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SAS announced at the SAS Innovate global data and AI conference that the world’s top enterprises are on the “quantum AI cusp”. Many experts projected that quantum hardware would not be production-ready until the early 2030s, but recent data reveals that leaders are no longer waiting. Instead, by using current quantum hardware to execute sophisticated machine learning algorithms, a potent new technique called “quantum AI” is enabling businesses to benefit right away.
The release coincides with a thorough assessment that SAS undertook to determine the preparedness of over 500 worldwide leaders in a variety of businesses for the quantum economy. The results show a sharp change in the difficulties the industry faces. The high implementation costs were the main barrier to adoption in 2025, closely followed by a lack of internal knowledge. But as of April 2026, the priorities have changed, and the biggest obstacle is now a lack of knowledge on useful, real-world applications for the technology.
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The Barrier Shift: From Cost to Utility
The biggest obstacles presently keeping businesses from going “all-in” on quantum AI, according to the 2026 poll results, are ambiguity about practical applications, high implementation costs, and a shortage of qualified staff. The restricted availability of current quantum AI technologies, a general lack of awareness, and unclear regulatory constraints complete the top six obstacles.
According to SAS Principal Quantum Architect Bill Wisotsky, companies are keen to create their own unique, patented methods for quantum AI to be prepared as the technology advances. He did, however, stress that leaders are moving cautiously because they are concerned that costly expenditures would not provide useful use cases or solutions to challenges. SAS is concentrating on “leveling the playing field” to allay these worries by creating quick, practical use cases that let clients get their “piece of the quantum pie” right now.
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Defining the Quantum AI Spectrum
According to SAS, technology is like a spectrum, with experimental, exponentially more powerful quantum computing at one end and proven classical computing at the other. The majority of contemporary business issues fall somewhere in the middle of this range, requiring a hybrid strategy that divides workloads between quantum and conventional computing to take advantage of each’s special advantages.
In particular, quantum AI helps businesses solve problems that were previously thought to be unsolvable on current hardware or do processes that used to take hours in only minutes. To improve long-term stability, this method can also be used to calibrate models to learn effectively with less data.
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A New Launchpad: SAS Quantum Lab
SAS released a “sneak peek” at their forthcoming SAS Quantum Lab in an effort to close the gap between curiosity and implementation. Although executives are enthusiastic about the possibilities of quantum, the obstacles to entrance have just been too great, according to Amy Stout, Head of Quantum Product Strategy at SAS. For SAS Viya clients, the SAS Quantum Lab is set to open in the fourth quarter. It is intended to be a practical “playground” where users may experiment and learn.
In addition to enabling non-physicists to investigate and confirm their theories, the Lab is meant to support the work of quantum specialists. Among the Lab’s primary attributes are:
- Side-by-Side Comparisons: To choose the best option for their company, users can compare the results of classical, quantum, and hybrid approaches for certain industry use cases.
- Performance and Savings: Up to 99% cost savings and a speedup of over 100 times have been demonstrated in recent testing of the Lab’s capabilities.
- Virtual Quantum AI Tutor: A virtual instructor will be on hand to respond to inquiries, provide example code, and advise users on what to do next to speed up the learning curve.
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Industry-Specific Innovations
Leaders were also able to send in answers to the question about the particular business issues they hope to resolve with quantum AI. Several important sectors were covered by the responses:
- Finance: Leaders want to improve fraud detection systems by more effectively recognizing intricate transaction patterns.
- Telecommunications: Optimizing 5G network path traffic in real-time is quite popular.
- Health and Life Sciences: Organizations want to find novel treatments more quickly by using molecular simulation and drug discovery.
- Supply Chain: Complex logistics and distribution issues are being investigated using quantum AI.
- Machine Learning and NLP: Improving predictive modeling for consumer behavior and lowering the resources needed to train large language models (LLMs) are among the objectives of machine learning and natural language processing.
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A Legacy of Innovation
At SAS Innovate, SAS celebrated its 50th anniversary, the announcement was a highlight. The conference highlights SAS’s commitment to turning data into trusted decisions with Microsoft, Intel, and AWS support. SAS is advising companies equipped to use quantum AI safely, wisely, and effectively as the quantum hardware supply chain stabilizes. Wisotsky continued, “We’re ready to work with you if you’re ready to explore quantum AI.”
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