AI Research
Quantum machine learning (QML) is closer than you think: Why business leaders should start paying attention now

The enterprise technology landscape is witnessing a remarkable shift. While most discussions around quantum computing focus on distant breakthroughs and theoretical applications, a quiet revolution is happening at the intersection of quantum systems and machine learning. Quantum machine learning (QML) is transitioning from academic curiosity to a practical business tool, and the timeline for enterprise adoption may be shorter than many anticipate.
The quantum advantage: Beyond classical limitations
To truly appreciate how QML is evolving, and how those changes might end up having a huge impact on business technology, it is important to first understand how it differs from current forms of computing. Traditional computers process information in binary states, using ones and zeros. Quantum computers, however, operate on quantum bits (qubits) that can exist in multiple states simultaneously through a phenomenon called superposition. This fundamental difference enables quantum systems to process complex, interdependent variables at scales and speeds that classical machines simply cannot match.
While current quantum hardware still faces significant limitations — including error rates, decoherence, and the need for extreme cooling — consistent progress in quantum simulation and optimization is confirming the technology’s transformative potential. The key insight is that quantum systems don’t need to be perfect to be useful; they need to be better than classical alternatives for specific problem sets.
Why QML matters: Unlocking new performance frontiers
The rapid growth of AI has played a key role in unlocking the potential of QML because it has created a foundation for the technology to be integrated into existing models. QML represents a hybrid approach that combines quantum circuits with classical machine learning models to unlock performance improvements in targeted, data-intensive domains. This isn’t about replacing classical AI wholesale; it’s about identifying specific use cases where quantum advantages can be leveraged within existing enterprise AI workflows.
Early-stage experimentation across industries is already demonstrating measurable improvements:
- Accelerated training: Complex models that typically require extensive computational resources can be trained more efficiently using quantum-enhanced algorithms, reducing both time-to-insight and energy consumption.
- High-dimensional data handling: Quantum systems excel at processing datasets with many variables and sparse data points, scenarios where classical methods often struggle or require significant preprocessing.
- Enhanced accuracy with limited data: QML can achieve greater model accuracy with smaller sample sizes, particularly valuable in regulated industries or specialized domains where data is scarce or expensive to obtain.
The timeline is shortening: From theory to practice
One of the most compelling aspects of QML is how well its inherently probabilistic nature aligns with modern generative AI and uncertainty modeling. Just as classical computing advanced despite early hardware imperfections, current-generation quantum systems are producing measurable results in narrow but high-value use cases.
The progression mirrors the early days of cloud computing or AI: initial skepticism gave way to pilot projects, which demonstrated clear value in specific applications, ultimately leading to widespread enterprise adoption. Today’s quantum systems may be imperfect, but they’re becoming increasingly consistent in delivering advantages for well-defined problem sets.
What enterprises can do today: Practical entry points
Organizations don’t need to wait for quantum hardware perfection to begin exploring value. Several practical entry points offer immediate opportunities for experimentation and learning:
- Risk scenario simulation: Financial services and insurance companies can use quantum systems to simulate rare or complex risk scenarios that are computationally intensive for classical systems. This includes stress testing portfolios under extreme market conditions or modeling catastrophic insurance events.
- Enhanced forecasting: Quantum-inspired sampling techniques can improve forecasting accuracy and sensitivity analysis, particularly for supply chain optimization, demand planning, and resource allocation.
- Synthetic data generation: In heavily regulated industries or data-scarce environments, QML can generate high-quality synthetic datasets that preserve statistical properties while ensuring compliance with privacy regulations.
- Anomaly detection: Quantum systems excel at identifying subtle patterns and anomalies in complex datasets, particularly valuable for fraud detection, cybersecurity, and quality control applications.
- Specialized industry applications: Early adopters are finding success in claims forecasting, patient risk stratification, drug efficacy modeling, and portfolio optimization — areas where the quantum advantage directly translates to business value.
Building quantum readiness: Strategic considerations
For enterprise leaders considering QML adoption, the focus should be on building organizational readiness rather than waiting for perfect technology. This means investing in quantum literacy across technical teams, identifying use cases where quantum advantages align with business priorities, and developing partnerships with quantum computing providers and research institutions.
The talent dimension is particularly critical. Organizations that begin developing quantum expertise today will have significant advantages as the ecosystem matures, whether they pursue projects by training existing data scientists or recruiting quantum-aware talent. This isn’t just about understanding quantum mechanics; it’s about recognizing how quantum capabilities can be integrated into existing AI and data science workflows.
The enterprise imperative: Early movers’ advantage
QML is no longer confined to research laboratories. It’s becoming a tool with real strategic potential, offering competitive advantages for organizations willing to invest in early-stage experimentation. The companies that begin building quantum capabilities today — starting with awareness, progressing to experimentation, and developing internal expertise — will be best positioned to capitalize on the technology as it continues to mature.
