In a recent trial, banking giant HSBC reported that using quantum computing alongside its existing systems improved its ability to predict whether it would win a corporate bond trade at a quoted price by up to 34 percent. Around the same time, biopharma firm Moderna applied quantum simulations to segments of mRNA, demonstrating the potential to expand the pool of viable therapeutic candidates beyond classical approaches. These were not laboratory exercises detached from the business. They were early efforts by major companies to test whether quantum computing could improve real trading and drug discovery outcomes. And they did.

These results are only the beginning of a much larger market. Investment bank Jefferies estimates that quantum computing revenues could approach $200 billion by 2040, up from about $1 billion today. Yet most companies are still watching from the sidelines.

Their hesitation is understandable. For years, quantum computing has been described as a breakthrough perpetually five or ten years away, so executives have learned to discount the headlines. But recent advances suggest companies will realize real value from quantum computing sooner than has been estimated. Waiting will also come at a cost that is easy to underestimate. That’s partly because the capabilities that will shape who benefits when quantum systems mature can’t be assembled quickly. It’s also because such work offers immediate benefits.

When companies begin adapting complex planning and forecasting models for quantum computers, they often uncover inefficiencies in their current approaches. In finance, supply chains, and manufacturing, even modest improvements in optimization can translate into measurable gains. Likewise, preparing for quantum-safe encryption forces organizations to inventory sensitive data and modernize aging security systems, thereby reducing exposure to existing cyber threats.

Quantum computing attracts both enthusiasm and skepticism because it is built on a different model of computation. Classical computers process information as bits—0 or 1. Quantum computers use qubits, which can exist in multiple states at once and can be linked together to represent many combinations simultaneously. That doesn’t make them superior for everyday tasks, but for certain problems—especially those that require brute-force computation, such as molecular simulation or complex optimization—quantum computers may eventually handle calculations that become prohibitively time-consuming for classical machines.

Over the past decade, in our research and advisory work across industries—from financial services and energy to manufacturing and telecommunications—we’ve seen an emerging divide: Though most organizations still treat quantum as a distant breakthrough, a smaller group treats the technology as both an immediate operational issue and a long-term strategic opportunity. These companies are beginning to protect their systems against future quantum threats, build their own algorithms, and cultivate the in-house expertise that takes years to develop.

In this article, we outline six steps that leaders can take now—seek early value, develop proprietary quantum algorithms, anticipate the inflection point, integrate quantum and AI, forge alliances, and prepare for quantum-era security. These steps deliver near-term benefits, while positioning companies for the moment when quantum computers become commercially decisive.

Seek Early Value

The first step is to stop treating quantum computing as merely a distant breakthrough and instead identify problems where experimentation is justified today. Current quantum computers are limited. They make errors and can’t yet handle large-scale industrial workloads on their own. But they can be embedded within classical workflows to test new approaches to difficult optimization and risk problems.

Yapi Kredi Bank, for example, explored quantum algorithms to estimate financial risk across its network of small and medium-sized enterprise clients. The objective for the Turkish bank was not to replace its existing systems, but to examine whether quantum techniques could highlight concentrations of risk or structural vulnerabilities that are harder to detect with conventional methods alone. Indeed, even when quantum hardware doesn’t yet outperform classical models outright, Yapi Kredi’s work shows that translating the problem into a quantum framework has the benefit of forcing teams to rethink assumptions and data structures. That process, in turn, sharpens how the bank evaluates risk across its client network.

The same logic applies in other settings where optimization drives results. Companies can use cloud-based access to experiment with quantum and quantum-inspired methods, compare outcomes against established baselines, and determine where “reformulation” changes their perspective. The goal: build a tested portfolio of use cases and internal familiarity, so that improvements in hardware translate quickly into business impact.

Develop Proprietary Quantum Algorithms

The second step is to recognize that the way a problem is framed often matters as much as the tool used to solve it. Quantum computing does not come with ready-made solutions. Its usefulness depends on how a company defines the problem that it wants to address. Those definitions, in turn, reflect deep knowledge of its business.

Using standard algorithms provided by a quantum hardware company may allow a firm to begin testing the technology, but it rarely creates a competitive edge. The more meaningful work happens instead when quantum techniques are adapted to a company’s unique data and assumptions about risk or performance.

Take Airbus. The European aerospace manufacturer explored quantum methods for aircraft loading and routing challenges, problems where careful optimization matters. Doing so meant rethinking how practical limits—such as weight restrictions and scheduling requirements—were described so that quantum systems could work with them. In the process, Airbus’s operational knowledge and constraints became part of how those loading and routing problems were defined, which meant that the approach was built around the company’s specific requirements, rather than a generic template.

Companies that shape their own approaches thus gain familiarity with how their models respond under different computational techniques. That familiarity can reduce reliance on outside providers as quantum capabilities mature. As hardware improves, the algorithms have to be adjusted as well, so companies that experiment early learn how their models behave as the tools change. Recasting complex planning problems in that process often exposes unnecessary complexity or assumptions that went unexamined. In some cases, these adjustments strengthen the classical systems that remain in daily use.

Anticipate the Inflection Point

The third step is to recognize that quantum progress is unlikely to occur evenly. Advances may appear incremental for years. Then, suddenly, performance crosses a threshold and a specific class of problems becomes economically viable. When that happens, the companies able to respond quickly will not be those encountering the technology for the first time. Anticipating the inflection point requires estimating when quantum systems could match or exceed classical performance at comparable cost—something companies can explore using forecasting tools that model different performance and cost assumptions.

