// FREE CHECKLIST

The AI Opportunity Checklist.

The 4-question framework that finds where AI saves your team the most time. Run this before you buy a single tool.

Map your workflows end to end in 30 minutes
Score each use case on 4 dimensions
Prioritize by impact, not hype
The exact process behind a $300K cost saving
// THE 4 QUESTIONS
Q1 → How often does the team do this task?
Q2 → How many minutes does it take each time?
Q3 → How much does consistency matter?
Q4 → How much does the team dread it?

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What's Inside 3 steps
// Step 1

Workflow Mapping

List every repetitive task your team does. The complete audit takes 30 minutes.

Map
// Step 2

Scoring Matrix

Rate each task across frequency, time saved, quality impact, and team frustration.

Score
// Step 3

Priority Stack

Rank by total score. Start with the highest-impact, lowest-risk opportunity.

Prioritize

Find your highest-impact AI opportunity in 30 minutes.

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Free Framework

The AI Opportunity Checklist

Before you buy a single AI tool, run this checklist. Find the workflows that are bleeding money, score them, and know exactly where to start.

95%
of AI pilots fail (MIT)
$300K
saved with one audit
30 min
to complete this checklist
4
scoring dimensions

Why most AI projects fail

95% of AI projects fail because teams open ChatGPT before understanding their business problem. They skip straight to prompts, get generic outputs, blame the tool, and move on. Six months later, someone else asks the same question and the cycle repeats.

The pattern that works is the opposite. Audit first, tool second. Understand the workflow, score the opportunity, then pick the tool that fits. Not the other way around.

MIT Sloan Management Review

"95% of generative AI pilots fail to deliver measurable impact on the P&L."

What works

IKEA ran their AI audit before buying tools. They redeployed people, created new revenue streams, and built AI into workflows that already existed. The tool came last.

The common mistake

Buy the tool first. Assign it to a team. Expect cost savings. When the results are underwhelming, blame adoption. The problem was never the tool. It was skipping the audit.

Three steps to find your AI opportunity

Before you score anything, you need to understand what your team actually does every day. These three steps surface the workflows worth examining.

Step 1: Interview the team

Walk through workflows step by step with the people who do them every day. Map every input, every output, and every handoff. The details matter. Ask "what happens next?" until you run out of answers.

Step 2: Look at the people

Not just the process. Find who is resistant and find who is enthusiastic. Early adopters become your internal champions. Resistors reveal where training gaps are hiding. Both signals matter.

Step 3: Match AI to the gap

Where is the team spending time on work a machine handles better? Where are repetitive inputs creating repetitive outputs? The overlap between "high volume" and "low creativity" is where AI wins.

Score every use case on four dimensions

This is the core of the checklist. For every potential AI use case you identified, score it 1 to 5 on each of these four dimensions. The highest total scores are your best bets.

Frequency
How often is this task done?
Daily = 5   |   Weekly = 3   |   Monthly = 1
Time Saved
How much time would AI save per use?
>2 hours = 5   |   1 hour = 3   |   <30 min = 1
Quality Impact
How much better would outputs be?
Transformative = 5   |   Noticeable = 3   |   Marginal = 1
Frustration
How much does this task frustrate the team?
Major pain point = 5   |   Mild annoyance = 3   |   No complaints = 1
Use Case Frequency Time Saved Quality Frustration Total
1. /5 /5 /5 /5 /20
2. /5 /5 /5 /5 /20
3. /5 /5 /5 /5 /20
4. /5 /5 /5 /5 /20
5. /5 /5 /5 /5 /20
Your top priorities

Your top 3 use cases are the ones that score highest across all 4 dimensions. These are where AI will deliver the fastest, most measurable ROI. Start here. Ignore everything else until these are working.

Define each use case clearly

A scored use case without a clear definition is just a wish. For each of your top priorities, fill in these four fields. If you cannot answer them, the use case is not ready to build.

Use Case Name

What workflow is this? Be specific enough that anyone on the team would recognize it.

Input Type

What does the user provide? Raw data, a brief, a transcript, a spreadsheet? Name it.

Output Type

What does the AI deliver? A draft, a summary, a scored list? Define what "done" looks like.

Success Criteria

How do you know it worked? A number, a threshold, a comparison. If you cannot measure it, rethink it.

Industry examples

SaaS Sales: Personalized Outreach

Input
Prospect info (name, company, role, recent news) + your value proposition
Output
Customized cold email tailored to the prospect's current priorities
Success
>30% open rate on sent emails

Healthcare: Patient Education Materials

Input
Medical condition + treatment plan details
Output
Patient-facing explainer written at an 8th-grade reading level
Success
Patients ask fewer follow-up questions about care instructions

What to avoid

These four mistakes turn promising AI use cases into wasted budget. If your use case description looks like the grey box, rewrite it until it looks like the green one.

Mistake 1

Too vague

"Use AI for writing"
"Transform campaign briefs into 10 creative concepts in 5 minutes"
Mistake 2

No clear inputs

"Create social media content"
"Turn a product launch brief + 3 key messages into 5 LinkedIn posts with hooks"
Mistake 3

Unmeasurable success

"Good quality output"
"<10% edit rate before publishing"
Mistake 4

Building for edge cases

"Automate the annual compliance report"
"Automate the daily 2-hour client summary report"

What to do with your results

Take your top 3 scored use cases and build them. Not all at once. Start with the one that scored highest and prove it works before moving to the next.

The framework you just completed is the same one behind the $300K Custom GPT Framework. The checklist finds the opportunity. The framework builds the solution.

Case study

When Ekwy ran this audit at The Economist, the answer was clear. A custom GPT trained on their own copy frameworks. Nine months later: $300K agency contract dropped. The checklist found the opportunity. The build delivered the result.

You just found your AI opportunity. Now build it.

In the Claude Cowork workshop, you build something real for your actual workflow. Live, hands on, no theory slides.

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