Minimal viable friction for better AI and business decisions
Minimal viable friction adds the smallest useful pause at the decisions where assumptions, consequences and accountability need to be visible before action becomes difficult to reverse.
What is minimal viable friction?
Minimal viable friction is the smallest amount of intentional resistance needed to improve a decision without creating unnecessary delay.
It is designed for decisions that are uncertain, consequential or difficult to reverse. Routine and low-risk work should remain fast. Higher-impact decisions should include a short checkpoint that makes assumptions, consequences and ownership explicit.
The framework is not another approval layer. It is a decision-design method that protects judgement where speed can otherwise hide weak reasoning.
The central principle: keep routine work smooth, but add a deliberate pause where a poor decision would be costly, harmful or difficult to unwind.
Why removing every form of friction creates risk
Organisations have spent years simplifying approvals, automating handoffs and reducing waiting. Much of that work has improved service and productivity.
The problem begins when all friction is treated as waste. Some checkpoints create the moment where a decision owner tests what they believe, considers wider impacts and accepts responsibility for the choice.
When those moments disappear, speed can become a substitute for confidence. A recommendation moves directly into action because the system allows it, not because the reasoning has been tested.
Bad friction: repeated approvals, duplicated evidence and waiting that do not improve the decision.
Useful friction: a proportionate check that exposes assumptions, impact or uncertainty before commitment.
Why minimal viable friction matters for AI
AI can produce summaries, classifications, forecasts and recommendations quickly. Fluency and speed can make an output feel more authoritative than the evidence supports.
Minimal viable friction gives teams a practical way to decide when an AI-supported action should remain smooth and when human judgement must be made visible.
The New Zealand Public Service AI Framework emphasises human oversight, transparency and explainability across the AI lifecycle. These principles are useful beyond government because they connect technology use to accountable decisions.
See the New Zealand Government’s Public Service AI Framework.
Where minimal viable friction belongs
The framework should be triggered by the nature of the decision, not by organisational hierarchy alone.
Where friction should be avoided
Minimal viable friction loses value when it is applied to every task.
Routine, repeatable and well-understood work should be easy to complete. Standard reporting, low-risk administration, approved knowledge retrieval and reversible workflow actions should not require a new decision paper.
Over-designed control
- Every action requires approval
- The same evidence is entered repeatedly
- Routine decisions wait for senior leaders
- Forms document activity rather than reasoning
- Teams work around the process to deliver
Proportionate control
- Low-risk work follows approved rules
- Decision owners act within clear authority
- Only material exceptions are escalated
- Evidence is captured once and reused
- Higher-risk actions receive stronger review
The three minimal viable friction checkpoints
The framework uses three lightweight checkpoints. They can be completed in a short written note, a meeting, a workflow form or an approval screen.
Assumption check
State what must be true for the decision to work and identify the assumptions that have not yet been tested.
Impact scan
Consider the effects beyond the immediate workflow, including people, customers, systems, incentives, privacy and service quality.
Judgement statement
Record why this is the right decision now, who owns it and what evidence would cause the organisation to revisit it.
Checkpoint one: make assumptions visible
Weak decisions often appear strong until their assumptions are stated plainly.
The decision owner does not need to document every possibility. They should identify the assumptions that materially affect value, feasibility, adoption or risk.
The purpose of the assumption check is not to eliminate uncertainty. It is to stop uncertainty being mistaken for fact.
Checkpoint two: scan the wider impact
A decision can optimise one workflow while making the wider system worse.
An automated triage process may improve speed but create more rework for the receiving team. A self-service tool may reduce internal handling while making support harder for vulnerable customers. A new AI assistant may save drafting time while creating additional review and governance work.
The impact scan checks whether the local gain creates cost, pressure or risk elsewhere.
Checkpoint three: record the judgement
The judgement statement creates a concise record of the reasoning at the point of commitment.
It should be short enough to complete and useful enough to guide later review. The objective is not defensive documentation. It is clarity about why the organisation acted.
