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Quantum AI: decoding the hype, risks and the road ahead

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Although quantum AI and its realisation is on the horizon, in many boardrooms the technology, broadly, is not understood. The desire to profit from being first on the scene, however, is driving significant spending.

A recent quantum AI survey from SAS found that three in five business leaders are now exploring or actively investing in the space.

Potential use cases are emerging in high-stakes industries requiring speed, scale and precision, including risk simulation in finance, precision diagnostics in healthcare, and real-time disaster response planning in governments.

Amy Stout, Head of Quantum Product Strategy, and Bill Wisostsky, Principal Quantum Systems Architect, at SAS, provide the scoop on the current quantum conversation; defining quantum AI and the quantum advantage, considering the timeline to a defining quantum moment, and explaining why people should care about this technology.

What is quantum AI?

Amy Stout: Quantum AI is the combination of artificial intelligence and quantum computing, a new type of computation.

Today’s laptops and super computers run on what we call classical computing, and function using binary bits, which can be zero or one. Quantum computers fundamentally work differently. They function using qubits, or quantum bits, which can be 0, 1 or a combination of both at the same time.

It sounds complicated, but basically, tapping into quantum AI can help solve specific types of problems with greater speed and/or accuracy. It’s expected to be most helpful in optimisation, machine learning and molecular modelling, which can impact different industries – like financial services, manufacturing, life sciences and many more.

What is the ‘quantum advantage’?

Bill Wisotsky: In the news, there are constant reports about quantum advantage. These stories typically involve speed, with research showing that a quantum computer could solve a problem in hours that would take conventional computers hundreds of thousands of years. 

These problems are very specific, and designed to demonstrate how quantum computers operate. Though these are important steps in research, they have nothing to do with useful applications for real-world customers. All too often, the media views quantum advantage as one-dimensional, but the quantum advantage isn’t only about speed – it’s multidimensional. 

For example, in quantum machine learning, the advantage could be the ability to encode data into higher-dimensional representations achieved by quantum physics that traditional machine learning can’t, and/or the ability to train models with less data. Quantum advantage could also mean a significant reduction in power usage that quantum computing requires.

This gets to the centre of my argument. When trying to solve applied problems with quantum computing, the quantum advantage needs to be judged multidimensionally, using applied criteria that benefit the business trying to leverage this technology. Yes, it could be about speed, but it could also include many other possibilities.

Are we reaching a quantum inflection point?

Stout: It’s a running joke in the quantum space – quantum is three to five years away, every year. Many experts are trying to be realistic about the state of the market. We don’t want people to come in thinking that quantum AI is going to solve all their problems right now. 

There are multiple types of hardware and multiple vendors that are all developing quantum computers working to achieve the scale, speed and accuracy that will be needed for these computers to provide tangible benefit for real-world, production-sized problems. Quantum has not yet reached that widespread technological maturity.

However, there is already much interest and investment in quantum today, and rightfully so. We’re seeing industry leaders investing in quantum, fully aware that in 2025 they likely won‘t get advancements that impact their bottom lines. What they will get is that first-mover advantage, including in-house expertise and intellectual property, for when the technology is more mature.

I’m an optimist, and I look at hardware providers’ R&D roadmaps and what’s been achieved over the last three to five years and what’s on the horizon for the next three to five years. I think we stand a good chance of seeing these computers able to provide quantum advantage for problems we’d consider low-hanging fruit relatively soon. From there, I hope that we’ll continue seeing examples of the heights we believe quantum AI could achieve. 

Why should people care about quantum?

Wisotsky: Simply, quantum computing could change the world. There are so many use cases, but the two areas I think will be most affected by quantum computing are AI and medicine. As quantum computers get more powerful, and our knowledge on how to use them evolves, AI will be able to take advantage of the physics that quantum computing uses for its computation. 

I think medicine will greatly benefit in the areas of drug discovery and biologics, with researchers gaining the ability to represent and model complex molecular and biological processes in ways that are currently impossible. That could look like researchers discovering better drugs and bringing them to market faster, accelerating processes that would’ve taken a decade of development otherwise.

However, in the future, I don’t think average users will even know they’re using quantum computing to accomplish their goals. I see quantum computing being almost like another accelerator, like all the ‘PUs’ we currently have. Are average users aware that the application they are using is running on a CPU, GPU or NPU? No, they just use the application.

There’s plenty of other editorial on our sister site, Electronic Specifier! Or you can always join in the conversation by visiting our LinkedIn page.





