In the last six months, a clear trend has emerged: organizations across industries are realizing that successful AI implementation hinges not just on cutting-edge algorithms, but on effective change management. From enterprise giants to scrappy startups and public agencies, leaders are grappling with how to prepare their people and processes for an AI-powered future. Surveys show near-universal enthusiasm for AI; 95% of US companies report using generative AI tools , yet they also reveal major growing pains. The paradox of 2025 is that while AI adoption is soaring, many organizations feel less ready than ever to harness it fully. Let’s
Tag: Artificial Intelligence
In the past six months, artificial intelligence has sprinted ahead forcing institutions in education, labor, and governance to run twice as fast just to keep up. The rise of autonomous “agentic” AI systems and ever-more powerful generative tools is transforming how we learn, work, and regulate. New AI models can not only compose text and code but also take actions on our behalf (scheduling tasks, executing workflows, handling customer service inquiries, etc.), blurring the line between tool and independent agent. This breakneck progress is a double-edged sword: it offers unprecedented efficiency and creativity, yet it challenges existing policies and frameworks
Artificial intelligence may be the hottest topic in boardrooms, but how prepared are companies really? Recent surveys paint a sobering picture of organizational AI literacy and readiness. Despite heavy buzz and investment, most companies, and their people, are still in the early stages of understanding and adopting AI. In this segment, we’ll break down the latest data on how well executives and employees grasp AI, how trained the workforce is, the rise (or lack) of formal AI roles, and how different regions of the world compare. Executive Hype vs. Employee Reality There’s a clear gap between leadership perception and on-the-ground
As an observer of the AI industry, I’ve been struck by how rapidly the landscape is evolving – not just in model capabilities, but in the hardware and infrastructure that underpin these advances. The last few months have seen record-breaking AI chip performance , new approaches to data center design (to handle unprecedented power and cooling demands), and a growing spotlight on the energy footprint of large-scale AI. Below, I’ve compiled a rundown of key developments from late 2024 through spring 2025. AI Chip Performance: Faster and More Efficient Than Ever The market for AI chips in data centers hit
Why some tasks feel like sci-fi and others still act like dial-up AI progress isn’t a smooth upward curve; it’s a jagged skyline. One discipline races ahead, another stalls, and a few keep surprising even the optimists. Understanding those gaps is the difference between shipping a game-changing product and burning budget on promises the math can’t keep…yet. Peaks of Superhuman Performance 1. Code & Contracts Large-language models (LLMs) now translate Ruby into Go, refactor legacy COBOL, and draft NDAs in seconds. They live in a world of text-rich, rule-bound data where success is easy to score: does the code compile,
Scary or Amazing? The good part is that Chat GPT is still underutilized. Most people have heard about it by now, but most are not using it. If they did, they would see just how powerful it really is. Even when they do, it’ll take some time for them to understand how to communicate with it. For the developer community, it’s pretty simple and we’re understanding the scope of its capabilities…and it’s frightening. I started noticing a downward trend in the last month with my articles. They’re getting less interaction and even the earning is a quarter of what it
Unlocking AI’s Business Potential Since everyone’s on the AI bandwagon, I thought we could go over some real life business use-cases. You would hope that the business side would ask these types of questions, but I believe that IT has a responsibility in educating the business side in what kind of questions are appropriate to ask IT for. For example, sales can and should ask, “what happens if we increase the price of these products? Do we believe that the number of sales will be the same or are we going to drop?” That’s a great question and with a
A glimpse into the impending AI evolution There have been many pivotal moments in tech-history. I say this because I truly believe we’re on another verge. GPT-4 is incredibly impressive. It has about a 100 trillion parameters. Our brains have about 86 billion neurons and roughly a 100 trillion synapses. It’s incredibly quick and can generate unique responses for each of your questions. I’ve already transitioned to using it for most of my searches. I even asked myself, “has Google always been this bad?” We’re starting to get spoiled. What were some of the other pivotal moments in tech-history? Invention
Empowering AI with a mission Something that I’ve been thinking about lately on applying AI to business. We’ve probably all seen those videos where the developer gives his AI system a goal for Super Mario and the AI figures out the most interesting glitches and exploits the game in a way that we would never be able to. There are also times when the AI thought that the best way to beat it was just to pause the game forever. Interesting result but not what we were looking for. How about a web application? Can we define a goal, or
The brainpower behind intelligent decision-making That title sounded scary. It’s not meant to be. Humans make judgement calls each day. When you have ambiguity, you have to understand the context and then state what you believe that ambiguity means. What are some examples of judgement that humans do well at work? Decision making Humans have the ability to weigh the pros and cons of different options, analyze potential outcomes, and make informed decisions based on available information. Good decision-making skills require critical thinking, problem-solving, and the ability to balance competing priorities. Problem-solving People can identify and analyze complex problems, break