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Decoding the Future of AI: From Disruption to Democratisation and Beyond
The global AI landscape has become a melting pot for innovation, with diverse thinking pushing the boundaries of what is possible. Its application extends beyond just technology, reshaping traditional business models and redefining how enterprises, governments, and societies operate. Advancements in model architectures, training techniques and the proliferation of open-source tools are lowering barriers to entry, enabling organisations of all sizes to develop competitive AI solutions with significantly fewer resources. As a result, the long-standing notion that AI leadership is reserved for entities with vast computational and financial resources is being challenged. This shift is also redrawing the global AI power balance, with a decentralised approach to AI where competition and collaboration coexist across different regions. As AI development becomes more distributed, investment strategies, enterprise innovation and global technological leadership are being reshaped. However, established AI powerhouses still wield significant leverage, driving an intense competitive cycle of rapid innovation. Amid this acceleration, it is critical to distinguish true technological breakthroughs from over-hyped narratives, adopting a measured, data-driven approach that balances innovation with demonstrable business value and robust ethical AI guardrails. Implications of the Evolving AI Landscape The democratisation of AI advancements, intensifying competitive pressures, the critical need for efficiency and sustainability, evolving geopolitical dynamics and the global race for skilled talent are all fuelling the development of AI worldwide. These dynamics are paving the way for a global balance of technological leadership. Democratisation of AI Potential The ability to develop competitive AI models at lower costs is not only broadening participation but also reshaping how AI is created, deployed and controlled. Open-source AI fosters innovation by enabling startups, researchers, and enterprises to collaborate and iterate rapidly, leading to diverse applications across industries. For example, xAI has made a significant move in the tech world by open sourcing its Grok AI chatbot model, potentially accelerating the democratisation of AI and fostering innovation. However, greater accessibility can also introduce challenges, including risks of misuse, uneven governance, and concerns over intellectual property. Additionally, as companies strategically leverage open-source AI to influence market dynamics, questions arise about the evolving balance between open innovation and proprietary control. Increased Competitive Pressure The AI industry is fuelled by a relentless drive to stay ahead of the competition, a pressure felt equally by Big Tech and startups. This is accelerating the release of new AI services, as companies strive to meet growing consumer demand for intelligent solutions. The risk of market disruption is significant; those who lag, face being eclipsed by more agile players. To survive and thrive, differentiation is paramount. Companies are laser-focused on developing unique AI capabilities and applications, creating a marketplace where constant adaptation and strategic innovation are crucial for success. Resource Optimisation and Sustainability The trend toward accessible AI necessitates resource optimisation, which means developing models with significantly less computational power, energy consumption and training data. This is not just about cost; it is crucial for sustainability. Training large AI models is energy-intensive; for example, training GPT-3, a 175-billion-parameter model, is believed to have consumed 1,287 MWh of electricity, equivalent to an average American household’s use over 120 years1. This drives innovation in model compression, transfer learning, and specialised hardware, like NVIDIA’s TensorRT. Small language models (SLMs) are a key development, offering comparable performance to larger models with drastically reduced resource needs. This makes them ideal for edge devices and resource-constrained environments, furthering both accessibility and sustainability across the AI lifecycle. Multifaceted Global AI Landscape The global AI landscape is increasingly defined by regional strengths and priorities. The US, with its strength in cloud infrastructure and software ecosystem, leads in “short-chain innovation”, rapidly translating AI research into commercial products. Meanwhile, China excels in “long-chain innovation”, deeply integrating AI into its extended manufacturing and industrial processes. Europe prioritises ethical, open and collaborative AI, while the APAC counterparts showcase a diversity of approaches. Underlying these regional variations is a shared trajectory for the evolution of AI, increasingly guided by principles of responsible AI: encompassing ethics, sustainability and open innovation, although the specific implementations and stages of advancement differ across regions. The Critical Talent Factor The evolving AI landscape necessitates a skilled workforce. Demand for professionals with expertise in AI and machine learning, data analysis, and related fields is rapidly increasing. This creates a talent gap that businesses must address through upskilling and reskilling initiatives. For