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US health insurance agency to use AI for authorising patient claims; how this may be a problem

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The Centers for Medicare and Medicaid Services (CMS), a federal agency responsible for health insurance services in the US, has announced a new artificial intelligence (AI) based pilot program. A press release issued by the agency states that this AI-powered program will be used to assess the “appropriateness” of certain medical services. According to a report by The New York Times, the program is scheduled to begin in six states by 2026, which will apply prior authorisation to a group of Original Medicare recipients. According to a CMS press release, the AI algorithms will be used to ensure that care recipients are not receiving “wasteful, inappropriate services.” The pilot program aims to target these services in Original Medicare, a process that is already common for those with Medicare Advantage.As per the report, similar AI-based algorithms like these have already faced litigation, adding that the AI companies involved “would have a strong financial incentive to deny claims.” The new pilot has even been described as an “AI death panels” program by the report.

What the agency said about this AI-based program

In the press release, CMS wrote: “The Centers for Medicare & Medicaid Services (CMS) is announcing a new Innovation Center model aimed at helping ensure people with Original Medicare receive safe, effective, and necessary care.Through the Wasteful and Inappropriate Service Reduction (WISeR) Model, CMS will partner with companies specializing in enhanced technologies to test ways to provide an improved and expedited prior authorization process relative to Original Medicare’s existing processes, helping patients and providers avoid unnecessary or inappropriate care and safeguarding federal taxpayer dollars.The WISeR Model will test a new process on whether enhanced technologies, including artificial intelligence (AI), can expedite the prior authorization processes for select items and services that have been identified as particularly vulnerable to fraud, waste, and abuse, or inappropriate use.”

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Understanding Ghanaian STEM Students’ AI Learning Intentions

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In recent years, the field of education has undergone a remarkable transformation, particularly with the rise of technology and artificial intelligence (AI). Amidst this evolution, a pressing question emerges: how do we foster an environment conducive for students to embrace AI technology, particularly in the context of Ghana? The research conducted by Abreh, Arthur, Akwetey, and their colleagues aims to unravel this very question, delving deep into STEM students’ intentions to learn about AI through a comprehensive modeling approach utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy set Qualitative Comparative Analysis (fsQCA).

The study focuses primarily on Ghana’s educational landscape, where the integration of AI into the curriculum presents new opportunities as well as challenges. The authors argue that understanding the factors influencing students’ intention to learn AI is crucial for policymakers and educators aiming to enhance the educational experience and job readiness of future generations. In an era defined by digital progression, an examination of student motivations and aspirations is not only relevant but essential in shaping the future of education in Ghana and beyond.

By employing the PLS-SEM approach, the researchers parsed through various dimensions, including individual characteristics, social influences, and perceived educational effectiveness, to determine how these factors impact students’ willingness to engage with AI. The data generated by this method offers a robust mechanism to visualize complex interrelations that traditional research methods might overlook. Importantly, PLS-SEM serves as a powerful tool to facilitate an understanding of both direct and indirect influences on students’ learning intentions.

In conjunction with PLS-SEM, the application of fsQCA provided an innovative lens through which to evaluate the heterogeneous nature of student populations. This method recognizes that varying combinations of factors can lead to the same outcome—in this case, the intention to learn AI. The researchers found that while certain commonalities existed among students, unique pathways also emerged depending on individual backgrounds, learning environments, and available resources. This nuanced understanding allows educators to craft tailored interventions that meet diverse learner needs.

Ghana’s demographic landscape presents both advantages and hurdles in increasing students’ interest in AI. The nation is youthful, with a significant percentage of the population being students. Capitalizing on this demographic dividend requires systematic educational reforms that align with the global demand for AI competency. By showcasing the vast potentials of AI, classrooms can become incubators for innovation where students are not only passive recipients of knowledge but active creators of technology.

The research highlights that students often struggle with understanding what AI entails and its relevance to their future careers. There is a gap between theoretical knowledge and practical application. To address this divide, educational institutions must incorporate hands-on learning experiences that engage students with real-world AI applications. Workshops, internships, and collaborative projects could serve as catalysts for interest and excitement in AI studies.

Moreover, the role of peer influence cannot be understated. The study underscores the importance of social interactions in shaping attitudes toward learning AI. Mentorship programs and peer-led initiatives can provide a supportive atmosphere wherein students encourage one another to delve deeper into AI topics. Creating a collaborative rather than competitive learning environment enhances motivation and retention of knowledge.

