AIFC-041: AI Waste Backlog

Status: Draft 0.1
Standard: AI-First Community Standard
Short name: AIFC
Builds on:

AIFC-000 Manifest of the AI-first community
AIFC-001 Core Concepts
AIFC-002 Community Model
AIFC-004 Feedback and Change Proposals
AIFC-010 Knowledge Structure
AIFC-020 Human-Managed AI
AIFC-024 Human Capability Reserve
AIFC-030 AI Capacity Planning
AIFC-033 AI Budget and Cost Control
AIFC-040 AI Retrospective

Purpose of this document: Define the AI Waste Backlog as a structured place for recording, classifying, prioritizing, and resolving repeated or low-value AI capacity use. The AI Waste Backlog helps the community convert wasted AI capacity into workflow conversion, templates, validators, skills, rules, cleanup, or decisions to stop doing the work.

1. Purpose of this document

This document defines the AI Waste Backlog.

An AI-first community must not only consume AI capacity.

It must also notice when AI is being used unnecessarily, repeatedly, expensively, with low quality, or as a substitute for a missing system capability.

The AI Waste Backlog captures situations where AI:

repeatedly performs routine work,
generates outputs that nobody uses,
creates too much review work,
replaces a simple rule or validator,
fixes the same type of issue over and over,
fills a gap in the knowledge base,
hides a poor workflow,
increases AI dependency,
creates noise,
or consumes capacity without corresponding value.

The AI Waste Backlog is not a list of failures.

It is a list of opportunities to improve the system.

2. Core principle

The core principle of this document is:

AI waste is not only a cost problem. It is a signal that the system can be improved.

AIFC says:

Repeated AI work should become system capability.

If AI repeatedly solves the same problem, the community should ask:

Why is AI still solving this?
Should this become a template, validator, workflow, skill, or rule?

3. Definition

AI Waste Backlog is a managed backlog of items that represent low-value, repeated, redundant, unsustainable, or systemically inappropriate consumption of AI capacity.

Each AI Waste Backlog item describes:

what AI capacity was consumed,
why it is considered waste,
how often it repeats,
what it costs,
how it affects review and attention,
whether it creates dependency,
what pattern it reveals,
what solution may be appropriate,
who owns it,
and what next step should follow.

The AI Waste Backlog is part of the AIFC learning layer.

4. Why AI Waste Backlog matters

Without an AI Waste Backlog, AI waste disappears into everyday operations.

People may feel:

“AI is helping us.”
“AI created something.”
“AI handled it quickly.”
“AI saved us work.”

On closer inspection, the truth may be more complex:

AI created many proposals that nobody read.
AI generated texts that were fully rewritten.
AI repeatedly fixed the same structural issue.
AI consumed more review time than it saved.
AI substituted for a missing template.
AI hid the fact that the team lacks a clear rule.
AI created dependency on a prompt instead of a stable workflow.

The AI Waste Backlog helps prevent these signals from being lost.

Minimum requirement

Significant and repeated AI waste signals must be recorded and assessed.

5. AI waste vs AI failure

AI waste is not the same as AI failure.

AI failure

AI failure is a situation where an AI output failed.

For example:

it was factually wrong,
it violated a rule,
it used forbidden data,
it hallucinated,
or it caused an incident.

AI waste

AI waste is a situation where AI may have technically worked, but its use was not a good system solution.

For example:

AI repeatedly generates the same type of document,
AI replaces a template,
AI performs a check that should be handled by a validator,
AI summarizes outputs that nobody needs,
AI creates a proposal without an owner.

AI failure requires correction or incident response. AI waste requires system improvement.

Minimum requirement

An AIFC community must distinguish AI failure from AI waste.

6. Types of AI waste

AIFC distinguishes several types of AI waste.

6.1 Repetitive AI waste

AI repeatedly performs the same routine.

Example:

it keeps converting the same kind of note into the same format.

Possible solutions:

template,
workflow,
UI action,
validator,
automation.

6.2 Review waste

AI creates outputs that consume more review time than they save.

Possible solutions:

better scope,
better skill,
lower AI Autonomy,
smaller outputs,
triage,
quality gate.