The question isn’t whether QML will impact enterprise AI, but rather when and how. Organizations that treat quantum computing as a distant future technology risk being left behind by competitors who recognize its emerging practical value. The time for quantum awareness and preparation is now.
As we’ve learned from previous technology transitions, the companies that lead aren’t always those with the most resources; they’re the ones that recognize inflection points earliest and act decisively. For QML, that inflection point is approaching faster than most expect.
Learn more about EXL’s data and AI capabilities here.
Anand “Andy” Logani is executive vice president and chief digital and AI officer at EXL, a global data and AI company.
AI Research
Researchers used AI to design the perfect phishing plot, what happened next shocked everyone

AI is increasingly being put to the test for its potential benefits, but a new experiment has shown how the same technology can also fuel online crime. A Reuters investigation, conducted in partnership with Harvard researcher Fred Heiding, has revealed that some of the world’s most widely used AI chatbots can be nudged into producing scam emails aimed at senior citizens.
In a controlled study, emails generated by these bots were sent to more than 100 elderly volunteers in the United States. While no money or personal data was taken, the results were troubling. About 11 per cent of the participants clicked on the links inside the phishing emails, suggesting that AI-generated scams can be as persuasive as those crafted by humans.
The fake charity experiment with Grok
The investigation began with a test on Grok, the chatbot developed by Elon Musk’s company xAI. Reporters asked it to create a message for older readers about a charity called the “Silver Hearts Foundation”. The mail looked convincing, speaking about dignity for seniors and urging them to join the mission. Without further prompting, Grok even added a line to create urgency: “Click now to act before it’s too late.” The charity did not exist, the entire email was designed to trick recipients.
Phishing: a growing global threat
Phishing, where people are deceived into revealing sensitive information or sending money, is one of the biggest challenges in cybersecurity. According to FBI figures, it is the most reported cybercrime in the US, and older people are among the worst affected. In 2023 alone, Americans over 60 lost nearly $5 billion to such fraud. The agency has also warned that generative AI tools can make these scams more effective and harder to detect.
Chatbots tested beyond Grok
The Reuters team went beyond Grok and tested five other major chatbots – OpenAI’s ChatGPT, Meta’s AI assistant, Google’s Gemini, Anthropic’s Claude and DeepSeek. Initially, most of them refused to generate phishing content. But with slight changes in the way requests were worded, such as describing the exercise as academic research or fiction writing, the chatbots eventually produced scam-like drafts.
Why AI makes scams easier
Heiding, who has studied phishing techniques for years, said this flexibility makes chatbots “potentially valuable partners in crime”. Unlike humans, they can generate dozens of variations instantly, helping criminals cut costs and scale up operations. In fact, Heiding’s earlier research showed that phishing emails written by AI could be just as effective in luring targets as those created manually.
When tested on seniors, five out of nine AI-generated mails resulted in clicks. Two came from Grok, two from Meta AI and one from Claude. None of the volunteers responded to ChatGPT or DeepSeek’s drafts. But the study was not intended to rank which chatbot is more dangerous, rather to show that several can be exploited for scams.
Tech firms acknowledge risks
Technology companies have acknowledged the concerns. Meta said it invests in safeguards to prevent misuse and regularly stress-tests its systems. Anthropic stated that using its chatbot Claude for scams violates its policies and accounts found misusing the tool are suspended. Google said it retrained Gemini after learning it had generated phishing content, while OpenAI has publicly admitted in past reports that its models can be misused for “social engineering”.
Security experts believe the issue lies in how companies balance user experience with safety. Chatbots are designed to be helpful, but stricter refusals could drive users towards rival products with fewer restrictions. This trade-off, researchers argue, creates room for misuse.
The problem is not confined to experiments. Survivors of scam operations in Southeast Asia told Reuters that they had been forced to use ChatGPT in real-world fraud schemes. Workers at such centres reportedly used the bot to polish responses, translate messages and build trust with victims.
Governments and regulators respond
Governments are beginning to take note. Some US states have passed laws against AI-generated fraud, though most target scammers themselves rather than the companies providing the technology. The FBI, in a recent alert, said criminals are now able to “commit fraud on a larger scale” because AI reduces the time and effort required to make scams believable.
– Ends
AI Research
SEERai™ by Galorath Wins SiliconANGLE TechForward Award with Industry-First Agentic Artificial Intelligence
SEERai Recognized as the Industry’s First Agentic AI Platform Transforming Cost, Schedule, and Risk Planning in Secure Enterprise Environments
LONG BEACH, Calif., Sept. 16, 2025 /PRNewswire/ — Galorath, the premier AI-powered operational intelligence platform provider, today announced that SEERai™ has been named a winner in SiliconANGLE’s 2025 TechForward Awards. The platform was recognized in the “AI Tech – Generative AI & Foundation Models” category for its impact in enabling secure, explainable AI-driven planning across complex programs.
