept on prioritized use cases, and adapt methods as the technology evolves.
• Leadership readiness. Establish systematic monitoring of technology and competitors, and build partnerships with start‑ups, labs, industrial players, and public programs to secure expertise and compute capacity.
• Skills and infrastructure. Decide how to access quantum infrastructure (build, buy, partner), integrate it with existing systems, including IT, operational technology, AI, and HPC. Maintain and evolve algorithms, identify and train critical skills, and manage change to drive adoption.

For each action category, the model offers specific metrics to gauge progress and development, from a starting point where organizations are not even monitoring an issue, to the goal of having established uses and a clear technology roadmap.

A phased roadmap to avoid falling behind

The key challenge for CEOs is pacing. Moving too fast can burn out resources and credibility before the technology is ready, but moving too slowly risks falling far behind once leaders turn quantum into a real advantage.

A better way to plan is to follow a phased roadmap that identifies key strategic actions and efforts, showing how they unfold in overlapping waves (see Figure 2).

1. Clarify the vision and posture (first year)

1. • Identify where quantum could matter most in your industry and value chain.
   • Decide your ambition level: early mover on a few critical domains, or fast follower with a clear trigger‑based plan.
   • Establish governance: executive sponsorship, cross‑functional steering mechanism, KPIs on learning and impact.

2. Launch targeted experiments and build capabilities (years 1 to 3)

2. • Select three to five high‑potential uses and launch pilots with clear success criteria and timelines.
   • Build the “Q factory”: methods, tools, internal capabilities, and external partnerships needed to run the six- to nine‑month experimentation cycle per use case.
   • Begin integrating quantum into your broader AI and analytics strategy, including infrastructure and talent.

3. Industrialize where quantum proves its value (years 2 to 4 and beyond)

3. • Scale successful pilot uses into production, embedding them in business processes and decision workflows.
   • Extend quantum literacy beyond specialists to business, operations, and finance leaders.
   • Continuously monitor technology signposts and competitor moves to adjust the pace of investment and scaling.

Figure 2

Given the time required to build capabilities and the uncertainty on exact hardware timing, the priority for CEOs is not to make large bets on specific devices but rather to ensure the organization is ready to move fast when quantum becomes economically attractive.

The risk of hesitating

For sectors where quantum is expected to deliver greatest value earlier—healthcare and pharma, financial services, logistics, aerospace, energy, and defense—quantum is poised to become not just a breakthrough technology but also a new operating capability embedded in how business is run.

Experience with previous waves of technology suggests that companies rarely lose because they started preparing too early. They lose because they underestimated how long it would take to build the capabilities and change the organization. Quantum computing may still be approaching its breakthrough moment, but for executives, the preparation phase has clearly begun.

The practical question for CEOs is simple: As AI scales across the business over the next 12 to 24 months, will you view quantum as a future research topic, or as the next analytics breakthrough that could redefine the competitive landscape in your industry?

The window to prepare is open but may not stay that way for long.