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All projects·From: AI-Native Marketing Tools
MiniAI Critique· 20 minutes

The Rip-Out Test: Auditing Your Team's Tool Stack for Bolted-On vs AI-Native

Sula Vineyards

Objective: Given a list of 8 tools a marketing team currently pays for, apply the lesson's 'rip out the model' test to classify each as AI-native or bolted-on, then flag which bolted-on tools are wasted AI spend.

You're the marketing ops lead at Sula Vineyards auditing next year's software budget. Finance wants to know which of the 8 tools on the AI line item are actually AI-dependent.

Apply the rip-out test to 8 real tools, classify native vs bolted-on, and recommend which bolted-on AI features to stop paying extra for.

Before you start

What you'll need

Free path (everything below is enough to finish)

Google Sheets
FreeRun the audit table

Free, no account needed beyond what the team already has

Perplexity(optional)
FreemiumResearch each tool's actual AI dependency if not obvious from the product page

Free tier gives enough cited searches to verify a handful of tools

The process

1 step

Step 01 of 01

Applying the rip-out test to classify a tool as AI-native or bolted-on

The lesson's test: rip out the model. If the product still works without it, it's bolted on. If it dies, it's native.

Sula Vineyards pays for 8 tools with 'AI' in the pitch deck. Which ones would survive if the LLM was switched off tomorrow?

Google Sheets— List each tool in a column, apply the test, mark native or bolted-on.

Procedure

  1. List all 8 tools with their AI feature and its monthly cost
  2. For each, ask: does the core workflow still function without the model
  3. Mark native (dies without model) or bolted-on (survives without model)
  4. For bolted-on tools, check whether the AI feature has a separate line-item cost
Sample output
AI SPEND AUDIT (8 tools)

NATIVE (3): Perplexity Pro, Granola, Cursor - all die without the model
BOLTED-ON (5): CRM 'AI insights' add-on, email tool 'AI subject lines', social scheduler 'AI captions', analytics 'AI summary', CMS 'AI SEO tips'

Flag: 3 of the 5 bolted-on add-ons cost extra and show under one use a month in the usage logs.

Healthy

Budget concentrates on native tools where the AI is the product; bolted-on add-ons are dropped unless usage data justifies the extra cost.

Unhealthy

Paying premium tiers for 'AI features' bolted onto tools the team would keep anyway, with no usage to show for it.

What this means

The rip-out test turns a vague 'we use AI tools' line item into a real budget decision.

So what do I do about it?

SymptomActionEffort
AI software spend keeps growing with no clear productivity signalRun the rip-out test on every AI line item before the next renewal30 min
YouYou can do this yourself, no engineering access required.

Final deliverable

An 8-tool audit table classifying each as native or bolted-on, with a recommendation on which bolted-on AI add-ons to cut.

See a reference example
Sample output
Go Digit Insurance, AI tool spend audit (excerpt)

NATIVE: ChatGPT Team, Claude, Perplexity Pro
BOLTED-ON: policy-CRM 'AI risk score' (+$40/seat/month, used twice in Q2), support-desk 'AI ticket summary' (+$15/seat/month, used weekly)

Recommendation: cut the risk-score add-on, keep the ticket summary, usage justifies the cost.

Success criteria

You're done when you can:

  • Correctly classifies at least 6 of 8 tools using the rip-out test
  • Flags bolted-on tools with a separate cost and low usage as cut candidates