6.3 Attention waste

AI generates many summaries, proposals, or alerts that overload people.

Possible solutions:

prioritization,
threshold,
batching,
Human Cockpit filtering,
signal aggregation.

6.4 Prompt waste

People repeatedly prompt AI manually for the same work.

Possible solutions:

AI skill,
prompt template,
workflow action,
agent role,
deterministic transformation.

6.5 Knowledge waste

AI repeatedly explains something that should be in the Source of Truth.

Possible solutions:

knowledge artefact,
FAQ,
human skill,
decision record,
onboarding page.

6.6 Structural waste

AI fixes errors caused by poor knowledge base structure.

Possible solutions:

metadata schema,
folder rules,
cleanup agent,
validation rule,
standard template.

6.7 Dependency waste

AI use creates or deepens dependency instead of building capability.

Possible solutions:

Human Capability Reserve,
AI-free practice,
human skill,
fallback,
training.

6.8 Model waste

An unnecessarily expensive or powerful model is used for simple work.

Possible solutions:

cheaper model,
validator,
rule,
script,
non-AI workflow.

6.9 Output waste

AI creates outputs that are not accepted, used, or written back.

Possible solutions:

better assignment,
owner requirement,
output triage,
stop workflow,
reassessment of need.

6.10 Governance waste

AI creates proposals or exceptions that overload governance.

Possible solutions:

better approval boundary,
batch review,
decision rules,
threshold,
delegation,
workflow conversion.

Minimum requirement

The AI Waste Backlog must support classification of waste by type.

7. Sources of AI waste items

The AI Waste Backlog may be populated from different sources.

AI Retrospective

The most important source.

The retrospective identifies where AI consumed capacity without sufficient value.

Cost review

Budget analysis reveals expensive or growing AI workflows.

Human review feedback

Reviewers report that AI outputs create too many corrections.

Human Cockpit Layer

The cockpit detects high volumes of ownerless proposals, high rejection rate, or a review backlog.

AI agent self-observation

An AI agent may propose that it is repeating a routine that should be moved into a workflow.

Incident review

A waste item may arise after a budget incident, boundary incident, or lock-in incident.

Team feedback

Team members may report waste manually.

Minimum requirement

The AI Waste Backlog must have at least one formal input mechanism from AI Retrospective or governance review.

8. AI waste item lifecycle

AIFC recommends this lifecycle:

observed
-> classified
-> triaged
-> accepted
-> solution proposed
-> converted / reduced / rejected / accepted risk
-> verified
-> closed

Observed

The waste signal is recorded.

Classified

The waste type is identified.

Triaged

Priority, cost, impact, and owner are assessed.

Accepted

The community recognizes that this is a real waste item.

Solution proposed

A solution is proposed.

Converted / reduced / rejected / accepted risk

The item is resolved through one of the available paths.

Verified

The community verifies whether the waste actually decreased.

Closed

The item is closed.

Minimum requirement

AI Waste Backlog items must have a status and an owner or a clear triage mechanism.

9. Triage

Not every AI waste item must be resolved immediately.

Triage evaluates:

frequency,
cost,
review load,
attention load,
dependency risk,
impact on values,
impact on Operational DNA,
availability of a simple solution,
connection to an existing workflow,
workflow conversion potential.

Prioritization example:

High priority:
High cost, high frequency, creates dependency or touches critical workflow.

Medium priority:
Repeated waste with moderate cost or attention impact.

Low priority:
Rare or low-impact waste, monitor only.

Minimum requirement

The AI Waste Backlog must have prioritization or triage rules.

10. Solution categories

An AI waste item may be resolved in different ways.

10.1 Workflow conversion

A repeated AI routine is converted into a stable workflow.

10.2 Template

A template is created for a repeated output.

10.3 Validator

A repeated check is converted into a software validation rule.

10.4 Script or automation

A routine is converted into a deterministic script.

10.5 Human skill

A human skill is created or updated.

10.6 AI skill

An AI skill is created or updated with better scope.

10.7 Better Source of Truth

Missing knowledge is written into the knowledge base.

10.8 Operating mode change

AI Intensity or AI Autonomy is reduced or increased.

