White Papers
Research and thinking from the Clayton team at Sagely Advisory on AI coaching, leadership at scale, and the future of organizational standards.
Why AI Assistants Don't Scale: The Case for Structured AI Coaching
Claude Projects, AI assistants, and Gemini Gems are powerful for individual productivity. But when a leader needs their entire team to produce work to a consistent standard — and wants to know if it's working — something more is needed.
Building An AI Coach Is Easy (Except for the Hard Bits)
The concept takes an afternoon. The content curation takes weeks. The distribution requires infrastructure you didn't plan for. And getting the AI to consistently behave the way you want? That's a problem that never fully goes away.
How Clayton Scores: Rubric-Based AI Assessment Explained
A score without a rubric is just a number. This paper explains how Clayton's structured scoring system works — from dimension-level Likert scales to final score calculation to ROI measurement — and why scoring consistency is the feature that matters most.
How to Track AI Assistant ROI: The Visibility Problem Nobody Is Solving
Speed is not ROI. This paper explains how to measure whether your AI assistant investment is actually improving the quality of work your team produces — and reducing the cost of reviewing it.
Why Generic LLMs Suck at Business
A case study: one model, one strategy question, two knowledge bases — and only one answer survived scrutiny.
Management by AI: The New Best Practice Leaders Need to Master
MBAI is not the abdication it sounds like. It is handing off discrete parts of the management function — first-pass review, coaching on the gap, keeping the operating rhythm honest — to AI that carries your standards, so your attention goes where only a manager's can.