Further, the researchers found that exposure to technology and AI-related content significantly boosts students’ intentions to learn. Integrating AI concepts across various disciplines—be it economics, healthcare, or environmental science—can broaden students’ perspectives and demonstrate the interdisciplinary applications of AI. Students should be able to see AI not just as a tool but as a transformative force that can solve complex problems in diverse fields.

The findings of this study also resonate beyond Ghana, highlighting the global need to assess students’ readiness to embrace emerging technologies. Countries grappling with similar educational challenges can adopt and adapt the models presented in this research. As we move into a future increasingly dominated by AI, educational methodologies must evolve to prepare students not only to consume technology but to innovate and lead in this field.

To ensure these educational reforms are sustainable, government support and investment are imperative. Stakeholders must collaborate to provide the necessary funding, infrastructure, and resources for educational institutions to thrive in the AI domain. Encouraging partnerships between academia, industry, and government can lead to synergies that enhance learning outcomes and pave the way for a skilled workforce equipped for the challenges of the 21st century.

Importantly, the study’s implications extend to teacher training programs as well. Educators themselves must be well-versed in AI technologies and methodologies to effectively teach their students. Professional development opportunities focused on AI can empower teachers, enabling them to inspire and guide students as they explore new territories in technology.

In essence, this research encapsulates a vital exploration of factors influencing students’ intentions to engage with AI in Ghana’s educational space. By employing advanced modeling techniques and reflecting on the complexities of various student experiences, the authors provide valuable insights that can inform effective teaching practices and policies. As AI continues to reshape the world, the educational approaches guided by this research may well serve as stepping stones toward a future where students are not only consumers of technology but innovative contributors to an AI-driven world.

Subject of Research: Intention of STEM Students to Learn Artificial Intelligence in Ghana

Article Title: Modelling STEM students’ intention to learn artificial intelligence (AI) in Ghana: a PLS-SEM and fsQCA approach

Article References:

Abreh, M.K., Arthur, F., Akwetey, F.A. et al. Modelling STEM students’ intention to learn artificial intelligence (AI) in Ghana: a PLS-SEM and fsQCA approach.
Discov Artif Intell 5, 223 (2025). https://doi.org/10.1007/s44163-025-00466-8

Image Credits: AI Generated

DOI: 10.1007/s44163-025-00466-8

Keywords: Artificial Intelligence, Education, STEM, Learning Intentions, Ghana, PLS-SEM, fsQCA, Student Engagement, Educational Reform, Technology Integration, Teacher Training, Peer Influence, Interdisciplinary Learning.

Tags: AI integration in curriculumAI learning intentionsdigital progression in educationeducational transformation in Ghanaenhancing job readinessfactors influencing AI learningfuture of education in Ghanafuzzy set qualitative analysisGhanaian STEM educationPLS-SEM methodologystudent motivation for AItechnology in education



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Globevisa CEO Unveils its AI Strategy, Transforming Traditional Services Into a Tech-Driven Powerhouse

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— In a decisive move set to redefine the future of service industries, Globevisa Group CEO and co-founder Henry Fan has launched a groundbreaking artificial intelligence (AI) transformation strategy that embeds AI at the core of the company’s operations. This bold initiative is not only improving efficiency and customer service but also positioning Globevisa as a global innovator in tech-driven business leadership.

Rather than relegating AI to a standalone IT function, Henry established an in-house AI Empowerment Center—a “special forces” unit that reports directly to him. This reflects his belief that AI is a business-wide opportunity, not a departmental add-on. As the architect of this transformation, Henry serves as strategist, change agent, and internal evangelist, overseeing a company-wide shift in how AI is deployed, adopted, and embraced.

The Strategist: Defining a Clear Vision for AI

Henry’s leadership begins with a clear vision for an “AI-driven Globevisa,” which he positions as the company’s North Star. This vision guides every decision, from budget allocation to the selection of core technologies. Henry ensures that AI efforts are tightly aligned with Globevisa’s business objectives, such as revenue growth, operational efficiency, and brand enhancement.

His approach is pragmatic and phased, focusing on high-value pilot projects before scaling up. He champions a “showcase to full coverage” strategy, quickly demonstrating tangible results in areas like marketing, customer service, and human resources. By tying AI initiatives directly to measurable business outcomes, such as reducing document processing times, increasing content production efficiency, or improving sales conversion rates, Henry ensures that Globevisa’s AI efforts are not just theoretical but practical and impactful.