10.9 Budget guardrail

A limit, threshold, or alert is set.

10.10 Stop doing it

The community decides that the work does not have enough value and will not be done.

Minimum requirement

Every accepted AI waste item must have a proposed solution category or an approved decision to monitor it only.

11. AI waste and workflow conversion

The most important output of the AI Waste Backlog is often workflow conversion.

If AI repeatedly handles the same pattern, that is a signal that the pattern should be captured by the system.

Example:

AI repeatedly rewrites meeting notes into action items.

Possible conversion:
- standard meeting note template,
- action item extraction workflow,
- metadata schema,
- Human Cockpit approval UI,
- deterministic parser for simple cases,
- AI only for ambiguous cases.

Workflow conversion is described in detail in:

AIFC-042: Workflow Conversion

Minimum requirement

A repeated AI routine with significant consumption must be assessed for workflow conversion.

12. AI waste and skills

AI waste often reveals missing or poorly defined skills.

Examples:

AI repeatedly creates a poor UX proposal -> an AI skill with good rules is missing.
People cannot assign work to AI well -> a human skill for assignment is missing.
Reviewers keep correcting the same issue -> a checklist or example is missing.
An agent generates outputs that are too long -> an output style rule is missing.
AI does not distinguish draft, proposal, and decision -> a governance skill is missing.

Minimum requirement

An AI waste item must be assessed for whether it requires a human skill or AI skill update.

13. AI waste and Source of Truth

AI waste may arise because the Source of Truth is incomplete or unclear.

Example:

AI repeatedly explains how to approve a change proposal.
This means the approval workflow is not accessible or readable enough.

Possible solutions:

update the document,
add a summary,
create a checklist,
add a Human Cockpit card,
add examples,
create a Decision Record.

Minimum requirement

AI waste caused by missing knowledge must lead to a Source of Truth improvement proposal.

14. AI waste and cost control

The AI Waste Backlog is an input to AI Budget and Cost Control.

It helps answer:

Where does AI consume the most capacity?
Where is the value-to-cost ratio low?
Which agent is expensive and not very useful?
Which workflow has a high rejection rate?
Where should budget be reduced?
Where should a guardrail be created?

Minimum requirement

Significant AI waste items must be visible to the AI budget owner.

15. AI waste and attention debt

AI waste is not only about cost.

It can create attention debt.

Examples:

many AI proposals without priority,
long summaries without action,
alerts that fire too often,
duplicate signals,
large AI analyses without a decision,
AI comments in every document.

Attention debt is dangerous because it weakens people’s ability to see truly important signals.

Minimum requirement

The AI Waste Backlog must be able to mark attention impact.

16. AI waste and Human Capability Reserve

AI waste may weaken human capability.

Example:

AI keeps generating routine code.
People stop writing routine code.
When tokens run out, the work stops.

This is not only a budget problem.

It is capability waste.

AI consumption has created a less resilient system.

Minimum requirement

An AI waste item must be assessed for its impact on Human Capability Reserve when it concerns critical or repeated work.

17. AI waste and AI dependency

AI waste and AI dependency are often connected.

AI may be used unnecessarily, but once a team gets used to it, dependency emerges.

Example:

Every Jira ticket must be rewritten by AI.
The team loses the ability to write clear tickets.
AI becomes a mandatory intermediary for routine work.

Minimum requirement

Repeated AI waste must be checked as a possible AI dependency signal.

18. AI waste and governance overload

AI may generate too much governance work.

For example:

hundreds of change proposals,
many low-value risks,
too many skill update proposals,
too many review requests,
too many classification uncertainties.

AI should support governance, not overload it.

Minimum requirement

AI-generated governance workload must be monitored and limited through prioritization, batching, or thresholds.

19. AI waste and model selection

AI waste may be caused by using the wrong model.

Examples:

an expensive model performs simple classification,
a weak model creates many wrong outputs,
a model without domain context creates review waste,
a public model requires redaction that is more expensive than a private model.

Minimum requirement

An AI waste item must support marking a model or tool fit issue.

20. AI waste and agent design

AI waste may be caused by poor agent design.

