Most enterprise leaders were taught to be cautious with new technologies: wait until the tech is stable, case studies are plentiful, and best practices are clear. It sounds prudent, but in the fast-moving world of Artificial Intelligence (AI) this instinct can be a strategic misstep. What if the best time to invest in AI is actually when things are still messy and unclear? This contrarian idea flies in the face of the usual “wait-and-see” approach, yet it’s exactly how today’s tech leaders are seizing competitive advantage. Generative AI’s explosion in the past two years underscores this: organizations are adopting AI
Tag: AI
In a board meeting, your new AI sales assistant confidently presents a detailed market report – and cites a data source that doesn’t exist. This scenario isn’t science fiction; it’s the emerging risk of AI “hallucinations” in the enterprise. AI hallucination refers to when a model produces a plausible-sounding but false or fabricated answer. For companies adopting generative AI, these mistakes aren’t just embarrassing, they can lead to real financial and legal repercussions. In one case, Air Canada’s customer service chatbot invented a refund policy , leading a tribunal to order the airline to compensate a passenger for misinformation. In
Over the past two years, large language models (LLMs) like ChatGPT have burst onto the scene and rapidly entered mainstream use. It took ChatGPT just months to reach over 100 million users, making it one of the fastest-adopted technologies ever. And it’s not slowing down; as of late 2024, ChatGPT’s website was seeing over 3.7 billion visits per month , and in a single day in May 2025 it handled a record 80 million visits. This explosive growth in conversational AI matters because it is already beginning to reshape how people search for information and products. In a world where
In boardrooms and team meetings alike, a new theme is emerging: we’re all starting to manage autonomous AI “agents” alongside our human colleagues. Just a year ago, many companies were merely experimenting with chatbots; now 2025 is being hailed as “the year of the AI agent.” What changed? In the past year, generative AI evolved from a clever chatbot into a capable co-worker embedded in daily operations. Advanced models like OpenAI’s GPT-4 moved beyond simple Q&A and gained the ability to execute tasks via plugins and APIs, effectively becoming digital agents that can carry out multi-step objectives. Tech giants and
When I started in software development, “quality assurance” often meant the newest developer on the team got saddled with testing someone else’s code. I still remember my first bug-fixing and testing tasks, essentially acting as an unofficial QA, learning the ropes through broken builds and edge-case checklists. Fast forward to today: I find myself, sometimes acting as a tech lead, asking ChatGPT to generate those very checklists and test cases. The strangest part? As a senior developer, I’ve become the one clarifying requirements and verifying the output, roles traditionally handled by business analysts and QA engineers. It raises a compelling
Not long ago, I found myself pair programming in a way I never expected. As a former business analyst sitting beside developers, and also a developer sitting alongside developers, I was used to articulating requirements while my human partner typed away. But this time, my partner wasn’t human at all; it was an AI coding assistant. Guiding ChatGPT through building a feature, I had a flashback to Extreme Programming (XP) sessions from years past. The rhythm felt familiar: a “driver” writing code (the AI) and a “navigator” guiding the problem-solving (me). In that moment, I realized that using AI to
I’ve led software engineering teams through many hype cycles, from cloud computing to mobile apps. But 2025 feels different . For the first time, I find myself working alongside autonomous AI “agents” that not long ago were science fiction. In meetings with fellow tech leaders, one theme keeps emerging: AI agents are everywhere. Tech headlines are even calling 2025 the year of the AI agent . As a CTO and software developer (I still love to write code myself, not as much as I used to), I’ve watched generative AI evolve from a clever chatbot into a workforce of problem-solvers
Several years ago, I strapped on an OpenBCI EEG headset in my home office, fueled by one burning question: Could I control a machine with my thoughts? At the time, brain-computer interface (BCI) tech felt like sci-fi, but I was too curious not to try. What started as a hobby project became one of the most fascinating experiments I’ve ever done, and it’s now pulling me back in, thanks to today’s leaps in AI, hardware, and industry interest in BCIs. A DIY Brain-Computer Interface Experiment (Years Ago) I wasn’t a neuroscientist or a cyborg tinkerer, just a developer with a
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