SEERai is the first commercially available agentic AI platform engineered for program-critical outcomes. Unlike generic AI copilots or disconnected estimation tools, SEERai uses a modular architecture of purpose-built agents, retrieval-augmented generation (RAG), and structured decision logic to deliver fully traceable outputs. It enables organizations to accelerate proposal timelines, standardize estimation practices, and scale expert insight—without compromising accuracy, auditability, or security.
“Being recognized by SiliconANGLE is a testament to Galorath’s ongoing commitment to innovation and impact,” said Charles Orlando, Chief Strategy Officer, Galorath Incorporated. “With rising costs, constrained budgets, and outdated tools testing the limits of traditional project planning, SEERai delivers an agentic AI solution that replaces static assumptions with accuracy, agility, and confidence.”
The TechForward Awards recognize the technologies and solutions driving business forward. As the trusted voice of enterprise and emerging tech, SiliconANGLE applies a rigorous editorial lens to highlight innovations reshaping how businesses operate in our rapidly changing landscape. As organizations face pressures to deliver projects faster, reduce costs, and improve outcomes across increasingly complex environments, traditional tools and approaches often fail to adapt to real-time changes, leaving teams struggling with inefficiencies, risks, and misalignment. Galorath’s award-winning SEERai solution is pioneering the future of AI for cost estimation, project planning, and risk management.
“These winners represent the most impressive achievements emerging from today’s fiercely competitive tech landscape, embodying the relentless drive and visionary thinking that pushes entire industries forward,” said John Furrier, co-founder and co-CEO of SiliconANGLE Media. “These are the solutions that business leaders trust to solve their most critical challenges. They’re not just products, they’re competitive advantages.”
The TechForward awards program honors both established enterprise solutions and breakthrough technologies defining the future of business, spanning AI innovation, security excellence, cloud transformation, data platform evolution and blockchain/crypto tech. SEERai was selected from a competitive field of nominees by a panel of industry experts and technology leaders. The complete list of winners can be found online at https://siliconangle.com/awards/.
About SiliconANGLE Media
SiliconANGLE Media is a recognized leader in digital media innovation, bringing together cutting-edge technology, influential content, strategic insights and real-time audience engagement. As the parent company of SiliconANGLE, theCUBE Network, theCUBE Research, CUBE365, theCUBE AI and theCUBE SuperStudios — such as those established in Silicon Valley and the New York Stock Exchange (NYSE) — SiliconANGLE Media transforms the way technology companies connect with their target markets. Founded by tech visionaries John Furrier and Dave Vellante, SiliconANGLE Media has built a powerful ecosystem of industry-leading digital media brands, with a reach of 10+ million elite tech professionals, 4+ million SiliconANGLE readers and 250,000+ social media subscribers. The company’s new, proprietary theCUBE AI LLM is breaking ground in audience interaction, leveraging CUBE365’s neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations.
About SEER® and SEERai
Galorath’s flagship project estimating software, SEER®, offers unparalleled capabilities in project cost forecasting, risk mitigation, and actionable insights, making it the go-to platform for project cost planning for hardware and software development, systems engineering, aerospace, and manufacturing companies. SEERai is Galorath’s modular agentic AI platform for estimation, sourcing, labor, schedule, and risk, standing out as a first-of-its-kind generative AI for digital engineering support. Combining its connection with the knowledge bases of SEER, along with secure, isolated integration of an organization’s backend systems, processes, databases, and projects, SEERai allows cost and project estimation professionals to use natural language to instantly generate actionable information and data for project and cost estimation, from Work Breakdown Structures (WBS) to project and cost estimation guidance and much more. For more information, visit https://galorath.com/ai.
About Galorath Incorporated
Leveraging four decades of in-market experience and success, Galorath transforms cost, scheduling, should-cost analysis, and project estimation, optimizing outcomes and achieving unparalleled efficiencies for public and private sector organizations worldwide. SEER®, Galorath’s flagship digital engineering platform, is trusted by industry giants like Accenture, NASA, Boeing, the U.S. Department of Defense, and BAE Systems (EU). SEER accelerates time to market, dramatically enhances project predictability and visibility, and ensures project costs are on track and on budget. For more information, visit https://galorath.com/.
All trademarks are the property of their respective owners. |
SOURCE Galorath
AI Research
Lila Sciences raises $235 million to expand AI-driven research platform | Pharmaceutical | The Pharmaletter

Lila Sciences has secured $235 million in series A financing, co-led by Braidwell and Collective Global, at a valuation of about $1.23 billion. The Massachusetts-based company, founded by Flagship Pioneering in 2023, is building an artificial intelligence platform designed to automate and accelerate the scientific method across multiple disciplines.
The latest financing follows a $200-million seed round in March and will be used to hire staff and open new sites in Boston, San Francisco and London. These locations will house the company’s so-called AI Science Factories, facilities that integrate AI, robotics and laboratory systems to design and run experiments at scale. Lila says these factories have already conducted hundreds of thousands of studies across life science, chemistry and materials science.
Building autonomous science at scale
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