A Majority (81%) believe they have reached the limits of classical computing’s capabilities for optimization

53% are planning to build quantum computing into their workflows and 27% are considering doing so, indicating a growing recognition of quantum computing’s real-world business value

PALO ALTO, Calif., July 21, 2025–(BUSINESS WIRE)–D-Wave Quantum Inc. (NYSE: QBTS) (“D-Wave” or the “Company”), a leader in quantum computing systems, software, and services, and the world’s first commercial supplier of quantum computers, announced the results of a new study today that highlights the potential for quantum optimization to create value across industries. According to the study, 46% of surveyed business leaders whose company has implemented quantum optimization or plans to do so within the next two years expect a return on investment of between $1 and $5 million, while 27% predict a return of more than $5 million in the first 12 months.

The findings are detailed in a new report published today by D-Wave in collaboration with Wakefield Research, a leading market research and thought leadership firm. In May 2025, Wakefield Research surveyed 400 business leaders from select countries in North America, Europe and Asia Pacific (APAC), the majority of whom are familiar with quantum computing and who make decisions about optimization technologies.

A majority of the business leaders surveyed (81%) believe that they have reached the limit of the benefits they can achieve through optimization solutions running on classical computers. Against that backdrop, many are starting to explore whether quantum technologies can help. 53% are planning to build quantum computing into their workflows and 27% are considering doing so, indicating a growing recognition of quantum computing’s real-world business value. Close to a quarter (22%) are seeing quantum make a significant impact for those who have adopted it, while another 50% anticipate it will be disruptive for their industry.

Quantum computing: an emerging “game changer” for optimization

The results of the study show that quantum computing is gaining recognition among business leaders for its ability to potentially deliver major efficiencies in addressing complex optimization problems and operational improvements. Three in five (60%) respondents expect quantum computing-based optimization to be very or extremely helpful in solving the specific operational challenges that their companies face. In fact, among those respondents most familiar with quantum, this figure rises to 73%, including nearly a quarter who describe it as “a game changer.” The areas in which business leaders expect to benefit from an investment in quantum optimization include: supply chain and logistics (50%), manufacturing (38%), planning and inventory (36%), and research and development (36%).

Organizations are starting to make organizational changes designed to generate future value from gen AI, and large companies are leading the way. The latest McKinsey Global Survey on AI finds that organizations are beginning to take steps that drive bottom-line impact—for example, redesigning workflows as they deploy gen AI and putting senior leaders in critical roles, such as overseeing AI governance. The findings also show that organizations are working to mitigate a growing set of gen-AI-related risks and are hiring for new AI-related roles while they retrain employees to participate in AI deployment. Companies with at least $500 million in annual revenue are changing more quickly than smaller organizations. Overall, the use of AI—that is, gen AI as well as analytical AI—continues to build momentum: More than three-quarters of respondents now say that their organizations use AI in at least one business function. The use of gen AI in particular is rapidly increasing.

How companies are organizing their gen AI deployment—and who’s in charge

Our survey analyses show that a CEO’s oversight of AI governance—that is, the policies, processes, and technology necessary to develop and deploy AI systems responsibly—is one element most correlated with higher self-reported bottom-line impact from an organization’s gen AI use. That’s particularly true at larger companies, where CEO oversight is the element with the most impact on EBIT attributable to gen AI. Twenty-eight percent of respondents whose organizations use AI report that their CEO is responsible for overseeing AI governance, though the share is smaller at larger organizations with $500 million or more in annual revenues, and 17 percent say AI governance is overseen by their board of directors. In many cases, AI governance is jointly owned: On average, respondents report that two leaders are in charge.

The value of AI comes from rewiring how companies run, and the latest survey shows that, out of 25 attributes tested for organizations of all sizes, the redesign of workflows has the biggest effect on an organization’s ability to see EBIT impact from its use of gen AI. Organizations are beginning to reshape their workflows as they deploy gen AI. Twenty-one percent of respondents reporting gen AI use by their organizations say their organizations have fundamentally redesigned at least some workflows.

