#Enterprise AI

The demand for AI tools from enterprise employees is fundamentally different from past technology waves. For the first time, subject matter experts can go from idea to working prototype in hours. Once they experience that entrepreneurial feedback loop, they don't go back to waiting in a queue.

Microsoft announced Copilot Cowork today, and most coverage treated it as a product update. We think the more interesting story is the Anthropic partnership underneath it, the timeline that connects it to the DoD situation, and what it means for how enterprise AI strategy actually works.

The problem isn't the technology. The problem is that we're treating AI like software.

After three years as a ChatGPT advocate, I'm finding myself shifting toward Anthropic. Here's why the enterprise versus consumer distinction matters.

The gap between AI enthusiasm and AI results has never been wider. 88% of organizations use AI, but only 7% have achieved enterprise-wide deployment. 2026 is the year that gap closes, one way or another.

AI coding tools cannot operate without humans. But the humans who thrive look different now. They manage multiple complex contexts, understand how the tools work, and know when to trust versus intervene. This is the compaction.

While ChatGPT dominates headlines, Anthropic is quietly winning enterprise accounts. Here's why the enterprise AI race may have a new leader.

Enterprise AI adoption is widespread, but scaling beyond pilots remains rare. Understanding this gap reveals why organizations increasingly need specialized help to operationalize AI and achieve real business outcomes.

Organizations at every stage of AI maturity ask the same question: where do we start? The answer depends on where you are today—but the path forward follows a consistent pattern. The Three Phases ...

You know you've reached a turning point in AI adoption when the questions change. Early on, people ask "Can AI really do that?" and "Is this accurate enough to trust?" But eventually, a different s...

Organizations invest heavily in AI tools, hire talented teams, and build ambitious roadmaps. Then they hit an invisible wall: their data systems can't talk to each other. This is data interoperabil...

The AI market is flooded with buzzwords and exaggerated claims. Every vendor promises transformation. Every tool claims to be revolutionary. Without a framework for evaluation, it's easy to chase s...