Start with the decision you are actually trying to make.
These are practical tools for situations where the answer is not obvious because strategy, economics, speed, talent and risk are pulling in different directions.
Capability Boundary
Use this when you are deciding what the business should own, control, partner for, outsource, buy or automate within a specific capability.
The model combines established thinking from Porter, Coase/Williamson, strategic sourcing, Pisano/Shih and real-options theory, then extends it for modern capability design.
The point is not to get a “score.” It is to make the tradeoff visible.
Define one real decision.
Do not evaluate “marketing” or “technology.” Pick the capability, workflow or constraint you are actually deciding about.
Use the established lenses explicitly.
Start with advantage and market governance, then apply learning, optionality and automation as visible overlays.
Separate ownership from execution.
A company may own the strategy, data and decision rights while partnering for execution and automating repeatable work.
Two other decisions that benefit from explicit economics.
These are working tools, not lead-generation calculators. The assumptions are visible and the outputs are meant to support an executive conversation.
Can the growth plan actually work?
Bridge current revenue to the ending run-rate target through retention, expansion, pricing and new business—then test demand, sales capacity, timing, contribution margin and CAC payback.
Open model →Which initiative creates the most economic value?
Compare up to three initiatives using revenue contribution, recurring cost savings, implementation cost, ramp time, payback and a user-supplied illustrative valuation multiple.
Open model →Two operating questions I’m still turning into useful tools.
I’m leaving these visible as questions, not pretending unfinished frameworks are products.
Where should AI change the work itself?
Separate work that should be automated, augmented, redesigned or kept primarily human based on judgment, consequence, repeatability and data readiness.
In developmentIs the future business being starved by the current one?
Compare legacy performance with future-model readiness to see where capital, talent or incentives are slowing the transition.
In developmentIf a model does not make the decision clearer, it does not belong here.
I am not interested in proprietary-sounding frameworks for ordinary business questions. The tool has to make a real decision easier to inspect, explain or improve.