Organizations are selectively centralizing elements of their AI deployment

The survey findings also shed light on how organizations are structuring their AI deployment efforts. Some essential elements for deploying AI tend to be fully or partially centralized (Exhibit 1). For risk and compliance, as well as data governance, organizations often use a fully centralized model such as a center of excellence. For tech talent and adoption of AI solutions, on the other hand, respondents most often report using a hybrid or partially centralized model, with some resources handled centrally and others distributed across functions or business units—though respondents at organizations with less than $500 million in annual revenues are more likely than others to report fully centralizing these elements.

Organizations vary widely in how they monitor gen AI outputs

Organizations have employees overseeing the quality of gen AI outputs, though the extent of that oversight varies widely. Twenty-seven percent of respondents whose organizations use gen AI say that employees review all content created by gen AI before it is used—for example, before a customer sees a chatbot’s response or before an AI-generated image is used in marketing materials (Exhibit 2). A similar share says that 20 percent or less of gen-AI-produced content is checked before use. Respondents working in business, legal, and other professional services are much more likely than those in other industries to say that all outputs are reviewed.

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Organizations are addressing more gen-AI-related risks

Many organizations are ramping up their efforts to mitigate gen-AI-related risks. Respondents are more likely than in early 2024 to say their organizations are actively managing risks related to inaccuracy, cybersecurity, and intellectual property infringement (Exhibit 3)—three of the gen-AI-related risks that respondents most commonly say have caused negative consequences for their organizations.

Respondents at larger organizations report mitigating more risks than respondents from other organizations do. They are much more likely than others to say their organizations are managing potential cybersecurity and privacy risks, for example, but they are not any more likely to be addressing risks relating to the accuracy or explainability of AI outputs.

Best practices for adoption and scaling can enable value, and companies are beginning to follow them

Most respondents have yet to see organization-wide, bottom-line impact from gen AI use—and most aren’t yet implementing the adoption and scaling practices that we know from earlier research help create value when deploying new technologies. In a complementary survey in a set of developed markets, only 1 percent of company executives describe their gen AI rollouts as “mature.” Even though these remain early days for deployment, we are beginning to see the impact when these practices are employed to capture value.

We asked respondents about 12 adoption- and scaling-related practices for gen AI and found that there are positive correlations on EBIT impact from each. The one with the most impact on the bottom line is tracking well-defined KPIs for gen AI solutions, while at larger organizations, establishing a clearly defined road map to drive adoption of gen AI also has one of the biggest impacts.

Overall, companies are in the early stages of putting these practices in place. So far, less than one-third of respondents report that their organizations are following most of the 12 adoption and scaling practices, with less than one in five saying their organizations are tracking KPIs for gen AI solutions. Respondents working for larger organizations are more likely to report using at least some of these practices (Exhibit 4). Those at larger organizations, for example, are more than twice as likely as their small-company peers to say their organizations have established clearly defined road maps to drive adoption of gen AI solutions (such as through phased rollouts across teams and business units) and to have established a dedicated team (such as a project management or transformation office) to drive gen AI adoption. Responses show larger organizations are also ahead on building awareness and momentum through internal communications about the value created by gen AI solutions, creating role-based capability training courses to make sure employees at each level know how to use gen AI capabilities appropriately, and having comprehensive approaches to foster trust among customers in their use of gen AI.

AI is shifting the skills that organizations need

This survey also examines the state of AI-related hiring and other ways AI affects the workforce. Respondents working for organizations that use AI are about as likely as they were in the early 2024 survey to say their organizations hired individuals for AI-related roles in the past 12 months. The only roles that differ this year are data-visualization and design specialists, which respondents are significantly less likely than in the previous survey to report hiring. The findings also indicate several new risk-related roles that are becoming part of organizations’ AI deployment processes. Thirteen percent of respondents say their organizations have hired AI compliance specialists, and 6 percent report hiring AI ethics specialists. Respondents at larger companies are more likely than their peers at smaller organizations to report hiring a broad range of AI-related roles, with the largest gaps seen in hiring AI data scientists, machine learning engineers, and data engineers.

