Redefined by Cynthia

  Most people think Artificial Intelligence is something they need to start using. In reality, they have been using it for years. There is a moment in many of my talks where the conversation shifts. I tell people that they are already using AI every day. Not occasionally. Not experimentally. Every day. The hesitation I see is not about disagreement. It is about definition. Many people still associate AI with generative tools or future scenarios, when in reality, machine learning systems have been embedded in everyday digital infrastructure for years. The most common examples are also the most instructive because they demonstrate how AI operates at scale through pattern recognition and probabilistic modeling. Let's take a look at Spotify. Its recommendation system is built on a combination of collaborative filtering, natural language processing, and audio signal analysis. The platform processes hundreds of billions of user interactions, including plays, skips, search queries, playli...

When I hear the word token,  I do not think of artificial intelligence. I hear the clatter of metal coins dropping into a tray and feel the small thrill that came with holding a fistful of possibility. I think of arcades, roller skating rinks, blinking lights, and the unmistakable soundtrack of fun. I think of Pac-Man, Centipede, and later taking my son to Chuck E. Cheese, where I would slide a dollar bill into a machine and watch tokens spill out like treasure. Back then, tokens meant freedom. They bought a few minutes of excitement, bragging rights on the high-score board, and 200 hard-earned skee-ball tickets for a tiny, 5-cent butterfly-shaped eraser that nobody actually needed. Today, the word means something entirely different. Tokens are now one of the hidden forces powering artificial intelligence. They are not made of metal, they do not rattle into trays, and they cannot be traded for prizes. But in today’s digital economy, they are every bit as valuable. So, what is a tok...

Last year, when I found myself without a job, I didn’t know what an applicant tracking system was. Not really. I had heard the term “ATS” in passing, maybe in a webinar or an article, but it never felt urgent or relevant to me. I had built a career the traditional way. Relationships. Reputation. Strong experience. Clear results. When I applied for roles, I expected my background to speak for itself. It did in the past. And then it didn’t. After months of applying to roles I was not just qualified for but, in many cases, overqualified for, I had exactly one interview to show for it. One. That is the moment something shifts. You stop assuming it is timing or competition and start asking a harder question. What am I missing? That is when I was introduced to what I now think of as the wizard behind the curtain. The system I could not see was making decisions about whether I would even be considered. The ATS. What an ATS actually is and why it exists At its core, an applicant tracking syste...

There’s a narrative forming right now that deserves a closer look. You’ve probably heard it before. Companies are laying people off, restructuring teams, and quietly reducing headcount, and the explanation often sounds the same. AI is making us more efficient. It is a clean story. It is also an incomplete one. While AI is beginning to reshape how work gets done, the scale and speed of workforce changes we are seeing cannot be explained by automation alone. What is happening is more complicated and more important to understand. In the same way companies once greenwashed to signal environmental responsibility, we are now seeing a version of AI washing. Business decisions are being framed as AI-driven transformation when, in reality, they are often driven by more familiar forces. Organizations are under pressure to improve margins. Stock performance is under scrutiny. Capital is being reallocated toward infrastructure, particularly data centers and compute. Long-standing efficiency initia...

In emergency management, we don’t wait for the crisis to arrive before we prepare for it. We anticipate. We plan. We communicate clearly. Even when the situation is still unfolding. I learned this over 14 years in emergency management, including a decade with the American Red Cross, where clarity and communication can shape outcomes in real time. That same mindset is now required for AI. Because while AI is often framed as a technological shift, what we are actually experiencing is something far more familiar to those of us in crisis communications: a slow-moving, compounding event (like a hurricane) that is already reshaping how people work, how organizations operate, and how decisions are made. It’s not a single moment of disruption. It’s a series of small, accelerating changes, often happening faster than people can process them. And right now, many organizations are responding the way they do in the early stages of a crisis: reacting instead of preparing. Over the past month, ...