In our previous Thinking Series article, we explored why better questions need better information. Information is the foundation of every decision. But having good information doesn't automatically give us the right answer. Information still needs to be understood. Information Needs Context Imagine looking at a sales report. You notice that sales have dropped by 15%. That is information. But it doesn't explain why. Did customers buy less? Did prices change? Was a product unav

In our previous Enterprise Thinking article, we explored why good questions matter. A clear question helps others understand what we're trying to achieve. But asking a good question is only part of the journey. To give a useful answer, we also need the right information. Without it, even the best question may still lead to the wrong conclusion. Information Shapes Decisions Every decision we make is based on information. When a doctor makes a diagnosis, they don't rely on a si

In our previous articles of our Enterprise Thinking Series, we explored how businesses change, why responsibilities evolve, and how people grow along with them. As our responsibilities change, so do the questions we ask. But why do good questions matter? The answer is actually quite simple. We ask questions because we want something. Every Question Has A Purpose We don't ask questions because we enjoy asking them. We ask because we need an answer. When we visit a doctor, we'r

In our first article, we explored a simple idea. Technology doesn't change businesses. People do. In our second article, we suggested that before choosing AI, we should first understand what we're trying to improve. But what happens after that? Every Business Change Starts With a Reason Businesses don't change for no reason. They change because something around them changes. A customer expects something different. A competitor introduces a better product. A new regulation app

Last week, we explored a simple idea. Technology doesn't change businesses. People do. If that's true, then the next question becomes: Where should we start? Today, it feels like every business is talking about AI. Every week there's a new model, a new feature, or a new tool promising to help businesses work better. The opportunity is exciting. But before asking, "Which AI should we use?" perhaps we should first ask a much simpler question. "What are we trying to improve?"

Every few years, there's a new technology everyone talks about. ERP. Business Intelligence. Cloud Computing. Mobile Apps. Digital Transformation. Today, it's Artificial Intelligence. Each promised to improve the way businesses work. Some organizations became much better. Others simply bought new software. Why? The technology wasn't the difference. The people were. Technology Doesn't Solve Problems by Itself Technology gives us new ways to solve problems. But technology doesn'

Why AI Is Unlike Every Technology Shift Before It Over the past three decades, I've watched many technology waves reshape the enterprise. · The Internet · ERP · Data Warehousing · Business Intelligence · Data Analytics · Data Science · Cloud Computing · Artificial Intelligence Every major technology transformed how businesses worked. Artificial Intelligence transformed how technology was adopted. That may sound like a su

Over the past year, we have been helping organizations explore how AI can be adopted within the enterprise. Interestingly, the discussions are often less about technology and more about business priorities. In many meetings, three themes consistently emerge: · AI is becoming a driver for innovation and funding. · AI adoption is viewed as a strategic requirement across the organization. · Intellectual property, governance, and data protection must remain non-

Over the past year, I’ve sat through countless conversations about “Agentic AI” — and now “Multi-Agentic AI.” These terms sound powerful, even futuristic. But when you look past the jargon, what you often find are approaches that are hard to operationalize: costs are difficult to forecast, outcomes can be hard to explain, and building trust in production settings becomes a real challenge. My perspective comes not from theory, but from hands-on experience building and testing