Respondents continue to see these roles as largely challenging to fill, though a smaller share of respondents than in the past two years describe hiring for many roles as “difficult” or “very difficult” (Exhibit 5). One exception is AI data scientists, who will continue to be in high demand in the year ahead: Half of respondents whose organizations use AI say their employers will need more data scientists than they have now.

Many respondents also say that their organizations have reskilled portions of their workforces as part of their AI deployment over the past year and that they expect to undertake more reskilling in the years ahead (Exhibit 6).

Our latest survey also shows how organizations are managing the time saved by their deployment of gen AI. Respondents most often report that employees are spending the time saved via automation on entirely new activities. They also often say that employees are spending more time on existing responsibilities that have not been automated. Respondents at larger organizations, however, are more likely than others to say their organizations have reduced the number of employees as a result of time saved. Our analyses find that head count reductions are one of the organizational attributes with the largest impact on bottom-line value realized from gen AI.

Overall, though, a plurality of respondents (38 percent) whose organizations use AI predict that use of gen AI will have little effect on the size of their organization’s workforce in the next three years. Looking at expectations by industry, survey respondents working in financial services are the only ones much more likely to expect a workforce reduction than no change. The findings show that C-level executives’ expectations for the workforce impact of gen AI are not significantly different from those of senior managers and midlevel managers. That said, when it comes to the head count impact of AI—including gen AI and analytical AI—C-level executives are more likely than middle managers to predict increasing head count.

Looking at the expected effects of gen AI deployment by business function, respondents most often predict decreasing head count in service operations, such as customer care and field services, as well as in supply chain and inventory management (Exhibit 7). In IT and product development, however, respondents are more likely to expect increasing than decreasing head count.

AI use continues to climb

Reported use of AI increased in 2024. In the latest survey, 78 percent of respondents say their organizations use AI in at least one business function, up from 72 percent in early 2024 and 55 percent a year earlier (Exhibit 8). Respondents most often report using the technology in the IT and marketing and sales functions, followed by service operations. The business function that saw the largest increase in AI use in the past six months is IT, where the share of respondents reporting AI use jumped from 27 percent to 36 percent.

Organizations are also using AI in more business functions than in the previous State of AI survey. For the first time, most survey respondents report the use of AI in more than one business function (Exhibit 9). Responses show organizations using AI in an average of three business functions—an increase from early 2024, but still a minority of functions.

The use of gen AI has seen a similar jump since early 2024: 71 percent of respondents say their organizations regularly use gen AI in at least one business function, up from 65 percent in early 2024. (Individuals’ use of gen AI has also grown. See sidebar, “C-level executives are using gen AI more than others.”) Responses show that organizations are most often using gen AI in marketing and sales, product and service development, service operations, and software engineering—business functions where gen AI deployment would likely generate the most value, according to previous McKinsey research—as well as in IT.

C-level executives are using gen AI more than others Individual use of gen AI by our respondents also increased significantly in 2024, with C-level executives leading the way (exhibit). Fifty-three percent of surveyed executives say they are regularly using gen AI at work, compared with 44 percent of midlevel managers. While we see variation in individuals’ use of gen AI across industries and regions, the data largely show widening use across the board.

While organizations in all sectors are most likely to use gen AI in marketing and sales, deployment within other functions varies greatly according to industry (Exhibit 10). Organizations are applying the technology where it can generate the most value—for example, service operations for media and telecommunication companies, software engineering for technology companies, and knowledge management for professional-services organizations. Gen AI deployment also varies by company size. Responses show that companies with more than $500 million in annual revenues are using gen AI throughout more of their organizations than smaller companies are.