BASF, for instance, invested in translating selected industrial problems into forms that quantum systems can handle. Doing this revealed limitations in existing computational models and clarified where they could be improved. As hybrid approaches start to mature, this work has also allowed the German multinational chemical firm to move beyond exploration and demonstrate practical applications. In a recent collaboration, BASF showed that hybrid quantum methods can improve manufacturing and supply-chain optimization, cutting production scheduling times from hours to seconds. Though the company continues to pursue longer-term uses, such as molecular simulation and catalyst design, its near-term focus is on optimization problems where quantum techniques might enhance classical approaches.

Importantly, that groundwork will reside in people and processes, as well as in technology. Companies will need internal teams that understand how their scientific models were built; these teams will also need to understand how quantum results fit into ongoing programs. Organizations without these capabilities will find themselves depending on outside specialists to interpret results or adapt core models while commercial decisions are already in motion. This matters because in industries with long research cycles or complex production environments, revising core models takes considerable time. Whether that preparation has already been done will influence how effectively a company can adapt once quantum computing becomes usable at scale.

Integrate Quantum And AI

The fourth step is to combine quantum computing and AI in ways that draw on what each does well. In a hybrid architecture, quantum capabilities can be inserted selectively into workflows, particularly for optimization or simulation tasks that are too difficult or time-consuming for traditional approaches. Used this way, quantum can improve certain calculations now, while giving teams practical experience that will be useful as the technology develops.

Intesa Sanpaolo explored this approach in fraud detection, while continuing to rely on its established classical machine learning and analytics platforms. The Italian bank tested quantum routines on defined portions of its transaction-monitoring process and compared the results directly with those generated by existing models. Running both approaches side by side made it possible to evaluate differences using the same data and performance standards that were already in place. Even where quantum did not deliver immediate superiority, the comparison exposed gaps in existing models and highlighted areas where assumptions could be refined.

Integrating quantum and AI in this way makes quantum part of day-to-day operations: Rather than treating the technology as a separate initiative, teams incorporate it into existing models and decision processes. As quantum evolves, its role within those workflows is already well understood.

Forge Alliances

The fifth step is to recognize that quantum computing is progressing through shared research and experimentation. Much of this foundational work is taking place in universities and specialized technology firms. Companies that stay engaged in those efforts are better able to judge what is practical and what remains speculative.

Participation doesn’t require large capital commitments. It can involve joint research projects, membership in industry groups, or collaborative testing of defined use cases. The benefit is visibility: Companies gain exposure to technical developments before they reach the headlines, and internal teams learn by working alongside specialists who focus on quantum systems.

BMW, for example, took part in collaborative research initiatives related to materials science and optimization challenges in vehicle design. Through that involvement, the German automaker’s engineers were able to test emerging approaches against real industrial constraints, while retaining control over their own modeling choices. That interaction allowed the company’s internal teams to develop useful intuition about where quantum methods may be relevant and where they’re not.

Engagement of this kind complements an enterprise’s own algorithm and modeling work. It helps the organization refine its understanding of the technology and continue to build proprietary capabilities. Close contact with researchers and industry peers also makes it easier to recruit talent and evaluate progress as it occurs. In a field that’s changing incredibly quickly, sustained involvement provides the kind of perspective that can’t be gained from occasional pilots alone.

Prepare For Quantum-Era Security

The sixth step is to address the security risks that quantum computing presents. Powerful quantum machines are expected to be capable of breaking many of the encryption methods that now protect digital transactions and sensitive data. That moment may still be years away, yet many of those encryption methods would not withstand a powerful quantum computer and are woven into the systems that companies rely on every day.

To be sure, updating encryption methods across an organization is not a small technical change: It affects how customers log in, how data moves between systems, and how companies connect with partners. Such work cuts across departments and vendors. It also requires a clear understanding of where older encryption methods are still in use. That review can also reveal weaknesses—such as outdated settings or inconsistent practices around how encryption keys are handled—that deserve attention even without a quantum threat. Fixing those gaps strengthens security immediately.

Many organizations already benefit, often without realizing it, from cloud providers and open-source projects that are adding post-quantum encryption to their software; these upgrades are starting to flow into enterprise systems. Even so, accountability for protecting their data remains with companies themselves, because only they can update their legacy systems and customized applications, many of which still rely on public-key encryption standards that are expected to be vulnerable to powerful quantum machines.

Some businesses have also begun updating their encryption methods for select services. Telefónica, for example, introduced quantum-resistant encryption into certain enterprise offerings, including secure links between data centers. The Spanish telecoms company tested updated encryption methods alongside existing ones to ensure compatibility before expanding their use. That approach allowed stronger protections to be added without disrupting operations or forcing customers to change systems abruptly.

Preparing for quantum-era security therefore begins with identifying which data must remain confidential for extended periods and where it resides. Information with a long lifespan carries greater exposure, since encrypted data that is stolen today may be decrypted years from now. Acting early strengthens protection now and ensures that the eventual shift to post-quantum security is less disruptive.

Get Ready For Quantum

Quantum computing will not reshape business overnight. Its influence will surface first in specific applications and then expand unevenly—developing proprietary quantum algorithms alone can take years, because most business problems must first be reformulated before they can be expressed in quantum terms. The six steps outlined above are designed for this reality. They focus on actions that strengthen organizations now, while building familiarity with a technology that’s still maturing.

By the time quantum computing commands broad attention, some companies will already enjoy a substantial lead in deploying it. Now’s the moment to make sure your organization is one of them.