100% of our team is active on AI tools. 71% of our org is active in Claude alone daily.
Here's what got us there:
1. We set the expectation that AI fluency is a baseline expectation at Scribe, and a big part of how we move faster and win. Usage spiked every time we
Companies have years of data that exists nowhere else on the internet.
Thomson-1 is the first example. It's trained on decades of proprietary content, technology, and domain expertise no other company can match. The result is a model Thomson Reuters fully controls, without the
Now that the base open weights AI models are getting far better, and post training infra is becoming more mature and commercialized, there are going to be all new plays for companies that have large amounts of data to have their own models.
Licensing data for external model
A few observations on AI transformation in the enterprise from recent conversations:
* “Agentic” does not necessarily mean fully autonomous. The more durable implementations are combining deterministic workflows with AI steps, especially where reliability and debugging matter.
It's the same planning fallacy - we overestimate how much we can do in the short-term, and underestimate how much we can get done long-term.
It's so common in tech, it has a name - Amara's Law.
People see how sophisticated the models are and get happy eyes about what's
This chart from @McKinsey is very telling and reflective of human nature and organizations. Impact of AI on organizations is slower than anticipated, as it is with almost all technology. However, expectations continue to be aggressive.
We overestimate AI's impact in the short
