Is enterprise technology on the cusp of quantum AI?

As the enterprise technology stack and its corresponding supply chain functions now align to (soon, we hope) support quantum hardware in a stabilised state, discussion is still open as to when this still-emerging technology will be popularised and production-ready.

Could it be by the early 2030s?

If so, some assume that means benefitting from the “quantum now” of today is somewhat out of the question.

How about quantum AI?

Also essentially still embryonic, quantum AI is an approach involving running machine learning algorithms on existing quantum hardware to run tasks in hours (instead of weeks) and/or work on rendering problems that were once considered impossible to realise on existing hardware.

It can also look like calibrating models to learn efficiently on less data, bolstering stability over time.

If this technology story is playing out thus, then who’s involved and what happens next? Data and AI platform company SAS surveyed more than 500 global leaders across industries on quantum AI. In the first instalment of the survey in 2025, the high cost of implementation ranked as the number one barrier to adoption, followed by a lack of understanding or knowledge.

That’s changed in 2026.

Barriers to quantum AI

The greatest barriers to quantum AI adoption in 2026 ranked as follows among survey respondents.

Uncertainty around practical, real-world uses.
High cost of implementation.
Lack of trained personnel.
Lack of knowledge or understanding.
Limited availability of quantum AI solutions.
Lack of clear regulatory guidelines.

SAS looks at classical and quantum computing as a spectrum: with proven classical computing on one end… and experimental and exponentially more powerful quantum computing on the other. Many industry and business problems fall somewhere in the middle, with a hybrid approach splitting workloads: quantum processing and classical processing each doing what they do best.

“Organisations of all sizes are eager to develop intellectual property – their original, patented approach to quantum AI – so they’ll be ready as the technology comes of age,” said Bill Wisotsky, principal quantum architect at SAS. “Despite continued strong interest, leaders are understandably proceeding with caution, and they don’t want to go all-in on expensive quantum investments they fear may not result in worthwhile use cases and solved problems. SAS is working to level the playing field, establishing real-world use cases for today, and ensuring that customers can get a piece of the quantum pie tomorrow.”

As interest in quantum AI accelerates, Scotiabank is taking an evidence‑based approach by running classical and quantum methods side by side to understand where quantum delivers real value today.

Through the Scotiabank-SAS Innovation Lab, the bank is exploring quantum computing in the context of consumer bankruptcy prediction, a complex, highly regulated risk modeling problem. Using anonymized client profile data, Scotiabank is developing a challenger bankruptcy score in SAS Viya, then benchmarking it against approaches that incorporate quantum techniques.

The initiative was designed to test practical outcomes, not theory, and answer the question: where do classical models remain the best option, and where might quantum help address combinatorial complexity and optimisation challenges?

Prepare for the quantum economy?

“This survey illuminates what SAS experts were already seeing in the market: that leaders are excited to use quantum, but the barriers to entry have been too high, and that requires a solution,” said Amy Stout, Head of Quantum Product Strategy at SAS. “SAS is excited to give a sneak peek of SAS Quantum Lab, a hands-on playground to learn and innovate for real-world ROI.”

Coming in Q4 to SAS Viya customers, Stout explains that SAS Quantum Lab is designed to be a complement to quantum experts on their existing work and to empower users who may not be quantum physicists, but are ready to explore, test and validate their ideas. It significantly reduces the cost of quantum AI exploration and helps customers avoid false signals, all while exploring this powerful technology efficiently and credibly.

SAS Quantum Lab is currently being designed to include the following: the ability to compare, side-by-side, classical, quantum and hybrid results for industry use cases, letting users find the best solutions for their business problems; performance-boosting capabilities, with current testing showing more than 100 times speedup and 99% cost savings; and a virtual quantum AI tutor to accelerate learning by answering questions, offering sample code and suggesting next steps.

At the conclusion of the survey, respondents had the option to answer a write-in question: if they were currently working on quantum, what use cases did they hope to achieve, or what business problem would they like to solve?

Responses included the following.

To enhance the accuracy of fraud detection systems in financial services, enabling more efficient identification of complex transaction patterns.
To optimise 5G network path traffic in real-time.
To accelerate molecular simulation and the drug discovery process for new therapeutic candidates.
For supply chain distribution and to optimise logistics problems.
To improve machine learning workflows with a focus on predictive modelling for customer behaviour.
To train large language models for natural language processing tasks, reducing the time and resources for model optimisation.

SAS’s Wisotsky concludes by saying to software engineers and data scientists, if they’re ready to explore quantum AI, the SAS team is ready to work with engineers and businesspeople alike, in ways that are valuable, safe and sensible.”