Author: Peggy Smedley
Here at Connected World and The Peggy Smedley Show, we have spent the last few years talking about AI (artificial intelligence), and now there is an AI revolution happening—one that is much more physical, and it is happening on the factory floor. The 2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing, published by NIST (National Institute of Standards and Technology), makes one thing clear: AI and machine learning are moving deeper into the systems that make, move, measure, and maintain the things we depend on every day. Why? Because manufacturing is not an isolated industry that exists…
For years, construction companies have been told that technology will transform the industry. We have seen it with BIM (building information modeling), drones, reality capture, connected equipment, digital twins, cloud-based project management, and AI (artificial intelligence). Now, we are moving from technology that helps workers do their jobs to technology that can increasingly do parts of the work itself. That is where the idea of the frontier firm comes in, a concept I have written about in the manufacturing space. These organizations embed technology deeply into operations, use realtime data to make decisions, and continuously redesign how work gets done.…
For years, manufacturers have invested billions of dollars in automation, software, sensors, cloud platforms, and now AI (artificial intelligence). Yet many are still asking the same question: Why is digital transformation taking so long? The answer might just be a lack of interoperability. That message came through loud and clear during CESMII’s recent manufacturing leadership discussion, where executives from ExxonMobil, GE Appliances, Toyota, and General Mills agreed on one important point: The future of manufacturing depends on making data move as freely as ideas. Think about it. Every day, manufacturers generate enormous amounts of operational data. Collecting the data is…
The construction industry has always been resilient, but resilience alone is no longer enough. Today, contractors, owners, and project teams are navigating a market defined by uncertainty, rising complexity, and rapidly evolving technology. The companies that will thrive are those that embrace change, which is not an easy feat in the construction industry. Across the industry, one thing is becoming increasingly clear: construction is entering a new era where collaboration and data are just as valuable as concrete and steel. Labor shortages continue to challenge project delivery, material costs remain unpredictable, and demand varies significantly by region and project type.…
For the better part of three years, the manufacturing conversation has centered on one question: Should we adopt AI (artificial intelligence)? That question has now been answered. It is a clear yes. Now comes the hard part. The reality is the more important question today is much more difficult: Can manufacturers actually scale AI in a way that transforms the business? That is the trillion-dollar question on the table today for the manufacturing industry. A recent Parsec global survey of 1,200 manufacturing leaders across executive, operational, and technical roles found 72% of manufacturers have adopted AI in some capacity, yet…
For years, contractors and project owners have invested heavily in technology—from project management software and ERP (enterprise-resource planning) systems to drones, connected equipment, sensors, dashboards, and now AI (artificial intelligence). Every new investment has generated more information. Yet despite this explosion of data, many construction organizations still struggle to answer fundamental questions: What is happening? Why is it happening? And perhaps most importantly, what should we do next? Simply, construction companies are drowning in data but starving for insights. Several trends are accelerating this transition. First, AI infrastructure investments continue to grow at an unprecedented pace. McKinsey & Co., has…
The conversation around digital transformation has evolved a lot in the past decade. We began by digitizing drawings and leaning into CAD (computer-aided design). Then we embraced BIM (building information modeling). Next came digital twins, connecting the physical and digital worlds in ways we could only imagine years ago. Then we saw explosive growth of data in many vertical industries such as construction, utilities, infrastructure, and manufacturing, just to name a few. Today, we stand at yet another turning point. The question is no longer whether organizations should collect more data. The real question is this: How can businesses unlock…
The future of construction depends on the industry rethinking how we build the workforce that will construct the homes, buildings, cities, and infrastructure of tomorrow. As we move closer to the year 2030, the industry’s greatest competitive advantage won’t just be AI (artificial intelligence), automation, or robotics. It will be people with the skills to work alongside these technologies. Throughout the past several years, we have watched AI and automation move from experimentation to implementation. In the construction industry, AI is already helping monitor jobsites, optimize schedules, identify safety risks, and improve project outcomes. But AI is not replacing construction…
The freight rail industry is one of the most critical yet often overlooked pillars of the global economy. In the United States alone, railroads move roughly 40% of long-distance freight when you measure it by 10 miles, transporting everything from agriculture products and chemicals to automobiles and consumer goods to so much more. V Krishnan, industry advisor for manufacturing and mobility, Microsoft, says, “Freight rail is this invisible backbone of the whole economy.” Also, its efficiency is unmatched. A single train can move a ton of freight roughly 400 miles on just one gallon of diesel, making rail one of…
Even the most sophisticated AI (artificial intelligence) models are constrained by data trapped in information silos and legacy systems. The challenge is every new data source requires its own custom implementation, making connected systems difficult to scale, which is something that could slow building the next-gen infrastructure projects we so desperately need. Enter MCP (model context protocol). In November 2024, Anthropic introduced the model context protocol, which is a standard for connecting AI assistants to the systems where data lives. Anthropic suggests MCP provides a universal, open standard for connecting AI systems with data sources. The objective is to replace…