Tackling Operational Inefficiencies with AI

Henry’s journey into AI began with a recognition of inefficiencies in the company’s internal processes, which were bogged down by repetitive, manual tasks. He saw AI not as a buzzword but as a tool to address these core operational challenges.

1.Document Processing

Globevisa’s success involves processing countless client documents, such as bank statements and passports, a task prone to human error and delays. To combat this, Henry spearheaded the development of an AI document extraction and auditing tool. This technology scans documents, extracts key information, and cross-checks it against system requirements, significantly reducing manual review time and errors. The result is faster, more accurate processing, enabling the team to handle a higher volume of clients.

2.Customer Service

Globevisa’s customer service team was overwhelmed by repetitive inquiries, leaving little time for complex, high-value interactions. Henry’s team introduced a 24/7 AI-powered chatbot capable of handling up to 80% of standard queries. This freed human staff to focus on nuanced, emotional, and complex client concerns, enhancing overall customer satisfaction.

3.Marketing Content Creation

The process of generating marketing content was slow and often lacked variety. Henry addressed this by deploying an “AI Content Factory” that generates blog posts, social media updates, and ad copy from simple keywords. This tool dramatically increased content production efficiency while reducing costs, ensuring Globevisa remains competitive in its digital marketing efforts.

The Breaker of Barriers: Overcoming Organizational Challenges

While implementing AI solutions, Henry quickly realized that the biggest obstacles were not technological but organizational and cultural. Resistance to change, data silos, and fears of job displacement were among the challenges he faced.

1.Breaking Data Silos

With 110,000 successful cases in hand, Globevisa sits on a treasury of data. However, many departments at Globevisa operated in isolation, hoarding data and refusing to share it. For instance, the AI team often needed years of sales data to train models, but obtaining access required navigating internal politics. Henry personally stepped in as a “Breaker of Barriers,” reframing data-sharing as an investment in the company’s future rather than a threat. He emphasized that AI would provide departments with sharper tools to achieve their goals, fostering a spirit of collaboration.

2.Addressing Job Displacement Fears

Employees, particularly senior staff such as copywriters, were initially hostile toward AI, viewing it as a potential replacement for their roles. Henry tackled this by redefining their positions and elevating their value. He assured employees that AI would handle 80% of mundane tasks, allowing them to focus on the remaining 20% of creative, high-value work. Copywriters, for example, were rebranded as “AI Creative Strategists” and “Final Quality Controllers,” responsible for refining and overseeing AI-generated drafts. This reframing not only eased fears but also inspired employees to embrace AI as a tool for professional growth.

3.Adjusting KPIs to Reward Adoption

In traditional service industries, departments often cling to outdated KPIs, which can hinder the adoption of new technologies. Henry addressed this head-on by personally revising performance metrics for teams involved in AI pilots. For example, customer service teams previously measured on “calls handled per hour” were now evaluated on metrics like “complex problem-solving rates” and “customer satisfaction scores.” This ensured that employees were rewarded for adopting new behaviors, not for sticking to inefficient practices.

The Chief Evangelist: Fostering a Culture of Innovation

Henry understands that technology alone cannot drive change; it requires a cultural shift. As Globevisa’s “Chief Evangelist,” he regularly communicates the importance of AI initiatives through all-hands meetings, internal newsletters, and personal demonstrations. By openly using AI tools, such as leveraging AI for meeting summaries, he leads by example, fostering a company-wide culture of innovation.

His leadership style is characterized by inclusivity and transparency. Instead of imposing top-down mandates, he actively involves employees in the transformation process, ensuring that AI is seen not as a threat but as an enabler. This human-centric approach has been instrumental in building trust and enthusiasm for AI across the organization.

A Model for the Future of Services

Henry’s approach provides a replicable roadmap for other service-based companies navigating digital transformation. His model centers on measurable value, cultural readiness, and human-AI collaboration, proving that even traditional industries can lead in a tech-first economy.

About Globevisa

Globevisa Group is a global leader in immigration and relocation services, with over 110,000 successful cases worldwide. Committed to service excellence and innovation, the company helps individuals and families navigate complex immigration processes with confidence.

Contact Info:
Name: Lena Huang
Email: Send Email
Organization: GLOBEVISA GROUP (HONG KONG) LIMITED
Website: https://www.globevisa.com/

Release ID: 89168612

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