Enterprises want quantum AI that delivers measurable ROI
SAS, the analytics software firm, released survey findings showing increased enterprise interest in quantum AI use cases that provide tangible business value rather than technology for technology’s sake. The company polled more than 500 global decision-makers across sectors including financial services, telecommunications, healthcare and logistics. The results indicate a shift: organizations are moving from curiosity about quantum capabilities toward a desire for real-world applications and proof points that justify investment.
Barriers persist, but priorities are changing
SAS said that in its 2026 survey respondents ranked uncertainty over practical uses as the top barrier to adopting quantum AI, overtaking high implementation cost, which topped the list in 2025. Other hurdles cited include a shortage of trained personnel, gaps in general understanding, limited availability of ready-made quantum AI solutions and unclear regulatory guidance.
Those responses reflect a familiar pattern in emerging enterprise technologies: early adopters and R&D teams drive capability development, but broader rollouts require clear, industry-specific case studies and predictable total cost of ownership. For many companies, the calculus has shifted from whether quantum computing will one day be transformative to whether quantum-enhanced machine learning can deliver measurable improvements on current problems.
What SAS is offering: Quantum Lab for practical experimentation
To address those concerns, SAS previewed SAS Quantum Lab at its SAS Innovate conference. The product, slated to be available to SAS Viya customers in Q4, is positioned as a practical sandbox where data scientists and business teams can compare classical, quantum and hybrid approaches on the same use cases.
Key features described by SAS include side-by-side comparisons of solution performance, tools to reduce exploratory cost and a virtual tutor to accelerate learning. The company also cited internal testing that suggested significant performance and cost advantages for certain workloads, noting examples of more than 100 times speedups and up to 99 percent cost savings. Those figures were presented as test results rather than validated industry benchmarks.
In SAS’s framing, quantum AI is not a replacement for classical computing but part of a continuum in which hybrid architectures split workloads so that each platform plays to its strengths. That model echoes a broader industry view that near-term quantum advantage will often be realized through targeted hybrid workflows rather than monolithic quantum-only systems.
Industry use cases and practical expectations
Survey respondents identified a range of near-term priorities where quantum AI could provide value. Common themes included:
- Improving fraud detection in financial services by uncovering complex transactional patterns.
- Optimizing 5G network traffic routing in real time to boost throughput and reduce latency.
- Accelerating molecular simulations to shorten drug discovery timelines.
- Solving large-scale supply chain and logistics optimization problems.
- Enhancing ML workflows for predictive modeling, including more efficient training of large language models.
These use cases align with areas where algorithmic improvements and specialized compute can produce quantifiable business outcomes. Still, experts caution that achieving consistent, production-grade gains will require careful problem selection, benchmarking against classical baselines and integration with existing data pipelines.
Implications for CIOs, procurement and regulators
For enterprise technology leaders, the survey underscores three practical priorities. First, pilot programs should be framed around clear KPIs and comparisons to classical methods, to avoid exploratory projects that are hard to scale. Second, workforce development remains a bottleneck: organizations will need to blend quantum specialists with existing data science teams and invest in upskilling. Third, procurement and vendor evaluation should place emphasis on reproducible benchmarks and transparent cost models.
On policy and standards, the survey’s mention of regulatory uncertainty points to another emerging need. As quantum-accelerated models touch regulated domains such as finance and healthcare, compliance frameworks will need to adapt to new risk profiles, including model explainability and supply-chain provenance for quantum hardware and toolchains.
Context: hardware progress and realistic timelines
Hardware suppliers and supply chains for quantum processors have been improving, but most industry forecasts still place broadly applicable, fault-tolerant quantum machines several years out. Many vendors and researchers therefore emphasize hybrid quantum-classical approaches and targeted algorithmic advances that can be executed on today’s noisy intermediate-scale quantum (NISQ) devices or simulated quantum environments.
SAS’s approach reflects that pragmatic posture: provide a structured environment to experiment and validate whether quantum techniques can reduce compute time, lower data requirements or unlock new solution avenues for specific business problems. That model may appeal to enterprises unwilling to commit to large hardware investments before obtaining clear operational benefits.
Bottom line
The SAS survey suggests enterprise interest in quantum AI is maturing from exploratory curiosity to a focus on measurable outcomes. SAS Quantum Lab aims to lower the cost and complexity of early experimentation and to provide comparative evidence for decision-makers. For enterprises, the near-term task will be separating use cases with realistic upside from speculative promises and building the hybrid talent and governance structures needed to scale successful pilots.
Disclosure: SAS presented the survey and product preview at its SAS Innovate event. Company-provided performance claims are based on internal testing and should be evaluated with independent benchmarking in enterprise deployments.