Applying minimal viable friction to AI use cases
The level of review should match the intended use, information involved and potential impact.
| AI decision | Useful friction | Evidence to retain |
|---|---|---|
| Approve an AI use case | Confirm the business problem, users, data, benefit and risk | Use-case definition and accountable owner |
| Move a pilot into production | Review performance, exceptions, adoption and operating ownership | Test results, controls and implementation decision |
| Use AI output without review | Define the permitted task, quality threshold and excluded situations | Validation results and monitoring rules |
| Give an agent authority to act | Set permissions, limits, approval thresholds and recovery steps | Authority model, logs and escalation path |
| Process personal information | Assess necessity, privacy, access, retention and disclosure | Privacy assessment and approved controls |
| Automate a material decision | Examine fairness, explainability, appeal and human accountability | Decision design and review requirements |
Privacy is a trigger for stronger friction
Where AI or automation changes the handling of personal information, the organisation needs a clear purpose and proportionate privacy assessment.
The Office of the Privacy Commissioner states that the Privacy Act applies to organisations using AI tools in New Zealand and recommends understanding the system and completing a Privacy Impact Assessment before use.
This does not mean every low-risk AI task requires the same review. It means personal information, sensitive decisions and new forms of disclosure should trigger stronger evidence and accountability.
See the Privacy Commissioner’s Artificial Intelligence and the Information Privacy Principles guidance.
Minimal viable friction and investment decisions
The same framework can improve technology, transformation and operating-model decisions.
New Zealand Treasury’s Better Business Cases approach gives decision-makers a structured way to consider strategic need, value, viability, affordability and achievability before committing to investment.
Minimal viable friction can sit inside an existing business-case or investment process by focusing attention on the few assumptions and impacts that require explicit judgement at the current stage.
Business case: develops the evidence required for an investment decision.
Minimal viable friction: makes the critical judgement visible at the point where the decision moves forward.
See New Zealand Treasury’s Better Business Cases guidance.
How to embed the framework without creating bureaucracy
Minimal viable friction should be built into existing work rather than added as a separate governance process.
Identify the inflection points
Find the moments where decisions become expensive, material or difficult to reverse.
Define the trigger
Use simple criteria such as data sensitivity, impact, uncertainty, authority or financial commitment.
Assign the decision owner
Name the person responsible for the reasoning, trade-off and outcome.
Capture only useful evidence
Keep the artefact short and reuse information that already exists in the workflow.
Monitor the decision
Track whether assumptions remain valid and whether the outcome requires adjustment.
Remove unnecessary friction
Reduce or eliminate checkpoints that do not change decisions, improve evidence or prevent material harm.
Measuring whether minimal viable friction works
The framework should improve decisions without creating a new source of delay.
How Changeable applies minimal viable friction
Changeable uses the framework to connect AI governance, process improvement and implementation decisions.
Explore Changeable’s AI use-case development, AI governance, process improvement, workflow automation and AI agents services.
Frequently asked questions about minimal viable friction
What is minimal viable friction?
Minimal viable friction is the smallest intentional pause or control required to improve a decision without adding unnecessary delay or bureaucracy.
Is minimal viable friction another approval process?
No. It should be embedded into existing decisions and used only when uncertainty, impact or irreversibility justify additional judgement.
When should minimal viable friction be used?
Use it for decisions that are difficult to reverse, materially affect people or the organisation, rely on uncertain assumptions or expand automated authority.
When should friction be avoided?
Avoid adding it to routine, reversible and well-understood work where an approved process already manages the risk adequately.
How does minimal viable friction apply to AI?
It helps teams decide when AI outputs, automated decisions or agent actions require assumption checking, human review, evidence and escalation.
What are the three checkpoints?
The three checkpoints are an assumption check, an impact scan and a short judgement statement recording ownership and the conditions for review.
Can Changeable embed minimal viable friction into our workflows?
Yes. Changeable can map decision points, define proportionate triggers and build the checkpoints into AI governance, operational workflows and automation.
Protect judgement without rebuilding bureaucracy.
Bring us the AI use case, automation decision or governance process. We will help identify where work should remain smooth and where a small, deliberate pause will protect the outcome.