Quantum AI combines machine learning with existing quantum hardware to solve complex problems faster and more efficiently.
Quantum computing has long been viewed as a futuristic technology that is still years away from practical adoption in enterprises. Although experts expect fully mature quantum hardware to be production-ready in the early 2030s, more organizations are already exploring how they can benefit from today’s quantum capabilities through an emerging field known as quantum AI.
Quantum AI combines machine learning with existing quantum hardware to solve complex problems faster and more efficiently than traditional computing alone. This approach is increasingly being positioned as a bridge between today’s classical computing systems and the more powerful quantum future that many industries expect.
New research from SAS shows that while interest in quantum AI remains high across global industries, organizations are becoming more realistic about where and how they invest.
The company surveyed more than 500 global business and technology leaders to understand the state of quantum AI adoption. While high implementation costs were at the top of the list of barriers in 2025, the latest findings in 2026 reveal that uncertainty about real-world applications is now the top concern.
The study found that the main barriers to quantum AI adoption in 2026 include uncertainty around practical real-world use cases, high implementation costs, lack of trained talent, limited understanding of the technology, limited availability of quantum AI solutions, and lack of clear regulatory guidance.
This shift suggests that organizations are no longer only concerned about the cost of quantum initiatives. Instead, many are asking more fundamental questions. What business problems can quantum AI solve today?
“Organizations of all sizes are working hard to develop intellectual property – unique patented approaches to quantum AI – so they are ready as the technology matures,” he said. bill wysotsky. “Despite continued strong interest, it’s understandable that leaders are proceeding cautiously, unwilling to commit to expensive quantum investments for fear they won’t lead to valuable use cases or problems solved. SAS is committed to leveling the playing field, establishing real-world use cases today and ensuring our customers can capture a piece of the quantum pie tomorrow.”
SAS describes quantum computing and classical computing as parts of a spectrum rather than competing technologies. In many scenarios, the most effective approaches may involve hybrid systems where classical computing handles standard workloads and quantum systems tackle highly complex optimization or simulation tasks.
This hybrid model is already gaining traction across sectors that process large datasets or require rapid decision-making. Potential applications include fraud detection in financial services, 5G network traffic optimization, supply chain logistics, predictive customer analytics, and accelerating drug discovery.
Survey respondents also highlighted interest in using quantum AI to improve the efficiency of large-scale language model training and natural language processing. This field continues to gain momentum as organizations increase their investments in generative AI.
To lower barriers to entry, SAS announced plans to launch SAS Quantum Lab for customers using the SAS Viya platform later this year.
“This research reveals what SAS experts have already seen in the market: Leaders are excited about leveraging quantum, but the barriers to entry are too high and solutions are needed,” he said. amy stout. “SAS is excited to give you a sneak peek of SAS Quantum Lab, a hands-on playground for learning and innovating with real-world ROI.”
This initiative is designed as a hands-on environment where organizations can explore, test, and validate quantum AI concepts without requiring deep expertise in quantum physics.
SAS Quantum Lab allows users to compare traditional, quantum, and hybrid computing results side-by-side for industry use cases, and is expected to help organizations determine the best solution to a specific business challenge. The platform also includes performance-enhancing features, with current testing showing over 100x speed increases and up to 99% cost savings, the company said. In addition, the platform will feature a virtual quantum AI tutor who can answer questions, provide sample code, and recommend next steps for users considering the technology.
The survey also revealed growing interest in applying quantum AI across a wide range of industries and business functions. Respondents said they want to use this technology to improve fraud detection systems in financial services by more efficiently identifying complex transaction patterns, optimize 5G network traffic in real time, accelerate molecular simulation and drug discovery processes, improve supply chain distribution and logistics operations, enhance predictive modeling of customer behavior, and reduce the time and resources needed to train large-scale language models for natural language processing tasks.
“If you are ready to explore quantum AI, we are ready to work with you,” Wisotsky added. “Bring your ideas and our experts will help you determine if and how quantum AI can be incorporated in a way that is worthwhile, safe, and smart.”
The findings confirm broader trends across the technology industry. Although quantum computing may still be years away from full maturity, companies are now beginning to position themselves for what many believe could be the next big computing game changer.