Examples:

the agent has no stop condition,
the agent scope is too broad,
the agent generates long reports,
the agent retries without a limit,
the agent cannot prioritize,
the agent creates proposals without an owner,
the agent does not know when to stop.

Minimum requirement

AI waste caused by an agent must lead to a review of the agent role, scope, guardrails, or skill.

21. AI waste and stopping work

Some AI waste items should not be converted into workflows.

Sometimes the right answer is:

Stop doing this work.

Examples:

nobody reads the report,
nobody uses the summary,
nobody approves the proposals,
the analysis does not lead to a decision,
the output does not support the purpose.

AI can make unnecessary outputs cheaper to create.

That does not mean we should create them.

Minimum requirement

The AI Waste Backlog must support the solution type stop doing it.

22. AI waste item structure

Minimum requirement

An AI waste item must contain at least a description, type, impact, owner or triage owner, and next step.

23. Root cause analysis

An AI waste item should not be resolved only on the surface.

The root cause must be sought.

Example root causes:

missing template,
missing validator,
missing Source of Truth,
missing owner,
missing skill,
agent scope too broad,
unsuitable model,
unclear workflow,
people lack a human skill,
output has no customer,
governance is too slow,
AI is used as a bypass around the real problem.

Minimum requirement

A high-priority AI waste item must have a root cause hypothesis.

24. Verification

It is not enough to close an item after implementing a solution.

The community must verify whether the waste actually decreased.

Examples of verification:

AI consumption decreased,
review time decreased,
rejection rate decreased,
the number of duplicate proposals decreased,
a workflow was created,
a validator was created,
a skill was created,
the output is used,
people can perform the work without AI,
AI dependency decreased.

Minimum requirement

Closing a significant AI waste item must include a verification method.

25. Relationship with AI Retrospective

AI Retrospective is the main mechanism for identifying AI waste.

The AI Waste Backlog is where waste is managed after it has been identified.

Relationship:

AI Retrospective
-> detects waste

AI Waste Backlog
-> tracks and resolves waste

Workflow Conversion / Skill Evolution / Source of Truth update
-> turns waste into system capability

Minimum requirement

AI Retrospective must be able to create AI Waste Backlog items.

26. Relationship with Workflow Conversion

The AI Waste Backlog is an input to Workflow Conversion.

An AI waste item may become:

workflow conversion candidate,
template candidate,
validation rule candidate,
automation candidate,
skill evolution candidate,
stop-work candidate.

Workflow Conversion is defined in detail in AIFC-042.

Minimum requirement

The AI Waste Backlog must be connected to the Workflow Conversion mechanism.

27. Relationship with Human Cockpit Layer

The Human Cockpit Layer must make the AI Waste Backlog visible.

It may show:

top AI waste items,
waste by cost,
waste by attention impact,
waste by dependency risk,
waste by agent,
waste by workflow,
conversion candidates,
overdue waste items,
verified savings,
ownerless waste items.

Without visibility, AI waste becomes a hidden operational cost.

Minimum requirement

Responsible roles must have a human-accessible view of significant AI waste items.

28. Relationship with Feedback and Change Proposals

An AI waste item may be an observed signal.

If it has significant impact, it should be converted into a change proposal.

Example:

Observed signal:
AI repeatedly creates Jira ticket rewrites with low acceptance rate.

Change proposal:
Create standard Jira ticket template and AI skill update. Limit AI rewrite to unclear tickets only.

Minimum requirement

Significant AI waste items must be processable as observed signals or change proposals.

29. AI role in AI Waste Backlog

AI may help with the AI Waste Backlog.

It may:

detect repeated patterns,
analyze cost logs,
find high rejection rates,
propose waste classification,
propose solution categories,
prepare workflow conversion proposals,
summarize waste trends.

AI must not decide by itself that a specific human activity is waste when that has governance, values, or organizational impact.

Minimum requirement

AI-generated waste classification must be marked as a proposal and reviewed by an owner.