example, Microsoft has launched an AI Skills Initiative, including free coursework and a grant program, to help individuals and organisations globally develop generative AI skills. What does this mean for today’s enterprise? New Business Horizons AI is no longer just an efficiency tool; it is a catalyst for entirely new business models. Enterprises that rethink their value propositions through AI-driven specialisation will unlock niche opportunities and reshape industries. In financial services, for example, AI is fundamentally transforming operations, risk management, customer interactions, and product development, leading to new levels of efficiency, personalisation and innovation. Navigating AI Integration and Adoption Integrating AI is not just about deployment; it is about ensuring enterprises are structurally prepared. Legacy IT architectures, fragmented data ecosystems and rigid workflows can hinder the full potential of AI. Organisations must invest in cloud scalability, intelligent automation and agile operating models to make AI a seamless extension of their business. Equally critical is ensuring workforce readiness, which involves strategically embedding AI literacy across all organisational functions and proactively reskilling talent to collaborate effectively with intelligent systems. Embracing Responsible AI Ethical considerations, data security and privacy are no longer afterthoughts but are becoming key differentiators. Organisations that embed responsible AI principles at the core of their strategy, rather than treating them as compliance check boxes, will build stronger customer trust and long-term resilience. This requires proactive bias mitigation, explainable AI frameworks, robust data governance and continuous monitoring for potential risks. Call to Action: Embracing a Balanced Approach The AI revolution is underway. It demands a balanced and proactive response. Enterprises must invest in their talent and reskilling initiatives to bridge the AI skills gap, modernise their infrastructure to support AI integration and scalability and embed responsible AI principles at the core of their strategy, ensuring fairness, transparency and accountability. Simultaneously, researchers must continue to push the boundaries of AI’s potential while prioritising energy efficiency and minimising environmental impact; policymakers must create frameworks that foster responsible innovation and sustainable growth. This necessitates combining innovative research with practical enterprise applications and a steadfast commitment to ethical and sustainable AI principles. The rapid evolution of AI presents both an imperative and an opportunity. The next chapter of AI will be defined by those who harness its potential responsibly while balancing technological progress with real-world impact. Resources Sudhir Pai: Executive Vice President and Chief Technology & Innovation Officer, Global Financial Services, Capgemini Professor Aleks Subic: Vice-Chancellor and Chief Executive, Aston University, Birmingham, UK Alexeis Garcia Perez: Professor of Digital Business & Society, Aston University, Birmingham, UK Gareth Wilson: Executive Vice President | Global Banking Industry Lead, Capgemini 1 https://www.datacenterdynamics.com/en/news/researchers-claim-they-can-cut-ai-training-energy-demands-by-75/?itm_source=Bibblio&itm_campaign=Bibblio-related&itm_medium=Bibblio-article-related
Virtual reality training tool helps nurses learn patient-centered care
University of Delaware computer science students have developed a digital interface as a two-way system that can help nurse trainees build their communication skills and learn to provide patient-centered care across a variety of situations. This virtual reality training tool would enable users to rehearse their bedside manner with expectant mothers before ever encountering a pregnant patient in person. The digital platform was created by students in Assistant Professor Leila Barmaki’s Human-Computer Interaction Laboratory, including senior Rana Tuncer, a computer science major, and sophomore Gael Lucero-Palacios. Lucero-Palacios said the training helps aspiring nurses practice more difficult and sensitive conversations they might have with patients. "Our tool is targeted to midwifery patients,” Lucero-Palacios said. “Learners can practice these conversations in a safe environment. It’s multilingual, too. We currently offer English or Turkish, and we’re working on a Spanish demo.” This type of judgement-free rehearsal environment has the potential to remove language barriers to care, with the ability to change the language capabilities of an avatar. For instance, the idea is that on one interface the “practitioner” could speak in one language, but it would be heard on the other interface in the patient’s native language. The patient avatar also can be customized to resemble different health stages and populations to provide learners a varied experience. Last December, Tuncer took the project on the road, piloting the virtual reality training program for faculty members in the Department of Midwifery at Ankara University in Ankara, Turkey. With technical support provided by Lucero-Palacios back in the United States, she was able to run a demo with the Ankara team, showcasing the UD-developed system’s interactive rehearsal environment’s capabilities. Last winter, University of Delaware senior Rana Tuncer (left), a computer science major, piloted the virtual reality training program for Neslihan Yilmaz Sezer (right), associate professor in the Department of Midwifery, Ankara University in Ankara, Turkey. Meanwhile, for Tuncer, Lucero-Palacios and the other students involved in the Human-Computer Interaction Laboratory, developing the VR training tool offered the opportunity to enhance their computer science, data science and artificial intelligence skills outside the classroom. “There were lots of interesting hurdles to overcome, like figuring out a lip-sync tool to match the words to the avatar’s mouth movements and figuring out server connections and how to get the languages to switch and translate properly,” Tuncer said. Lucero-Palacios was fascinated with developing text-to-speech capabilities and the ability to use technology to impact patient care. “If a nurse is well-equipped to answer difficult questions, then that helps the patient,” said Lucero-Palacios. The project is an ongoing research effort in the Barmaki lab that has involved many students. Significant developments occurred during the summer of 2024 when undergraduate researchers Tuncer and Lucero-Palacios contributed to the project through funding support from the National Science Foundation (NSF). However, work began before and continued well beyond that summer, involving many students over time. UD senior Gavin Caulfield provided foundational support to developing the program’s virtual environment and contributed to development of the text-to-speech/speech-to-text capabilities. CIS doctoral students Fahim Abrar and Behdokht Kiafar, along with Pinar Kullu, a postdoctoral fellow in the lab, used multimodal data collection and analytics to quantify the participant experience. “Interestingly, we found that participants showed more positive emotions in response to patient vulnerabilities and concerns,” said Kiafar. The work builds on previous research Barmaki, an assistant professor of computer and information sciences and resident faculty member in the Data Science Institute, completed with colleagues at New Jersey Institute of Technology and University of Central Florida in an NSF-funded project focused on empathy training for healthcare professionals using a virtual elderly patient. In the project, Barmaki employed machine learning tools to analyze a nursing trainee’s body language, gaze, verbal and nonverbal interactions to capture micro-expressions (facial expressions), and the presence or absence of empathy. “There is a huge gap in communication when it comes to caregivers working in geriatric care and maternal fetal medicine,” said Barmaki. “Both disciplines have high turnover and challenges with lack of caregiver attention to delicate situations.” UD senior Rana Tuncer (center) met with faculty members Neslihan Yilmaz Sezer (left) and Menekse Nazli Aker (right) of Ankara University in Ankara, Turkey, to educate them about the virtual reality training tool she and her student colleagues have developed to enhance patient-centered care skills for health care professionals. When these human-human interactions go wrong, for whatever reason, it can extend beyond a single patient visit. For instance, a pregnant woman who has a negative health care experience might decide not to continue routine pregnancy care. Beyond the project’s potential to improve health care professional field readiness, Barmaki was keen to note the benefits of real-world workforce development for her students. “Perceptions still exist that computer scientists work in isolation with their computers and rarely interact, but this is not true,” Barmaki said, pointing to the multi-faceted team members involved in this project. “Teamwork is very important. We have a nice culture in our lab where people feel comfortable asking their peers or more established students for help.” Barmaki also pointed to the potential application of these types of training environments, enabled by virtual reality, artificial intelligence and natural language processing, beyond health care. With the framework in place, she said, the idea could be adapted for other types of training involving human-human interaction, say in education, cybersecurity, even in emerging technology such as artificial intelligence (AI). Keeping people at the center of any design or application of this work is critical, particularly as uses for AI continue to expand. “As data scientists, we see things as spreadsheets and numbers in our work, but it’s important to remember that the data is coming from humans,” Barmaki said. While this project leverages computer vision and AI as a teaching tool for nursing assistants, Barmaki explained this type of system can also be used to train AI and to enable more responsible technologies down the road. She gave the example of using AI to study empathic interactions between humans and to recognize empathy. “This is the most important area where I’m trying to close the loop, in terms of responsible AI or more empathy-enabled AI,” Barmaki said. “There is a whole area of research exploring ways to make AI more natural, but we can’t work in a vacuum; we must consider the human interactions to design a good AI system.” Asked whether she has concerns about the future of artificial intelligence, Barmaki was positive. “I believe AI holds great promise for the future, and, right now, its benefits outweigh the risks,” she said.
