Healthcare’s inefficiencies are not moral failures; they’re architectural ones.
Nearly $400 billion in annual administrative waste³.
43 percent of patients waiting more than two weeks for claims settlement³.
55 percent growth in Medicare Advantage denials in a single year³.
These numbers don’t reflect bad intentions—they reflect systems designed for file exchange, not reasoning.
Read MoreI am most concerned about this issue in healthcare—perhaps obviously, because de-skilled clinicians could endanger human health. The other general reason why healthcare professionals might let AI de-skill themselves is that they are under considerable pressure to be more productive—at least in the U.S. The average patient visit with a physician is only about 15 minutes, and there are temptations galore to take AI-enabled shortcuts. If AI can easily summarize patient visits in clinical notes, diagnose diseases and communicate with patients about them, and help navigate highly bureaucratic insurance processes, why shouldn’t doctors and nurses take advantage of this capability?
Read MoreHaving been a tech bubble collector for more than 25 years, I noticed that by 2023, the signs of a bubble were already hidden in plain sight. Fast forward to October 2025. Fueled by VC exuberance (FOMO), a stock market frenzy, exaggerated user expectations, copyright infringements, the explosion of vibe coding, and AI startups competing for world domination, the bubble will soon burst (or at least deflate).
Read MoreYes, there is an AI bubble. AI-related stock prices are inflated, valuations of startups are inflated, there is too much marketing hype, data center construction is excessive, etc. All of these related bubblets will deflate, and probably soon.
Read MoreMIT 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.
Read MoreNew research reveals a widening gap between hype and real enterprise value. A recent article reviews MIT’s The GenAI Divide: State of AI in Business 2025, revealing that 95% of generative-AI pilot programs fail to deliver meaningful business outcomes, such as revenue growth or productivity improvements.
Read MoreAI is transforming public procurement by enhancing efficiency, reducing costs, and increasing transparency. Tangible examples from across the world demonstrate AI's potential to revolutionize procurement processes. As governments continue to adopt AI solutions, it is imperative to address associated challenges and ensure ethical, transparent and effective use of these technologies to maximize public value.
Read MoreHere are some practical data-quality audits every organization undergoing digital transformation should run in 2025. The future of AI-driven operations won’t be built on algorithms alone. It’ll be built on the integrity of the data that those algorithms depend on. And that makes data quality the most strategic step in any AI initiative, whether you're a public agency or a private enterprise.
Read MoreIn mid-2025, we are entering the early stages of a new age of digital transformation where networked technologies that combine engineering, computer algorithms, and culture are becoming impactful on a global scale. The upcoming digital revolution and technological convergence will drastically affect our patterns of living, working, and networking in the near future.
Read MoreSince OpenAI announced ChatGPTin November of 2022, many business executives have focused their attention on generative AI,” wrote Babson College professorTom Davenport, and technology and business strategist Peter Highin “How Gen AI and Analytical AI Differ — and When to Use Each,” an article published in the Harvard Business Review (HBR) issue of December, 2024. “This relatively new technology set off a frenzy around AI and caused companies to pay attention to it for the first time. This is a positive development, since the technology is powerful and important, and enables many new business possibilities
Read MoreAbout three years ago I started following and writing about Generative AI (GenAI), Large Language Models (LLMs), chatbots, and related topics. Then a few months ago I started hearing about agentic AI, and since then I’ve been trying to better understand what agentic AI is all about.
Read MoreAs artificial intelligence (AI) systems rapidly proliferate—powering decisions in healthcare, finance, employment, and public safety—the call for regulatory oversight has intensified. Yet a stubborn myth persists: that regulation and innovation are fundamentally at odds. Many technologists and policymakers argue that oversight stifles creativity and hinders emerging ventures. This article challenges that assumption. In fact, well-designed regulation doesn’t just protect society—it facilitates sustainable innovation. By creating clarity, trust, and a fair playing field, regulation can become a foundational driver of progress in the AI era.
Read MoreFor hospital administrators and personnel, this latest wave of AI deployments can be an exciting windfall. At hospitals across the country, AI agents designed to improve clinical documentation are quickly and effectively digesting mounds of paperwork. They are spotting inefficiencies—saving unnecessary costs for hospitals and freeing up clinicians to focus on what matters most—clinical judgment, care coordination, and high-complexity decision-making.
Read MoreThe volume of headlines around AI is staggering. In boardrooms around the country, it's being hailed as the most revolutionary technology since the Internet. But while leaders are fascinated by the promise of artificial intelligence, few are seeing significant returns on their investments. In fact, many AI initiatives fail. However, there are exceptions. Companies such as Mars Wrigley, Colgate Palmolive, Turbo Tax and Pega Systems are putting AI into action.
Read More“Broadly speaking, agentic systems refer to digital systems that can independently interact in a dynamic world,” explained a McKinsey reportpublished in July of 2024. “While versions of these software systems have existed for years, the natural-language capabilities of gen AI unveil new possibilities, enabling systems that can plan their actions, use online tools to complete those tasks, collaborate with other agents and people, and learn to improve their performance. Gen AI agents eventually could act as skilled virtual coworkers, working with humans in a seamless and natural manner. … In short, the technology is moving from thought to action.”
Read MoreIn 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.
Read More“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.”
Read MoreThe 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.
Read MoreDeploying 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.
Read MoreI’ve been following the evolution of AI since the 1970s, especially the more recent era of data-centric AI systems based on highly sophisticated models trained with large amounts of information and powerful computer technologies. We were wowed when in 1997 Deep Blue won a celebrated chess match against then reigning champion Gary Kasparov, — one of the earliest, most concrete grand challenges of AI. The 2010s saw increasingly powerful deep learning AI systems surpass human levels of performance in a number of tasks like image and speech recognition, skin and breast cancer detection, and playing championship-level Go.
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