Discovery-Driven Planning¶
A high-uncertainty venture plan works backward from required success, lists its make-or-break assumptions, and stages commitment around tests that turn assumptions into knowledge.
Core Idea¶
Discovery-driven planning (DDP) is McGrath and MacMillan's high-uncertainty venture method: define what success must look like, work backward to economic and operating requirements, make implicit assumptions explicit, and stage tests before larger commitments.[ref-4961885d4e40][ref-cc34e618dc33] Learning changes whether the venture continues, revises or exits; it is still a plan, not improvisation.
Scope of Application¶
The 1995 article uses Kao Corporation's entry into floppy disks as a worked case according to Columbia Business School's abstract.[^ref-4961885d4e40-2] This attests the case setting, not any inaccessible numerical milestones or that Kao executed each step operationally. McGrath says the method can also apply to uncertain established businesses. It is most useful where forecasts contain more assumptions than knowledge.[^ref-cc34e618dc33]
Clarity¶
Separate a required result from an Assumption and test evidence. A reverse income calculation may reveal needed revenue; it does not show customers will buy. A milestone is discovery-driven only if the evidence it gathers can change the next funding or design decision. A fixed schedule or backward budget without explicit assumption tests is not the whole method.
Manages Complexity¶
In a constructed subscription venture, a $100,000 surplus goal plus $300,000 fixed cost at a 40% contribution margin requires $1 million annual revenue. At $50 annual revenue per paying customer-equivalent that requires 20,000 customers. Demand and retention are assumptions; a small paid pilot tests them before a broad sales buildout. The numbers are illustrative, not Kao data.
Abstract Reasoning¶
DDP trades some time and pilot spending for evidence that may avert larger losses from false premises. Immediate scale can enter a market faster but exposes more capital to untested assumptions. The authorial technical note evaluates success as useful learning for limited expenditure rather than closeness to initial forecast figures in highly uncertain projects.[^ref-311256ab225e]
Knowledge Transfer¶
The conserved relation is prospective commitment conditioned on learning about required success. The named method remains business-specific because profit/operating requirements, budgets and go/no-go authority matter. Its strict parent is the Planning prime; scenario planning is related but not its necessary genus.
[^ref-4961885d4e40]: Rita Gunther McGrath and Ian C. MacMillan, “Discovery-Driven Planning,” Harvard Business Review 73 (1995), accessible original opening. https://hbr.org/1995/07/discovery-driven-planning [^ref-4961885d4e40-2]: Rita Gunther McGrath and Ian C. MacMillan, “Discovery-Driven Planning”, Harvard Business Review 73 (1995), Columbia Business School faculty abstract of the same article. Its single-author and January 1 metadata conflict with the publisher's two-author July–August record; the publisher metadata governs the work identity. [^ref-cc34e618dc33]: Rita Gunther McGrath, author presentation material, “Discovery Driven Planning,” p. 7. https://ritamcgrath.com/pdf/ritamcgrath_topics.pdf [^ref-311256ab225e]: McGrath, MacMillan and Cooper, “Technical Note: Putting Discovery-Driven Planning to Work,” 2008, authorial description. https://store.hbr.org/product/technical-note-putting-discovery-driven-planning-to-work/KEL355
Relationships to Other Abstractions¶
Current abstraction Discovery-Driven Planning Domain-specific
Parents (1) — more general patterns this builds on
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Discovery-Driven Planning is a kind of Planning Prime
Discovery-driven planning is a venture-specific prospective goal-to-action plan with assumption tests and conditional commitments.
Neighborhood in Abstraction Space¶
Discovery-Driven Planning sits in a sparse region of the domain-specific corpus (88th percentile for distinctiveness): few abstractions share its structure, so a faithful description tends to retrieve it precisely.
Family — Unclustered & Miscellaneous (2551 abstractions)
Nearest neighbors
- Reverse Logistics — 0.82
- Feature Factory — 0.80
- Available-to-Promise — 0.80
- Pivot Thrashing — 0.80
- Mass Customization — 0.80
Computed from structural-signature embeddings · 2026-10-08