NASA Grant Funds Research Exploring Methods of Training Vision-Based Autonomous Systems
Conducting research at 5:30 a.m. may not be everybody’s first choice. But for Siddhartha Bhattacharyya and Ph.D. students Mohammed Abdul, Hafeez Khan and Parth Ganeriwala, it’s an essential part of the process for their latest endeavor. Bhattacharyya and his students are developing a more efficient framework for creating and evaluating image-based machine learning classification models for autonomous systems, such as those guiding cars and aircraft. That process involves creating new datasets with taxiway and runway images for vision-based autonomous aircraft. Just as humans need textbooks to fuel their learning, some machines are taught using thousands of photographs and images of the environment where their autonomous pupil will eventually operate. To help ensure their trained models can identify the correct course to take in a hyper-specific environment – with indicators such as centerline markings and side stripes on a runway at dawn – Bhattacharyya and his Ph.D. students chose a December morning to rise with the sun, board one of Florida Tech’s Piper Archer aircraft and photograph the views from above. Bhattacharyya, an associate professor of computer science and software engineering, is exploring the boundaries of operation of efficient and effective machine-learning approaches for vision-based classification in autonomous systems. In this case, these machine learning systems are trained on video or image data collected from environments including runways, taxiways or roadways. With this kind of model, it can take more than 100,000 images to help the algorithm learn and adapt to an environment. Today’s technology demands a pronounced human effort to manually label and classify each image. This can be an overwhelming process. To combat that, Bhattacharyya was awarded funding from NASA Langley Research Center to advance existing machine learning/computer vision-based systems, such as his lab’s “Advanced Line Identification and Notation Algorithm” (ALINA), by exploring automated labeling that would enable the model to learn and classify data itself – with humans intervening only as necessary. This measure would ease the overwhelming human demand, he said. ALINA is an annotation framework that Hafeez and Parth developed under Bhattacharyya’s guidance to detect and label data for algorithms, such as taxiway line markings for autonomous aircraft. Bhattacharyya will use NASA’s funding to explore transfer learning-based approaches, led by Parth, and few-shot learning (FSL) approaches, led by Hafeez. The researchers are collecting images via GoPro of runways and taxiways at airports in Melbourne and Grant-Valkaria with help from Florida Tech’s College of Aeronautics. Bhattacharyya’s students will take the data they collect from the airports and train their models to, in theory, drive an aircraft autonomously. They are working to collect diverse images of the runways – those of different angles and weather and lighting conditions – so that the model learns to identify patterns that determine the most accurate course regardless of environment or conditions. That includes the daybreak images captured on that December flight. “We went at sunrise, where there is glare on the camera. Now we need to see if it’s able to identify the lines at night because that’s when there are lights embedded on the taxiways,” Bhattacharyya said. “We want to collect diverse datasets and see what methods work, what methods fail and what else do we need to do to build that reliable software.” Transfer learning is a machine learning technique in which a model trained to do one task can generalize information and reuse it to complete another task. For example, a model trained to drive autonomous cars could transfer its intelligence to drive autonomous aircraft. This transfer helps explore generalization of knowledge. It also improves efficiency by eliminating the need for new models that complete different but related tasks. For example, a car trained to operate autonomously in California could retain generalized knowledge when learning how to drive in Florida, despite different landscapes. “This model already knows lines and lanes, and we are going to train it on certain other types of lines hoping it generalizes and keeps the previous knowledge,” Bhattacharyya explained. “That model could do both tasks, as humans do.” FSL is a technique that teaches a model to generalize information with just a few data samples instead of the massive datasets used in transfer learning. With this type of training, a model should be able to identify an environment based on just four or five images. “That would help us reduce the time and cost of data collection as well as time spent labeling the data that we typically go through for several thousands of datasets,” Bhattacharyya said. Learning when results may or may not be reliable is a key part of this research. Bhattacharyya said identifying degradation in the autonomous system’s performance will help guide the development of online monitors that can catch errors and alert human operators to take corrective action. Ultimately, he hopes that this research can help create a future where we utilize the benefits of machine learning without fear of it failing before notifying the operator, driver or user. “That’s the end goal,” Bhattacharyya said. “It motivates me to learn how the context relates to assumptions associated with these images, that helps in understanding when the autonomous system is not confident in its decision, thus sending an alert to the user. This could apply to a future generation of autonomous systems where we don’t need to fear the unknown – when the system could fail.” Siddhartha (Sid) Bhattacharyya’s primary area of research expertise/interest is in model based engineering, formal methods, machine learning engineering, and explainable AI applied to intelligent autonomous systems, cyber security, human factors, healthcare, explainable AI, and avionics. His research lab ASSIST (Assured Safety, Security, and Intent with Systematic Tactics) focuses on the research in the design of innovative formal methods to assure performance of intelligent systems, machine learning engineering to characterize intelligent systems for safety and model based engineering to analyze system behavior. Siddhartha Bhattacharyya is available to speak with media. Contact Adam Lowenstein, Director of Media Communications at Florida Institute of Technology at adam@fit.edu to arrange an interview today.
