SELECTED BLOG POSTS
What we’ve learned from Microsoft’s own AI transformation – The Official Microsoft Blog
These lessons come together in three practical recipes: Persona Acceleration for a role, AI-Powered Process Redesign for a workflow and AI-First Possibility for a greenfield opportunity. Distilled from hundreds of transformation efforts, they are patterns teams can adapt — often in combination — to move faster and avoid reinventing the journey.
Machine god metaphors eat your brain – by Henry Farrell
The Machine God metaphor, which is really just a catchy rephrasing of longstanding notions among a particular community of AI people, has broken out of containment. It is leading, more or less by necessity, to a radical simplification of debate. If you believe that the machine gods are coming, so that super-empowered goal directed superintelligence is right around the corner, then the only thing that matters is ensuring that this superintelligence’s goals are aligned with the ones that you yourself care about. If you stare into the sun, you only see its glare, missing out on all the other questions that may be important. Thinking about these questions involves adopting a more pluralistic understanding of where you might look for knowledge, and asking about the much broader variety of futures that face us than machine god: good or machine god: bad. That’s it.
Think Clearly issue #10.08: The Messy Reality of Enterprise AI
The TL;DR (For the busy folks)
The problem: We have 270+ HR policies. Leaders struggle to know which rules apply to specific, nuanced employee situations.
The (current) solution: We built a custom AI agent that uses a slightly rigid, verifiable workflow. It doesn’t just "guess." It reads policy summaries, selects the relevant policies, extracts exact quotes, and cites its sources before making a recommendation.
The current status: A pilot group of leaders is using it. Feedback is overwhelmingly positive. They have not yet caught it making a mistake.
One big takeaway: Finding narrow, highly specific use cases (and forcing AI to follow strict workflows rather than just treating it as an oracle) is where the real enterprise value lies.
Jev introduces a new shape of LLM—System One, aka Decision Models
Last week TypeSafe AI unveiled Jev, their first example of a new category of model that they are calling “System One models” (I’m with Maggie Appleton, I think “decision models” is a better name for these). Jev is an interesting variant on the usual LLM format: it still accepts text inputs, but instead of text output it returns floating point numbers corresponding to categories, yes/no questions, ratings, and associated confidence scores.
AI Agents Are Changing How HR Leaders Work
From M&A risk and compliance to onboarding and employee experience, these leaders are moving beyond using AI and learning to build it. For the past two years, AI has dominated conversations about HR. Now, some HR leaders are moving from talking about AI to building with it.
Gartner predicts that by 2030, 60 percent of HR work tasks will be completed through intelligent agents or large language model-based interfaces. Gartner’s research suggests the question is no longer whether AI will change HR, but whether HR will help shape that change.
Nvidia’s Nemotron hit enterprise AI inflection point | Constellation Research
Nvidia’s open Nemotron models are at an inflection point where they’re likely to be the most dominant AI tools for enterprises. Simply put, you’ll either tailor Nemotron models on your own or consume them via your enterprise software vendor.
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