Innovation Portfolio Matrix
Balance incremental, adjacent and transformational innovation by classifying initiatives, adjusting investment, testing target mixes and observing how evidence and budget constraints reshape the portfolio.
A healthy innovation portfolio balances exploitation of the current business with exploration of new growth.
The matrix classifies initiatives using market/customer novelty and capability/solution novelty, then compares how investment, potential value and evidence confidence are distributed across incremental, adjacent and transformational innovation.
Innovation Portfolio Matrix — Step by Step
Eight locked stages move from portfolio context through classification, investment, evidence, target mix and balance lens, then apply a funding shock and conclude with management review.
Novelty Matrix, Investment Mix and Portfolio Balance
Reveal the portfolio context to begin.
Compare category, investment, value and evidence confidence initiative by initiative.
Test a Portfolio Mix
Experiment Mode is isolated from the guided demonstration. Change category investment and the intended mix to see how portfolio variance responds.
Test innovation-portfolio judgement.
Innovation portfolio cues
Incremental
Relatively familiar markets and capabilities. Typically supports improvement, extension and near-term delivery.
Adjacent
Extends into a new market, capability or proposition while retaining meaningful familiarity on at least one dimension.
Transformational
Combines high market/customer novelty with high capability/solution novelty and therefore greater learning demand.
Investment Mix
Assess the percentage of actual resources committed to each category rather than relying on idea count.
Evidence-adjusted View
Discount potential value by evidence confidence to see where portfolio upside depends on weaker assumptions.
Target Mix
Use a context-specific target as a policy guide, then update it when strategy or constraints materially change.
Make novelty, resource commitment, value and evidence visible together, then rebalance when strategy, learning or constraints change.