The underlying questions do not change much across enterprises. What varies is the starting point, the urgency, and the mix of functions that need attention first.
In every role I have had, I operated like a GM of the business. I build a diagnostic framework, stand up the data to make it actionable, design the repeatable operating model and enable the teams to run it at scale. Twenty years of doing this across functions and company stages is how I perfected my playbook.
The underlying questions do not change much across enterprises. The functions are the same — HR, finance, support, product development. The intervention playbooks are largely the same and the KPIs that validate outcomes are comparable. What varies is the starting point, the urgency, and the mix of functions that need attention first.
This is my playbook. Across five functions I have closely worked with across industries — GTM and revenue, product and engineering, HR and org design, finance and ops, and customer success — each broken into three tool types: a diagnostic framework, an AI-powered application, and a competitive moat that compounds across engagements. I have tagged each by EBITDA impact: top line, bottom line, or both.
In the agentic AI era, the underlying logic of these workflows is well established. What we gain through AI is speed and scale — not replacing the judgment, but removing the friction between diagnosis and action.
I am building the AI-native versions of several of these tools. The table below maps the full framework. If you are working through similar questions — whether inside a company trying to move faster, or advising across several — I would be glad to think through it together.