In every era of transformative progress, a tipping point emerges—an inflection where yesterday’s impossibilities become the infrastructure of today. In healthcare, we are nearing such a moment. For decades, the health insurance industry has been hindered by administrative complexity, rising costs, and structural inertia. Despite numerous attempts at policy reform, meaningful simplification has remained elusive. But a convergence of emerging technologies—artificial intelligence, decentralized systems, and real-time data networks—is poised to change that.
“Our data is everywhere and powering everything,” noted “Pathways to Open Data,” a report by Linux Foundation Researchpublished in March of 2025. “From marketing, to healthcare, to government services, to the emerging phenomenon of programming AI agents, organizations leverage data to be as efficient and effective as possible. However, data is often siloed within entities and any third-party data access requires overcoming significant technical, legal, economic, operational, and cultural obstacles that are multifactorial and at times may seem intractable. The increasing reliance on data calls for an assessment of these obstacles and how organizations can shift toward greater openness and sharing.”
The information technology landscape has significantly changed in recent years in terms of corporate value creation (and performance). Wherever data is stored, the digital revolution has created new challenges but also new solutions for innovation and efficiency. Unfortunately, data has a high value to those with nefarious purposes and enhancing data protection needs to become a priority for every business and organization.
Deploying artificial intelligence (AI) has complex challenges concerning ethics, transparency, bias, and fairness. AI governance can mitigate these challenges. What is AI governance? OECD has proposed that artificial intelligence (AI) governance refers to the comprehensive framework of policies, regulations, ethical guidelines, and processes designed to oversee the development, deployment, and utilization of artificial intelligence (AI) systems in a manner that is ethical, transparent, and aligned with societal values. According to IBM, artificial intelligence (AI) governance refers to the processes, standards and guardrails that help ensure AI systems and tools are safe and ethical. AI governance frameworks direct AI research, development and application to help ensure safety, fairness and respect for human rights.
Since the publication of my book, "Navigating the Techstorm," I've closely watched the rapid evolution of emerging technologies through the lens of a deep-tech investor with extensive experience in technology startups. The framework I proposed—Analyze, Assess, Adapt—has not only remained relevant but has become even more critical as the pace of technological innovation accelerates. It's become clear that understanding and proactively responding to technological disruption is no longer merely beneficial—it's essential for sustained growth and competitive advantage.
AI agents are fundamentally reshaping data center design, infrastructure, and operations. As these agents grow more sophisticated and widespread, traditional data centers must evolve to meet unprecedented demands—from escalating computational power and cooling needs to advanced networking capable of handling dynamic, high-volume traffic. This report examines how data centers are adapting to support AI workloads, highlighting innovations in technology, design, and operations that will drive the future of digital infrastructure.
One of the best places to implement AI practically and successfully is in external or internal processes,including front and back-office processes used in everyday and game-changing modes. Processes are often the basis of organizational actions that cross internal and external boundaries. These processes often employ resources that could benefit from AI's automation or assistance, especially where knowledge, decision-making, and agile optimization based on changing or emerging goals are required.
Generative AI is dominating headlines, boardroom discussions, and innovation budgets. From marketing copy to code generation, it's being hailed as the most revolutionary technology since the internet. But while enterprise leaders are captivated by the promise of artificial intelligence, few are seeing real returns on their investments.
While artificial intelligence (AI) has the potential to be transformative, the track record to date is disappointing. Although billions have been invested in AI, recent research reveals that only 1 percent of companies surveyed consider themselves to be “mature” – i.e. to have fully integrated AI into workflows and thereby produce better business outcomes. The same research report found that the biggest barrier to scaling AI is not employees—but leaders. Mayer, Hannah, Lareina Yee, Michael Chui, and Roger Roberts. "Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential." McKinsey & Company, January 28, 2025. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work.
The integration of Artificial Intelligence (AI) in clinical trials has emerged as a transformative force in the United States healthcare system. While AI offers significant benefits in clinical trials, including cost reduction and improved efficiency, it also presents complex challenges in governance, regulation, and ethical implementation. Authors aim to analyze specific AI applications in U.S. clinical trials, focusing on case studies, regulatory frameworks, and ethical considerations.
When we released "AI for the Rest of Us" two years ago, we stood in a liminal space, observing as artificial intelligence seemed poised to transform not only Silicon Valley but the entirety of human experience. In hindsight, we recognize that our predictions have been realized in both expected and surprising ways.
MIT professor emeritus Rodney Brooks has been posting an annual Predictions Scorecard in rodneybrooks.com since January 1, 2018, where he predicts future milestones in three technology areas: AI and robotics, self driving cars, and human space travel. He also reviews the actual progress in each of these areas to see how his past predictions have held up. On January 1 he posted his 2025 Predictions Scorecard.
According to a recent Boston Consulting Group (BCG) report, while there is much hype around artificial intelligence (AI), the value is hard to find. Based on recent research involving more than 1,000 companies worldwide, only 22% of companies have advanced beyond the proof-of-concept stage to generate some value, and only 4% are creating substantial value.
“Open source software (OSS) has become a driving force behind innovation, collaboration, and the democratization of technology,” said the 2024 Global Spotlight Insights Report. The report, published last month by Linux Foundation Research, analyzed regional and industry differences in open source opportunities and challenges and tracked year-over-year trends…
President Donald Trump’s administration will assume a cybersecurity portfolio that has continued to evolve toward combating digital threats since the Cybersecurity and Infrastructure Protection Agency (CISA) was created 6 years ago out of the Department of Homeland Security (DHS). CISA’s mission is a formidable one. The list of hostile threat players in cyberspace is quite extensive. Nation-states, organized criminals, terrorists, and hacktivists are all included.
It is well known that artificial intelligence (AI) enables better, faster and more automated decisions. Indeed, it has been proposed that AI is driving a resurgence of interest in redesign business processes.[i] That’s partly due to the ability of certain AI tools, such as robotic process automation (RPA) which when combined with machine learning as “intelligent process automation,” can automate information-intensive processes. It has also been argued that AI fits well into improvement methods such as Lean Six Sigma and can be applied at each stage of the so called DMAIC process (Define-Measure-Analyze-Improve- Control).[ii] Note that Six Sigma and Lean Six Sigma are highly codified and structured methods of process improvements which have a strong bias towards incremental improvement within organizational boundaries. The integration of AI into process improvement may have the potential to reignite interest in more major change – targeted at large enterprise processes – perhaps even reengineering.
There is no doubt that 2023 was a tough year for cyber security. The amount of data breaches keeps rising from previous years, which was already very scary. An exponential rise in the complexity and intensity of cyberattacks like social engineering, ransomware, and DDOS attacks was also seen. This was mostly made possible by hackers using AI tools.
