While humans have assembled a lot of weather data, flash floods are too short-lived and localized to be measured comprehensively, the way the temperature or even river flows are monitored over time. That data gap means that deep learning models, which are increasingly capable of forecasting the weather, aren't able to predict flash floods.
Weather impacts sales. Every retailer knows it. But for most, the likelihood that it might rain, snow, or sleet on the third of March somewhere in the Midwest is rarely used. Vendors such as Weather Trends have offered accurate, long-range forecasts for more than 20 years. But the opportunity is not predicting the weather; it's knowing what to do with the data. AI might change that.
We're investing a lot in AI - we're doing a lot, but we're stopping at individual productivity. We're not taking the next step. You can't just screw AI on everything - it only makes you faster. It means you need to think about, 'how are our teams collaborating? How are people collaborating?' You probably need to change the way you work.
Simply having data isn't enough-how you organize, govern, and activate it makes all the difference. Leading organizations implement three specific practices: connect all your data together, label and organize it so it's easy to find, and set controls to ensure only the right people (or agents) have access to sensitive data sets.
The NFL is no stranger to innovation. Over the years, teams have adopted new strategies, technologies, and data-driven approaches to stay ahead of the competition. One of the most significant advancements in recent years is the rise of sophisticated analytics and modeling. These tools have become essential for teams seeking to improve player performance, game strategy, and overall team development.
Data science and artificial intelligence are fundamentally redefining what constitutes skill in investment management, shifting the sources of sustainable competitive advantage in ways most firms have yet to comprehend. This is not about automating existing workflows. It is about reconceptualizing which analytical tasks can be systematized and which genuinely require human judgment, then rebuilding investment processes around that distinction. Firms that fail to recognize this depth are not simply adopting tools more slowly. They are misunderstanding the nature of the change itself.
A new study analyzing data from 1990 to 2023 found that AI can predict 71% of mutual fund managers' trade directions. The research suggests that thousands of high-paying finance jobs could become automated. The study, published by the National Bureau of Economic Research, looked at the $54 trillion asset management industry and discovered that senior managers in less competitive categories are the most predictable-and thus the most replaceable.
A traveler might search for a weekend getaway and still see travel ads weeks later, long after returning home. The data was right. The timing wasn't.AI-driven marketing has the potential to close that gap - but only if it understands context. Personalization built solely on identity or past behavior can reveal who someone is, but not when or why they're ready to act.As AI takes center stage in marketing strategy, context is emerging as the differentiator that turns reactive automation into predictive intelligence.
AI is no longer optional at banks. The road map, and showing how it pays off, is the hard part. Alexandra Mousavizadeh, the cofounder and co-CEO of Evident, which tracks AI use in the financial industry, said some AI capabilities are "table stakes" for banks at this point - think back-office functions like reviewing legal documents and routine onboarding tasks. Beyond that, though, Mousavizadeh banks need to double down on their "competitive edge."
For most of modern finance, one number has quietly dictated who gets ahead and who gets left out: the credit score. It was a breakthrough when it arrived in the 1950s, becoming an elegant shortcut for a complex decision. But shortcuts age. And in a world driven by data, digital behavior, and real-time signals, the score is increasingly misaligned with how people actually live and manage money.
