Outcome-First Decisions: Keep, Change, or Kill

📊 Full opportunity report: Outcome-First Decisions: Keep, Change, or Kill on ThorstenMeyerAI.com — validation score, market gap, and execution plan.

TL;DR

Outcome-First Decisions is a framework that guides organizations to evaluate initiatives by current outcomes, enabling them to keep, change, or kill projects. It emphasizes pruning to improve portfolio health and efficiency.

A new decision framework called Outcome-First Decisions has been introduced to help organizations evaluate ongoing initiatives based on current outcomes rather than sunk costs or emotional attachment. This approach aims to improve portfolio health by encouraging systematic pruning of underperforming projects.

Outcome-First Decisions centers on a single question: what outcome is this initiative producing right now, and is that outcome worth its ongoing cost? It uses a mechanism called the Worth Filter to guide decisions, which produces three verdicts: keep, change, or kill. The framework is open source under the AGPL-3.0 license and designed to be provider-agnostic and local-first, enabling frequent, honest reviews without external dependencies. It aims to address the common problem of organizations continuing projects that no longer serve their goals, often due to emotional or sunk cost biases. The framework emphasizes that the hardest decision is often to stop or kill an initiative, which is crucial for freeing up capacity for new or more valuable work.

Outcome-First Decisions — Keep, Change, or Kill · Built in Public Day 8/19
Built in Public · Day 8 / 19 ThorstenMeyerAI.com · the operator portfolio
The Decision Layer · Day 08 Dispatch

Outcome-First Decisions — keep, change, or kill

The hardest decision isn’t what to start — it’s what to stop. Judge every initiative by the outcome it produces now, not the effort already spent.

01 The Worth Filter
The Worth Filter
is the outcome worth the ongoing cost?
judged forward (outcome) — not backward. Ignored: sunk cost · effort spent · identity
✓ Keep
Affiliate cluster A
compounding revenue
Channel E
reach still growing
↻ Change
Product C
right problem, wrong shape
alter deliberately — don’t drift
✕ Kill
Experiment B
flat · high upkeep
Side project D
zero traction · sunk cost
3verdicts: keep · change · kill outcomesthe only input that counts AGPLopen source · local-first
02 Why stopping is the leverage
kill
the verdict everything in human nature avoids — made normal, not a failure.
forward
judge what it will produce next, not what you’ve already spent. Sunk cost is gone either way.
capacity
killing dead work reclaims the focus and capital trapped in it — the cheapest growth there is.
03 The thesis the whole series inherits
01
Local-first
Reviews run on owned compute — cheap enough to run as often as honesty requires.
02
Provider-agnostic
The reasoning isn’t welded to one model. Swap freely; no lock-in.
03
Non-developer build
A small, opinionated framework — AGPL-3.0, open so the method stays inspectable.
04
Edit by subtraction
The whole product is subtraction — killing what no longer earns its place.
04 The operator constellation
18 products · one foundation
Today: Outcome-First lit — the keep/change/kill review that closes the loop. The Decision layer is complete: validate → plan → review.
Content
DojoClaw
RoundupForge
Stenvrik
ChannelHelm
IdeaNavigator
Decision
IdeaClyst
Threlmark
Outcome-First
Platform
Grimfaste
Delvasta
Open / Reg
Glasspane
QAtrial
Markets
Polybot
TradingAgents
Defense / Intel
Argus
VigilSAR
VigilSAR-Bench
Diagnostic
World Model Readiness
Local-first · Provider-agnostic foundation

Independent commentary, produced with AI assistance under human editorial oversight. The views are the author’s own and may change. Outcome-First Decisions is open source under AGPL-3.0, provided “as is” without warranty; see the repository LICENSE. The framework’s verdicts are reasoning aids based on the inputs given and may be wrong — decision support, not decisions; verify independently before acting. Product and company names are trademarks of their respective owners; mention does not imply endorsement.

ThorstenMeyerAI.com · Built in Public · Day 8 of 19 · © 2026 Thorsten Meyer

Why Outcome-First Decisions Reshape Portfolio Management

This framework matters because it offers a disciplined way to cut through emotional biases and sunk costs, enabling organizations to focus resources on initiatives with meaningful current outcomes. It promotes a culture of pruning, which can lead to more efficient use of capital, attention, and effort. By making kill decisions easier and more systematic, organizations can avoid the trap of maintaining dead projects that drain focus and resources, ultimately improving agility and strategic clarity.

Amazon

project portfolio management software

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The Challenge of Continuing Dead Projects

Many organizations struggle with the long tail of ongoing projects that neither succeed nor are actively killed. These projects consume attention, resources, and capital without delivering value, often justified by sunk costs or emotional attachment. The problem is compounded by the difficulty of making the decision to stop, as it conflicts with organizational identity and effort justification. Historically, decisions to continue or stop initiatives have been biased by backward-looking metrics, making it hard to objectively evaluate current worth.

“Outcome-First Decisions is about judging every initiative solely by its current outcome and whether it’s worth its ongoing cost.”

— Thorsten Meyer, creator of the framework

Amazon

decision-making framework tools

As an affiliate, we earn on qualifying purchases.

As an affiliate, we earn on qualifying purchases.

Limitations and Risks of Outcome-First Decisions

It is not yet clear how accurately the Worth Filter can measure outcomes, especially in complex or slow-start projects. There is a risk of misjudging or gaming the metrics, leading to premature kills or missed opportunities for slow-growing initiatives. Additionally, the framework cannot provide emotional courage; decision-makers may still resist stopping projects despite analytical clarity. The effectiveness of the framework depends heavily on the quality of outcome measurement and organizational discipline.

Amazon

initiative evaluation software

As an affiliate, we earn on qualifying purchases.

As an affiliate, we earn on qualifying purchases.

Next Steps for Adoption and Refinement

Organizations interested in Outcome-First Decisions are expected to pilot the framework within their portfolios, refine their outcome metrics, and develop organizational discipline around pruning. Further development may include integrating the framework into existing portfolio management tools and sharing case studies on its impact. Ongoing feedback will help improve the Worth Filter’s accuracy and usability, making it a standard part of strategic review cycles.

Amazon

portfolio pruning tools

As an affiliate, we earn on qualifying purchases.

As an affiliate, we earn on qualifying purchases.

Key Questions

How does Outcome-First Decisions differ from traditional project evaluation?

Unlike traditional methods that often focus on effort, sunk costs, or past investment, Outcome-First Decisions evaluates initiatives solely based on their current outcomes and ongoing worth, promoting more objective pruning.

Can this framework be applied to all types of projects?

While designed to be provider-agnostic and flexible, its effectiveness depends on the ability to accurately measure outcomes. Slow-start or complex projects may require careful metric selection and judgment.

Is this framework suitable for large organizations with many projects?

Yes, its local-first and open-source design allows frequent, honest reviews, making it suitable for large portfolios seeking disciplined pruning processes.

What are the main challenges in implementing Outcome-First Decisions?

The primary challenges include establishing reliable outcome metrics, overcoming emotional resistance, and ensuring organizational discipline to make kill decisions when justified.

Source: ThorstenMeyerAI.com

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