For autonomous machines to flourish, scalability is everything
The past decade has seen remarkable advancements in robotics and AI technologies, ushering in the era of autonomous machines. While the rise of these machines promises to revolutionize our economy, the reality has fallen short of expectations. That’s not for a lack of intensive investments in research in development, says Yuhao Zhu, an associate professor of computer science at the University of Rochester. The reason we’re not seeing more service robots, autonomous drones, and self-driving vehicles, Zhu says, is that autonomation development is currently scaling with the size of engineering teams rather than the amount of relevant data and computational resources. This limitation prevents the autonomy industry from fully leveraging economies of scale, Zhu says, particularly the exponentially decreasing cost of computing power and the explosion of available data. Zhu recently co-authored a report on the quest for economies of scale in autonomation in Communications of the ACM and is part of an international team of computer scientists focused on making autonomous machines more reliable and less costly. He can be reached by email at yzhu@rochester.edu.
Managing cyber risk is no longer a technical necessity but also a strategic imperative in global business. As companies are more interconnected and reliant on artificial intelligence (AI), the Internet of Things, and the rest of the digital ecosystem, they are exposed to greater opportunities and risks. In this video, Senior Managing Director and cybersecurity expert Denis Calderone shares topics covered in the 2025 J.S. Held Global Risk Report focused on managing cyber risk in the year ahead. The global regulatory landscape is evolving rapidly in response to the increasing severity of cyber threats. Governments and regulatory bodies, including the U.S. Securities and Exchange Commission (SEC), the European Union (EU), and the U.S. Transportation Security Administration (TSA), have introduced cybersecurity mandates that require businesses to strengthen their defenses, improve incident reporting, and ensure compliance with new industry standards. The 2025 Global Risk Report by J.S. Held provides perspectives on these regulatory shifts, helping businesses navigate the complexities of cyber risk and compliance. The growing frequency and severity of cyberattacks are reshaping how businesses approach risk management. The J.S. Held 2025 Global Risk Report explores key issues facing business today, including: Business Interruption from Cyber Incidents: High-profile cases like Change Healthcare’s 2024 breach demonstrate how cyberattacks can halt operations, lead to regulatory scrutiny, and result in massive financial losses. Reputational and Legal Fallout: Cyber incidents can trigger lawsuits and damage a company’s reputation, often leading to prolonged trust recovery periods with customers and investors. Loss of Sensitive Data: Data breaches can expose critical information, including personal, financial, and proprietary data, amplifying risks of identity theft and fraud. Tightening Regulatory Landscape: New cybersecurity laws, such as the EU’s NIS2 Directive and Cyber Resilience Act, alongside the US SEC’s disclosure rules, demand stricter compliance from businesses in key sectors. Complexities in Cyber Insurance: Many companies lack clarity on whether their policies cover ransomware or meet legal and operational needs, leaving them exposed to potential financial risks. Ransomware Dilemmas and Legal Risks: Paying a ransom may violate international sanctions, creating additional legal complications for organizations already dealing with cyberattacks. Proactive Cybersecurity Enhancements: Companies implementing advanced cybersecurity measures like MFA, EDR, and immutable backup systems improve their defenses and reduce risks of disruption. AI-Powered Threat Detection: Artificial intelligence enables companies to identify fraud and cyberattacks faster by analyzing patterns and anomalies in real time, minimizing damage, and reducing costs. Increased Demand for Cyber Insurance: As companies across industries seek better coverage, insurers have opportunities to innovate new products, though exclusionary clauses are becoming more common. Business Continuity and Resilience: Organizations with strong cyber hygiene, incident response plans, and dependency mapping are better prepared for attacks and may benefit from reduced insurance premiums. Cybersecurity risk is just one of the five key areas analyzed in the J.S. Held 2025 Global Risk Report. Other topics include sustainability, supply chain, cryptocurrency and digital assets, AI and data regulations. If you have any questions or would like to further discuss the risks and opportunities outlined in the report, email GlobalRiskReport@jsheld.com. To connect with Denis Calderone simply click on his icon now. For any other media inquiries - contact : Kristi L. Stathis, J.S. Held +1 786 833 4864 Kristi.Stathis@JSHeld.com
