Problems › Where Should We Invest Next?
What has to survive scrutiny before it reaches the board pack — applied to where should we invest next?, where the numbers have to reconcile before the story does.
A CFO is rarely short of analysis. What is scarce is analysis that survives being questioned — where every number has a visible derivation, the assumptions are stated rather than embedded, and the case does not quietly change shape when one input moves.
That is a different requirement from the one an operating team has. An operator needs a decision they can act on this week. A finance function needs a decision it can still defend in six months, to an auditor, a lender or a board that has since watched the market move.
The practical consequence is that the useful output is not a recommendation. It is a recommendation with its arithmetic attached, a stated downside, and a named condition under which it stops being the right answer.
Capital allocation goes wrong when the loudest line gets funded rather than the one with the best return on the next dollar. Most businesses allocate by history and by advocacy: the lines that got money last year get it again, and the person who argues best gets the increment. Neither has anything to do with where the next dollar earns most.
Read the full treatment of where should we invest next?, including the signals that distinguish it from neighbouring problems and the move that usually makes it worse.
What has to survive scrutiny before it reaches the board pack. In practice that means four things:
✓ Every figure traces to either your own ledger or a stated assumption — no unattributed benchmarks
✓ The downside case is genuinely adverse rather than the base case reduced by a polite margin
✓ Sensitivities are explicit: which input, moved how far, changes the conclusion
✓ The recommendation carries a failure condition that can be observed before the money is gone
The failure mode to watch for. The characteristic finance-side failure is precision without provenance — a model accurate to two decimal places built on an assumption nobody wrote down. It survives internal review because it looks rigorous, and fails the first time somebody external asks where a number came from.
This question routes to Growth Portfolio Framework (catalog id bcg-v2) and works through return by line, growth vs share, cash generation, payback. The output is a sequence with a stopping rule, and every figure carries its derivation — which is the property that matters when the numbers have to reconcile before the story does.
Read a complete report and judge whether it would survive your own scrutiny. No email required.
Each of these applies the same question to one industry's actual economics, with an unedited excerpt from a real completed analysis.
Percision is the wrong tool if you already know the answer and only need execution capacity, or if the business is pre-revenue — then the constraint is evidence about the market, not analysis of your own figures. Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library. Also wrong if you need facilitation, politics, or someone to sit with a lender or buyer. Those are human jobs.
Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library.
That is what it is built for. The output states its assumptions inline, shows the derivation of each figure, and separates what came from your data from what the engine inferred. Where the data does not support a conclusion it says so rather than filling the gap, which is the property that matters when somebody checks.
A general model produces a fluent answer with no traceable derivation and no memory of what it assumed last time. The difference that matters to a finance function is not quality of prose — it is that here the arithmetic is visible, the assumptions are named, and the same company profile produces a consistent answer across a planning cycle.
Few are. The analysis works from what you have and marks explicitly which conclusions depend on the weakest inputs, which is usually more valuable than the answer itself — it tells you which part of the ledger is worth fixing first because it is currently load-bearing.
Seven to fifteen minutes against eight to twelve weeks. The honest comparison is not like-for-like: a consulting engagement includes primary research and stakeholder work this does not. What it replaces is the analytical core — the modelling, the framework application, the option ranking — which is the part that takes the longest and varies least.
Price the durability explicitly. A return that decays needs a stated half-life; once each option carries one, options with different horizons become comparable rather than a matter of taste.
Percision is the wrong tool if you already know the answer and only need execution capacity, or if the business is pre-revenue — then the constraint is evidence about the market, not analysis of your own figures. Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library. Also wrong if you need facilitation, politics, or someone to sit with a lender or buyer. Those are human jobs.
Percision is not a lawyer, tax advisor, auditor, licensed appraiser, clinical or regulatory filer, or an AI implementation shop. It does not do HR casework, creative-only brand work, or impersonate a named consulting firm. It is a strategy analysis engine — not a template library.
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