Eversund | Where ideas connect

The 8 numbers that explain what’s really happening with AI adoption

There is no shortage of evidence that companies are adopting Artificial Intelligence (AI). Every quarter brings another survey showing that more organizations are experimenting with generative AI, putting models into production, giving employees access to copilots, or increasing their spending on the technology. At the same time, there is a growing body of evidence suggesting that the economic impact of all this activity remains much less significant than the level of investment would imply. Both observations can be true, and when the numbers are put side by side, the apparent contradiction becomes easier to understand. Therefore, a more useful way to think about the current state of enterprise AI is not to ask whether companies are adopting it. They clearly are. The more important question is whether companies are changing their operations quickly enough to turn widespread access to AI into any meaningful business outcomes.

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Is your product actually a solution, or is it a platform?

The software industry loves labels. Product. Solution. Platform. Network. Offering. They're often used interchangeably, but they don't necessarily mean the same thing. There isn't a universally accepted definition for these terms, but there are some widely accepted perspectives in the software industry worth taking into consideration when you develop your go-to-market messaging. More importantly, using the right terminology helps set the right expectations with your customers.

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9 AI-Native Supply Chain Planning trends reshaping the industry

AI-native supply chain planning is becoming a more visible part of the technology landscape. Over the past year, Alex Pradhan has spoken with a growing number of newer supply chain planning vendors and identified a number of characteristics and trends that distinguish AI-native platforms from traditional systems being retrofitted with AI.

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Enterprise AI is about orchestration, not models

By: Bryan Nella | 3 min read

For the past two years, enterprise AI conversations have been focused on one question: "What AI model should we standardize on?" That's becoming the wrong question. The most pressing issue isn't choosing the best model; it's finding the most direct and efficient path to an outcome. As a user, I no longer care whether I'm using ChatGPT, Claude, or Copilot. I need the most accurate and useful output. A CFO, on the other hand, seeks the optimal outputs at the lowest cost. We're seeing foundational models rapidly advance and, in many cases, become interchangeable. The real differentiator is the ability to consistently deploy the optimal model, system, or resource for each task. Not every inquiry requires a heavy lift. For example: Should a policy question or vendor scorecard inquiry go to ChatGPT or to an internal knowledge base? Should a procurement analysis be handled by a domain-specific supply chain agent? These aren't just technology decisions. They're business and economic decisions.