J.S. Held Experts Examine Crypto’s Pitfalls and Potential
The global cryptocurrency market has surged to a staggering USD 3.4 trillion. However, alongside this rapid expansion, significant challenges and risks continue to emerge. The J.S. Held 2025 Global Risk Report examines the evolving landscape of crypto and digital assets, highlighting both the potential and the pitfalls of this dynamic sector. The explosion of cryptocurrency adoption across industries—from gaming to decentralized finance (DeFi)—has led to increased regulatory scrutiny and security concerns. With the expected growth in the number of users to exceed 107.3 million in the market by 2025, every sector is looking at what crypto and this blockchain technology can do to transform their business. Even the gaming industry has entered the crypto space with bridging services offering “Play-to-Earn” (P2E) games. While anonymity remains a key feature in both the risk and success of cryptocurrency, the concept of “Know Your Customer” on centralized platforms is still required but continues to evolve because not all anonymity is evil. Despite regulatory, environmental, geopolitical, and other business risks, the J.S. Held 2025 Global Risk Report reveals how the crypto industry continues to evolve, offering new opportunities for businesses and investors around: Enhanced Transparency & Security Regulatory Clarity Education & Compliance Digital Identity Solutions “With regulatory frameworks tightening globally—from the European Union’s Markets in Crypto-Assets (MiCA) law to China’s outright ban—the future of crypto remains at a critical inflection point,” observes J.P. Brennan, Global Head of Fintech, Payments, Crypto Compliance and Investigations at J.S. Held. “As the industry matures, the balance between risk mitigation and innovation will shape the next phase of digital asset adoption,” J.P. Brennan adds. J.P. Brennan examines the crypto risks and opportunities outlined in the 2025 J.S. Held Global Risk Report in this video: Cryptocurrency and digital asset risk is just one of the five key areas analyzed in the J.S. Held 2025 Global Risk Report. Other topics include sustainability, supply chain, Artificial Intelligence (AI) and data regulations, and managing cyber risk. If you have any questions or would like to further discuss the risks and opportunities outlined in the report, please email GlobalRiskReport@jsheld.com. To connect with J.P. Brennan, simply click on his icon now. For any other media inquiries - contact : Kristi L. Stathis, J.S. Held +1 786 833 4864 Kristi.Stathis@JSHeld.com
Expert Spotlight: AI and Accreditation in Behavioral Health
Recently, Mike Johnson, MA, CAP, Senior Managing Director of Behavioral Health at CARF International sat down with the host of the podcast No Notes to discuss how the major accrediting agencies are thinking about AI—and whether an organization’s use of AI ultimately impacts their accreditation. Tune into this captivating discussion using the link below. Michael makes a point in the podcast that CARF has no issue with AI tools being used for documentation as long as the notes meet established standards for quality and accuracy. There are positive effects and benefits of responsible AI use in behavioral health—from reduced provider burnout and turnover to better client engagement and outcomes. But, how do these technologies stack up against behavioral health accreditation standards? And how do the industry’s top accreditation bodies feel about the use of AI in behavioral health practice? Michael Johnson is the CARF International Senior Managing Director of Behavioral Health. If you are looking to know more or connect with Michael, view his profile below to arrange an interview today.
