Philip Arkcoll

AI isn't just challenging how employees work. It's challenging how organizations operate. Many companies are about to discover that the hardest part of AI adoption isn’t generating more output; it’s building organizations capable of handling it.‍

Centralized vs decentralized organizational structure: research-backed data on which model wins, when hybrid works, and how to measure org design.

AI overspending starts with daily habits. Learn how employees can write tighter prompts, pick the right tool, & cut LLM costs without engineering changes.

A practical guide to monitoring LLM token consumption and API costs across OpenAI, Anthropic, and more with code examples and best tools.

Effective feedback is more than just a performance review. Master the art of constructive criticism to lead a high-performing team.

A complete guide to advanced workplace analytics. Learn key metrics, cohort analysis, and predictive models to optimize performance & reduce burnout.

The most common assumption about AI in the workplace is that it’s a universal accelerator. If everyone works faster, the entire organization should move faster too. But in reality, that’s not what happens. Instead, we’re seeing AI dramatically improve how individuals work, but not automatically improve how work flows across teams. And those are two very different things. Let’s dig in.

Is remote work more productive than the office? What randomized trials show about focus, collaboration and hybrid schedules, and how to measure it for your teams.

Explore top productivity challenges in hybrid work, from communication gaps to fragmented workflows, & learn practical strategies to improve performance.