Discovery-driven planning: A better way to evalua...

My 4th article on #entrepreneurialfinance…

Dear finance executive,

Growth investments look more and more like ventures. They venture into unknown territory: New markets, new business models, new operating models.

Yet, most firms still evaluate venture investments as if uncertainty was the exception rather than norm. Many investment evaluations reward precision over truth. Teams present immaculate spreadsheets, confident market narratives, and linear rollouts. Leaders then approve investments that look ready — even though most of the underlying assumptions have never been validated.

That mindset may have worked when markets were slow. Today it destroys capital.

Rita McGrath and Ian MacMillan warned about this decades ago. Euro Disney remains the archetype: Assumptions about length of stay, merchandising, and dining patterns were never tested and did not materialize. This led to over $1B in early losses despite hitting visitor numbers.1

The deeper issue: Ventures are funded based on assumptions pretending to be knowledge. In a world of accelerating competition, shifting technologies, and unpredictable customers, those assumptions multiply. What used to be a planning exercise is now a risk-management exercise.

Discovery-Driven Planning (DDP) gives Finance a rigorous, structured way to evaluate ventures under uncertainty — by forcing ideas to earn their funding through evidence, not optimism.

[

](https://substackcdn.com/image/fetch/$s_!HO-n!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fb846e1de-b695-4c82-a09f-275bb5d05857_1024x1024.png)

Discovery-Driven Planning (DDP) as a venture evaluation system

DDP is fundamentally a method for evaluating ventures by treating every plan as a collection of hypotheses. It replaces prediction with disciplined learning.

DDP’s modern form — now widely used in innovation, R&D, digital transformation, and venture incubation — incorporates three essential ideas:

Only validated assumptions deserve capital.
Funding is released in tranches tied to evidence.
When new information contradicts the plan, the plan changes

This “planning to learn” mindset is what keeps companies from burning millions on attractive but unrealistic ventures.

How leading companies evaluate ventures using DDP

Flatiron Health: Pivot only after validating assumptions

Flatiron didn’t pivot from ad-tech into oncology data because the story was compelling. They ran small experiments, tested feasibility, and validated clinician demand. Only once core assumptions held up did they scale — ultimately leading to a $1.9B acquisition by Roche. A classic DDP evaluation: define success, validate cheaply, pivot decisively.

Best Buy: Evaluating digital initiatives through experiments, not forecasts

Best Buy didn’t “approve” transformation projects; it tested them. Price-matching, service expansions, store-in-store concepts, and fulfillment pilots were all evaluated based on measurable learning. Ventures progressed only when evidence supported the next step. Leadership embedded the DDP logic: capital follows proof.

Kloeckner: Digitalization in industrial B2B through phased validation

Instead of launching a massive digital overhaul, Kloeckner evaluated each venture — online ordering, AI pricing, digital services — via small-scale tests. Each pilot had defined assumptions to validate (e.g., “customers will buy steel online if ordering is simplified”). Funding expanded only when assumptions held. This DDP discipline modernized a century-old industrial firm without catastrophic bets.

AGC: R&D funding governed by DDP logic

AGC now evaluates R&D ventures using assumption checkpoints inside its Stage-Gate process. Funding is tied to validation events, not deliverables. Assumptions about technical feasibility and market adoption must be proven before progressing. This effectively treats every R&D initiative as a real-options portfolio — only invest more when the venture earns the right to scale.

Across industries, the shift is clear:

**Ventures aren’t approved, they’re tested.

Capital isn’t allocated, it’s earned.**

How to evaluate ventures using Discovery-Driven Planning

Finance’s role is not to predict venture outcomes — it’s to evaluate venture viability. DDP provides a structured sequence for doing exactly that.

1. Start with the required economics (reverse income statement)

Before evaluating potential, define exactly what the venture must deliver financially.

Work backward from required profit and return thresholds.

This immediately filters out ventures that cannot meet strategic or economic relevance.

2. Surface all critical assumptions

Every venture model hides dozens of assumptions:

Demand
Pricing
Adoption curves
Conversion
Cost-to-serve
Channel economics
Competitor responses
Operational feasibility

DDP calls for making these assumptions explicit. Recent DDP discussions emphasize the importance of competitor responses, because competitive reaction can erode margins quickly.

This is where most venture evaluations fail: assumptions remain invisible.

3. Benchmark assumptions against external reality

This is where overconfidence collapses. Market sizing, pricing power, competitive dynamics, cost curves, and adoption rates should be benchmarked against best-in-class data.

A significant number of corporate ventures die in this step — which is exactly the point. Better to invalidate assumptions now than during execution.

4. Translate economics into operational requirements

This step converts “theoretical viability” into “real-world feasibility.”

If a model needs 40,000 enterprise sales conversations, that operational truth matters more than the initial top-line ambition.

If a digital venture assumes premium pricing, what evidence of differentiation must exist?

McGrath’s recent research reinforces this: DDP requires testing not just demand assumptions but operating model assumptions; something startups often ignore.

5. Evaluate the venture through milestone-based evidence

In DDP, milestones are not activities. They are assumption tests.

What must be true for the venture to deserve the next tranche of funding?
What evidence will validate or invalidate the key assumptions?
What is the stopping rule if assumptions fail?

Modern DDP practice has strengthened this discipline:

Budget follows evidence.

Subscribe now

Practical takeaway: Finance’s strategic advantage in venture evaluation is not superior forecasting. It’s structured skepticism. Our role is to force ideas to earn their next round of funding, not with slideware, but with validated assumptions. What must be true for this to work and how will we test it?

Your competitive edge is not predicting the future. It’s converting assumptions into knowledge faster, and acting decisively on what you learn.

All the best,

Sebastian

PS: Related articles…

Thanks for reading CFO Impulse! This post is public so feel free to share it.

McGrath, R. G. & MacMillan, I. C. (1995) Discovery-Driven Planning. Harvard Business Review, July–August.

McGrath, R. G. & MacMillan, I. C. (1999) Discovery-Driven Planning: Turning Conventional Planning on its Head. DeepCanyon / HP e-publishing.

Gallo, A. (2017) A Refresher on Discovery-Driven Planning. Harvard Business Review, February.