Supply chain disruptions cost organizations an estimated $184 billion annually, according to Swiss Re. A recent survey of 2,000 European shipping customers by logistics giant Maersk revealed that 76% experienced supply chain disruptions that delayed their business operations in the past year, with 22% reporting more than 20 disruptive incidents in the same period. These figures underscore the growing businesses’ growing vulnerabilities, as detailed in the 2025 J.S. Held Global Risk Report, which outlines how companies worldwide must adapt to an increasingly complex and volatile supply chain landscape. As highlighted in the 2025 Global Risk Report, modern supply chain disruptions stem from a range of factors, including climate change, natural disasters, cyberattacks, fraud, and geopolitical instability. Conflicts such as the Russia-Ukraine war and tensions in the Middle East continue to exacerbate these challenges. Gone are the days when companies could shift blame to suppliers without accountability. The globalization of supply chains has made them increasingly susceptible to cyber incidents, material shortages, and regulatory scrutiny. Consumers and governments alike are demanding greater transparency, pushing companies to disclose where products come from, how they are sourced, and whether their manufacturing processes harm people or the environment. The 2025 Global Risk Report notes that in response, governments worldwide have introduced stricter regulations, particularly in the European Union, where new and existing legislation is enforcing greater oversight and compliance. “As consumers, governments, and corporations acknowledge the effects of supply chain risks, transparency and due diligence will become more critical to the internal compliance structure of global businesses,” said Vice President of Sustainability Andrea Korney. “The enactment and greater enforcement of laws focused on sustainability issues have increased the obligations on companies to examine the sources and actions of their suppliers and how it all impacts the entire value chain.” In the 2025 J.S. Held Global Risk Report, multidimensional experts who combine scientific, technical, financial, and risk management expertise identify and explore key business risks shaping the future of supply chain resilience, including: Geopolitical instability Natural disasters and climate science Maritime route disruptions Regulatory fragmentation Cybersecurity threats Trade and tariff threats Critical minerals dependency Financial risks and fraud J.S. Held environmental risk and compliance expert John Peiserich, Esq., observes, “These risks are no longer hypothetical—they are actively reshaping the business landscape. Organizations that fail to anticipate and mitigate these challenges risk operational disruptions, financial losses, and reputational damage.” For businesses seeking to build resilient supply chains, the 2025 J.S. Held Global Risk Report serves as an important guide, providing expert insights and data-driven analysis to help companies navigate the evolving risk landscape. J.S. Held experts serve as trusted advisors to global clients on these and other risks, crafting business strategies, leveraging technology seeking to mitigate risk, and optimizing business opportunities to build resilience in an era of uncertainty. Supply chain risk is just one of the five key areas analyzed in the J.S. Held 2025 Global Risk Report. Other topics include sustainability, the rise of crypto and digital assets, AI and data regulations, and managing cyber risk. If you have any questions or would like to further discuss the risks and opportunities outlined in the report, please email GlobalRiskReport@jsheld.com. To connect with Andrea Korney or John Peiserich simply click on either expert's icon now. For any other media inquiries - simply contact : Kristi L. Stathis, J.S. Held +1 786 833 4864 Kristi.Stathis@JSHeld.com
A Beginner’s Guide to Expertise Marketing
Audiences today are consuming more digital content than ever, but they’ve also become far more discerning. Algorithms, AI search summaries, and social platforms have changed how information is discovered and trusted. The result is that organizations often get caught up in pushing out content quickly—only to be overlooked when it lacks depth or credibility. From misinformation to shallow click-driven posts, audiences are quick to disengage. What they’re seeking now are authoritative voices backed by proven expertise. That’s where Expertise Marketing comes in: a strategy focused on showcasing real knowledge, research, and experience in ways that build trust, attract attention, and strengthen reputation. According to Edelman’s Trust Barometer Study, experts play a vital role in establishing credibility amongst audiences and developing more meaningful interactions with businesses and organizations. As far back as their 2019 report results showed that 56% of people trust businesses as a source of news and information while only 47% trusted the government. On top of that, they also reported that 73% of participants were worried about false information or fake news being used as a weapon. This distrust has only gotten worse since COVID and the polarizing politics of recent years. With this in mind, there’s a real opportunity for knowledge-based organizations to step up and show their smarts through expertise marketing. What is Expertise Marketing? Expertise marketing is the practice of making the knowledge and skills of your human resources more visible to your partners and audiences. It draws attention to the value that your people can bring as brand ambassadors and strategically leverages the work your experts are doing to tell a more personal story. In many cases, expertise marketing can also be used to showcase your strengths in research and innovation. Creating a stronger digital presence, expertise marketing more effectively uses your channels to connect with audiences such as media, customers, partners and donors. It builds a sense of trust with your audiences and above all else, it helps establish your reputation as an industry leader. Expertise