Category: Artificial Intelligence

In B2B sales, speed and preparedness are everything. Yet the data shows a sobering reality: 51% of deals are lost because the seller misses a follow-up step or can’t share information the buyer needs when they need it. Nearly half of salespeople (42%) admit they don’t have sufficient information before even talking to a prospect, a knowledge gap that can stall momentum from the very start. And when a hot inbound lead comes in, waiting just 30 minutes to respond can make your team 21× less likely to qualify that lead, since 78% of buyers ultimately go with the first

It’s a question I’ve been asking myself a lot lately. In the rush to innovate and keep up with trends, how often do we pause and scrutinize our day-to-day work? Before diving into any shiny new tool or technology, we need to take a hard look in the mirror and ask: What are the key things we’re wasting time on? Often, this means confronting inefficient workflows, redundant processes, and “busywork” that creeps into every corner of the organization. The truth is, we can’t fix or automate something we haven’t first acknowledged as a problem . The Temptation of Flashy AI

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,