In the AI era, competitive advantage will depend less on access to technology and more on whether leaders and teams can interpret data, question outputs, and make sound decisions under real operating conditions.

Forecasting is one of the most powerful tools we have in market research. Whether you’re in retail anticipating seasonal demand or in aviation planning capacity and routes, the ability to predict what’s coming next is essential. 

Most AI initiatives do not fail at the model layer. They fail at the execution layer.  AI does not create business value just because it is introduced. It creates value when workflows, decision rights, and control structures are redesigned around it. 

Most conversations about AI and business models start in the wrong place. They start with tools: copilots, chat interfaces, automation, generative capabilities. But AI doesn’t reshape business models because it can write text or summarize documents.

From Data Chaos to Strategic Clarity: How AI Is Rewriting the Forecasting Playbook Forecasting used to be about guessing the future based on the past. Today, it’s about adapting to the present in real time—with AI as the engine.

Most companies do not have an AI adoption problem. They have an AI commitment problem.  The real barrier is rarely interest in AI itself. It is the unwillingness of companies to redesign workflows, assign ownership, define controls, and commit to measurable operating change.  Most companies are no longer struggling to notice AI. They are struggling to commit to what AI requires.

Artificial intelligence is entering everyday work quietly. It appears in the email drafted before you have found the words yourself, in meeting notes that appear instantly, and in reports that now take a fraction of the time they once did. It is becoming part of work through small, practical shifts in how things get done.