Marketing Defined: The practice of collectively promoting an organization's experts as brand ambassadors to demonstrate their skills or knowledge. Best practices to publish and connect organizational expertise in ways that foster internal discovery, collaboration, shared knowledge and diversity. Activities that leverage expertise to nurture conversations and connections with audiences such as media, customers, partners, government and funding agencies. How to Make Your Expertise More Visible Properly executed, expertise marketing is about harnessing your in-house expertise and making it more visible. By delivering comprehensive, relevant information in a visually engaging format, you can create a window into your organization that helps audiences better understand your offering and encourages more meaningful conversations. Here are three areas where expertise excels: On Your Website There’s a good chance that you already created touchpoints for expertise marketing but they’re just not optimized for audiences. For example, many organizations are unaware that the “About Page” is the second most visited page on a website and may overlook its potential for attracting audiences. Other webpages that strongly benefit from expert content include: Speaker’s Bureaus Media Rooms Employee Directories Faculty Directories Blogs Employee Intranets Awards Recognition Research & Technology Transfer Through Search Engines Content marketing and search engine optimization (SEO) go hand-in-hand – and it’s key to making your expertise more visible. In Google’s search algorithm, factors like trust and authority are significantly impacted by items such as content and expertise. While SEO is no small task, tools like the ExpertFile Platform are designed to make aggregating and optimizing expert content as seamless as possible. In addition, organizations can also improve their rankings by: Identifying and showcasing a range of expertise Using rich media to display expert content Regularly updating your website with expert content Producing content related to current trends and emerging news Through Distribution Networks Showcasing your experts isn’t just about hosting profiles on your own website—it’s about ensuring they are discoverable where key audiences are already looking. By publishing expert content on dedicated search engines such as expertfile.com and the ExpertFile Mobile App, organizations dramatically expand their reach beyond their immediate networks. These channels are designed for the very audiences that matter most—media, event organizers, research partners, donors, and prospective clients—who are actively searching for credible voices to inform stories, shape agendas, and build partnerships. Leveraging these distribution networks amplifies visibility, positioning your experts as go-to authorities well beyond the boundaries of your institutional website. In The Media For many organizations, media opportunities are an afterthought but it’s the perfect way to highlight your expertise and attract a broad range of audiences. Media outlets are constantly on the hunt for topic-specific experts to speak at conferences, weigh-in on their editorials and enhance the overall quality of their reporting. By making your experts more visible to this audience, you’re not only building your brand reputation as an industry authority but you’re also creating opportunities for new revenue. Starting an Expertise Marketing Program Bringing an expertise marketing program to life starts by taking a deeper look at your human resources and pinpointing the people in your organization who can support your expertise marketing initiatives. This post on Identifying Expertise is a great starting point for understanding what makes someone an expert and how you can position them for various tasks in your expertise marketing program. From there, it’s about getting buy-in from key stakeholders, collaborating across departments to surface expert content and strategizing with your team about where your expertise is best served. Download The Complete Guide to Expertise Marketing For a comprehensive look at how expertise marketing benefits the entire organization and drives measurable return on investment, follow the link below to download a copy of ExpertFile’s Complete Guide to Expertise Marketing for Corporate & Professional Services, Higher Education Institutions, Healthcare Institutions or Association & Not-for-Profits.
The ethics of using AI in academic writing: Opportunities and challenges in education
A major topic buzzing around educational circles right now is the use of AI in academic writing. With AI tools becoming more sophisticated, students and educators find themselves navigating a new academic landscape. It’s both exciting and daunting. Joshua Wilson, an associate professor of education at the University of Delaware, can discuss this landscape. Drawing on his research in automated writing evaluation (AWE), Wilson explores how AI tools – particularly generative AI – can transform the teaching and learning of writing by supporting critical thinking and knowledge transformation. He emphasizes that AI can help writers overcome lower-level constraints, such as grammar and organization, enabling deeper reflection and metacognitive engagement. Additionally, AI tools hold promise for helping students structure their thoughts and ideas, serving as valuable aids in organizing ideas before they begin writing. Thus, making writing more accessible and less intimidating for learners at all levels. However, he cautions that the value of AI depends on its thoughtful integration into educational practices, alignment with learning theories, and addressing challenges such as equity, feedback accuracy, and ethical use. He provides actionable insights for educators, researchers, and policymakers on how AI can enhance writing instruction, critical thinking and accessibility while avoiding potential pitfalls. Wilson has appeared in publications including The Washington Post, The Baltimore Sun and The Philadelphia Inquirer. To speak with Wilson further about AI and writing, click on his profile.