Most respondents reporting use of gen AI—63 percent—say that their organizations are using gen AI to create text outputs, but organizations are also experimenting with other modalities. More than one-third of respondents say their organizations are generating images, and more than one-quarter use it to create computer code (Exhibit 11). Respondents in the technology sector report the widest range of gen AI outputs, while respondents in advanced industries (such as automotive, aerospace, and semiconductors) are more likely than others to use gen AI to create images and audio.

An increasing share of respondents report value creation within the business units using gen AI. Compared with early 2024, larger shares of respondents say that their organizations’ gen AI use cases have increased revenue within the business units deploying them (Exhibit 12). Respondents report similar revenue increases from gen AI as they did from analytical AI activities in the previous survey. This emphasizes the need for companies to have a comprehensive approach across both AI and gen AI solutions to capture the full potential value.

Overall, respondents are also more likely than in the previous survey to say they are seeing meaningful cost reductions within the business units using gen AI (Exhibit 13).

Exhibit 13

Yet gen AI’s reported effects on bottom-line impact are not yet material at the enterprise-wide level. More than 80 percent of respondents say their organizations aren’t seeing a tangible impact on enterprise-level EBIT from their use of gen AI.

Organizations have been experimenting with gen AI tools. Use continues to surge, but from a value capture standpoint, these are still early days—few are experiencing meaningful bottom-line impacts. Larger companies are doing more organizationally to help realize that value. They invest more heavily in AI talent. They mitigate more gen-AI-related risks. We have seen organizations move since early last year, and the technology also continues to evolve, with a view toward agentic AI as the next frontier for AI innovation. It will be interesting to see what happens when more companies begin to follow the road map for successful gen AI implementation in 2025 and beyond.

About the research

The online survey was in the field from July 16 to July 31, 2024, and garnered responses from 1,491 participants in 101 nations representing the full range of regions, industries, company sizes, functional specialties, and tenures. Forty-two percent of respondents say they work for organizations with more than $500 million in annual revenues. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

D-Wave Quantum Inc. (NYSE: QBTS) has announced the results of a new study highlighting business leaders’ expectations for return on investment (ROI) from quantum optimization. Conducted by Wakefield Research on behalf of D-Wave, the study found that among surveyed business leaders whose companies are implementing quantum optimization or planning to do so within the next two years, 27% predict an ROI of more than $5 million, and 46% anticipate between $1 million and $5 million within the first 12 months of adoption.

The study, which surveyed 400 business leaders across North America, Europe, and Asia Pacific in May 2025, indicates that 81% believe they have reached the limits of optimization solutions running on classical computers. In response, 19% of respondents have integrated quantum computing into their workflows, with an additional 53% planning to adopt the technology. Of those who have already adopted quantum, 22% report a notable impact, while 50% anticipate the technology will affect their industry. Adoption rates vary by market, with 25% in North America having deployed quantum computing, 42% in Europe planning adoption within two years, and 34% in APAC planning adoption within two years.

Respondents indicate that quantum computing-based optimization is expected to contribute to solving operational challenges, with 60% anticipating it to be very or extremely helpful. This figure increases to 73% among those most familiar with quantum technology. Expected benefits from quantum optimization include supply chain and logistics (50%), manufacturing (38%), planning and inventory (36%), and research and development (36%). Key hurdles to improving optimization capabilities include reliance on outdated technology (39%), budgetary restrictions (38%), dependence on classical optimization (36%), and staffing or skill gaps (35%).

The findings suggest a growing recognition among business leaders of quantum computing’s potential for operational improvements and addressing complex optimization problems. D-Wave CEO Dr. Alan Baratz commented on the increasing interest in applying quantum to complex optimization use cases. The study’s results highlight industries’ push to adopt new optimization tools to overcome conventional computational limitations.

Read the full press release here and download the full Wakefield Research report here.

July 21, 2025