Full Potential Diagnostic

Full-Potential Diagnostic

When to use

Use at the start of any performance improvement or transformation program to size the actual opportunity before committing to initiatives — replacing a vague "we think there's meaningful upside" with a quantified, metric-by-metric value-at-stake number. Also use to build the business case for launching a transformation, since a credible size-of-the-prize is usually what gets a program funded in the first place.

What it does

Compares current performance to a top-quartile or best-practice benchmark across every relevant metric (margin, cost ratios, working capital efficiency, productivity), converts each gap into a dollar value via a bundled calculator, and rolls the metrics into a single total value-at-stake figure — giving the transformation program a size, not just a direction.

Method

1. Select the metrics that matter for this business: typically gross margin, SG&A as % of revenue, working capital efficiency (inventory turns, DSO/DPO), and productivity metrics (revenue per employee) — chosen for relevance to this specific business, not a generic checklist applied blindly.
2. Establish current performance for each metric from actual financials, not estimates.
3. Establish the benchmark: top-quartile performance among true peers (same industry, similar scale) where available; where peer data doesn't exist, use the best internal benchmark (a comparable business unit, region, or plant) rather than an arbitrary aspirational number.
4. Run the bundled calculator (scripts/full_potential_calc.py) to convert each metric's gap into a dollar value at stake — percentage-of-revenue metrics translate directly; working-capital metrics translate through the capital freed by moving to benchmark efficiency.
5. Sum to a total value-at-stake figure and express it as a percentage of revenue — this is the number that anchors the transformation's ambition level in front of the board or investment committee.
6. Apply a realistic capture-rate discount explicitly, rather than presenting the raw gap as an expected outcome: real full-potential programs typically capture something well short of 100% of the identified gap within a realistic timeframe, and stating this openly is what makes the diagnostic credible rather than a sales pitch.
7. Rank the metrics by value at stake and pass the top few into initiative-prioritizer or transformation planning — the diagnostic sizes the opportunity, it does not by itself tell you how to capture it; that's the next step, not this one.
8. Revisit the diagnostic periodically during the program (not just once at the start) to track how much of the identified gap has actually closed — this connects directly to a value-realization discipline later in the program.

Inputs

• Current financial performance by metric
• Peer or internal benchmark data for each metric
• Revenue base and, for working-capital metrics, the relevant working-capital base
• Config saved as JSON matching the format documented at the top of scripts/full_potential_calc.py

Output format

Metric-by-metric table of current vs. benchmark vs. dollar value at stake; total value at stake as an absolute number and as % of revenue; explicit capture-rate caveat; ranked list of highest-value metrics to prioritize in the transformation plan.

Example

A $120M-revenue business shows a combined value at stake of $23.2M (19% of revenue) across gross margin, SG&A ratio, and inventory turns gaps to top-quartile peers. Rather than presenting $23.2M as the transformation's expected outcome, the diagnostic states the realistic range (40-70% capture within 18-24 months, based on typical program performance) — giving the board a credible $9-16M target instead of an inflated headline number that erodes trust when the program under-delivers against it.

Common pitfalls

• Presenting the full theoretical gap as the expected outcome instead of applying a realistic, stated capture-rate discount.
• Benchmarking against an aspirational or cherry-picked "best in class" example instead of a true, comparable peer set.
• Running the diagnostic once at kickoff and never returning to it during the program to check actual progress against the sized opportunity.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Natan-Mohart
• Source: Natan-Mohart/24-strategy-skills-for-claude
• License: MIT

Install and usage instructions live in the source repository linked above.