30. Suggested metadata

Example metadata for an AI waste item:

ai_waste_item:
  id:
  title:
  status: observed | classified | triaged | accepted | solution_proposed | in_progress | verified | closed | rejected | accepted_risk
  owner:
  triage_owner:
  source:
    - ai_retrospective
    - cost_review
    - human_review_feedback
    - cockpit_signal
    - incident_review
    - team_feedback
    - ai_agent_observation
  waste_type:
    - repetitive
    - review
    - attention
    - prompt
    - knowledge
    - structural
    - dependency
    - model
    - output
    - governance
  related_ai_workflow:
  related_ai_team_member:
  related_ai_engagement:
  related_model_or_tool:
  frequency:
  cost_impact: low | medium | high | critical
  review_impact: low | medium | high | critical
  attention_impact: low | medium | high | critical
  dependency_impact: low | medium | high | critical
  priority: low | medium | high | critical
  root_cause_hypothesis:
  proposed_solution_category:
    - workflow_conversion
    - template
    - validator
    - script
    - human_skill
    - ai_skill
    - source_of_truth_update
    - operating_mode_change
    - budget_guardrail
    - stop_doing_it
    - monitor_only
  related_change_proposal:
  verification_method:
  created_at:
  last_reviewed:

This structure is illustrative.

The final schema should be defined in the agent-actionable layer of the standard.

31. Anti-patterns

AIFC rejects the following anti-patterns.

31.1 AI waste ignored

The community sees low-value AI consumption but does not address it.

31.2 AI waste treated only as cost

Waste is addressed only by reducing budget, not by improving the system.

31.3 Repeated prompting normalized

People keep manually prompting the same routine instead of creating a workflow.

31.4 AI summaries nobody reads

AI creates summaries without a clear audience, owner, or decision value.

31.5 Review overload accepted

AI creates too many outputs and the review load is treated as normal.

31.6 AI used instead of Source of Truth

AI keeps explaining something that should have been written in the knowledge base.

31.7 AI waste without owner

Waste is named, but nobody is responsible for resolving it.

31.8 Agent loops without guardrails

An agent consumes capacity without a stop condition, output condition, or budget limit.

31.9 AI dependency hidden as efficiency

AI routinely replaces human work and human capability is lost, but the change is presented as savings.

31.10 No verification

The community changes something but does not verify whether waste actually decreased.

32. Minimal requirements

In the area of AI Waste Backlog, an AIFC community must at minimum:

Distinguish AI waste from AI failure.
Have a mechanism for recording repeated or significant AI waste.
Assign a waste type to AI Waste Backlog items.
Assign a status to AI Waste Backlog items.
Assign an owner or triage owner to AI Waste Backlog items.
Use prioritization or triage rules.
Include cost, review, attention, or dependency impact for significant AI waste items.
Assess repeated AI routines for workflow conversion.
Assess AI waste for skill evolution.
Ensure AI waste caused by missing knowledge leads to a Source of Truth improvement proposal.
Make significant AI waste items visible to the budget owner.
Consider attention impact in output-heavy AI workflows.
Assess AI waste for Human Capability Reserve impact when it concerns critical or repeated work.
Check repeated AI waste as a possible AI dependency signal.
Monitor AI-generated governance workload.
Ensure AI waste caused by an agent leads to a review of role, scope, guardrails, or skill.
Support the solution type stop doing it.
Provide a root cause hypothesis for high-priority AI waste items.
Provide a verification method for closing significant AI waste items.
Connect the AI Waste Backlog to AI Retrospective, Workflow Conversion, and Change Proposals.
Make significant AI waste items visible through the Human Cockpit Layer or a governance interface.
Mark AI-generated waste classification as a proposal and review it by an owner.

33. Summary

The AI Waste Backlog helps the community avoid losing important signals in daily AI operations.

AI waste does not only mean that AI cost too much money.

It may mean that:

a workflow is missing,
a template is missing,
a validator is missing,
a human skill is missing,
an AI skill is missing,
the Source of Truth is missing,
an owner is missing,
a guardrail is missing,
or AI dependency is being created.

AIFC therefore says:

Do not let repeated AI work remain repeated AI work.
Turn it into system capability.

The AI Waste Backlog does not make AI less important.

It makes AI use more meaningful.

AI Waste Backlog turns wasted AI capacity into system improvement.