I've been thinking about how you might apply agentic analysis to the software project analysis process we used to run. Your article reminded me of a glib comment made by the then CTO about that way to get an effective investment management process would be to RIF the business middle management ranks by 80%.
I specifically appreciated your analysis of game theory as a tool for CIOs to elicit cooperation. From an Economics-Mathematics perspective, your breakdown of the barriers to maximizing EBITDA lift was a fresh take. Often, enterprise tech discourse focuses on the 'vibe' of the code, but ignores the deadweight loss and value leakage that occur during adoption. Framing IT hurdles as game-theoretic problems makes the solution feel much more engineering-led rather than just cultural.
If you’re looking for feedback on the 'product': I think a brief 'Bottom Line Up Front' (BLUF) section at the start of the longer deep dives—like the one with Philip Rathle—would be helpful for the LinkedIn version. It mirrors that high-impact McKinsey style and helps 'busy' readers anchor the technical insights to a strategic takeaway immediately.
If agentic AI reduces the cost of analysis but doesn't resolve misaligned incentives, what does? Is this just an organizational design problem that better structure or governance could solve, or is coordination friction essentially a permanent feature of large organizations?
This resonates. Most corporate friction isn’t due to lack of intelligence but lack of coordination. Even if AI agents become extremely capable, they don’t resolve misaligned incentives across teams, budget owners, or functions. If anything, lowering the cost of analysis could increase coordination overhead because more stakeholders will have inputs into decisions.
I've been thinking about how you might apply agentic analysis to the software project analysis process we used to run. Your article reminded me of a glib comment made by the then CTO about that way to get an effective investment management process would be to RIF the business middle management ranks by 80%.
"I didn't get a harrumph out of that guy..." https://www.youtube.com/watch?v=uTmfwklFM-M
I specifically appreciated your analysis of game theory as a tool for CIOs to elicit cooperation. From an Economics-Mathematics perspective, your breakdown of the barriers to maximizing EBITDA lift was a fresh take. Often, enterprise tech discourse focuses on the 'vibe' of the code, but ignores the deadweight loss and value leakage that occur during adoption. Framing IT hurdles as game-theoretic problems makes the solution feel much more engineering-led rather than just cultural.
If you’re looking for feedback on the 'product': I think a brief 'Bottom Line Up Front' (BLUF) section at the start of the longer deep dives—like the one with Philip Rathle—would be helpful for the LinkedIn version. It mirrors that high-impact McKinsey style and helps 'busy' readers anchor the technical insights to a strategic takeaway immediately.
If agentic AI reduces the cost of analysis but doesn't resolve misaligned incentives, what does? Is this just an organizational design problem that better structure or governance could solve, or is coordination friction essentially a permanent feature of large organizations?
This resonates. Most corporate friction isn’t due to lack of intelligence but lack of coordination. Even if AI agents become extremely capable, they don’t resolve misaligned incentives across teams, budget owners, or functions. If anything, lowering the cost of analysis could increase coordination overhead because more stakeholders will have inputs into decisions.